# kb

id: rheoscope-20260505-0
file: data/en.wikipedia.org/wiki/Rheoscope-0.md
category: reference
title: Rheoscope
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Rheoscope
date_saved: 2026-05-05T06:25:53.014293+00:00
instance: kb-cron
parent: rheoscope-20260505-0
summary:
In fluid mechanics (specifically rheology), a rheoscope is an instrument for detecting or measuring the viscosity of a fluid. In the study of blood flow, a rheoscope is used to observe and measure the deformation of blood cells subject to different levels of fluid shear stress. == References == Stein, Jess, ed. (1980). The Random House college dictionary. Random House. p. 1132. ISBN 0-394-43600-8.


id: scale-of-chords-20260505-0
file: data/en.wikipedia.org/wiki/Scale_of_chords-0.md
category: reference
title: Scale of chords
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scale_of_chords
date_saved: 2026-05-05T06:25:54.290747+00:00
instance: kb-cron
parent: scale-of-chords-20260505-0
summary:
A scale of chords may be used to set or read an angle in the absence of a protractor. To draw an angle, compasses describe an arc from origin with a radius taken from the 60 mark. The required angle is copied from the scale by the compasses, and an arc of this radius drawn from the sixty mark so it intersects the first arc. The line drawn from this point to the origin will be at the target angle. == Mathematics == A chord is a line drawn between two points on the circumference of a circle. Look at the centre point of this line. For a circle of radius r, each half will be r sin ⁡ θ 2 {\displaystyle r\sin {\tfrac {\theta }{2}}} so the chord will be 2 r sin ⁡ θ 2 {\displaystyle 2r\sin {\tfrac {\theta }{2}}} . The line of chords scale represents each of these values linearly on a scale running from 0 to 60. == Availability == It appears on Gunter's scale and the Foster Serle dialing scales. The commercial company Stanley marketed a metal version (Stanley 60R Line of Chords Rule) in 2015. == See also == Ptolemy's table of chords


id: scanning-habitable-environments-with-raman-and-luminescence-for-organics-and-chemicals-20260505-0
category: reference
title: Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals
tags: science, encyclopedia
date_saved: 2026-05-05T06:25:55.517038+00:00
instance: kb-cron
parent: scanning-habitable-environments-with-raman-and-luminescence-for-organics-and-chemicals-20260505-0
file:
data/en.wikipedia.org/wiki/Scanning_Habitable_Environments_with_Raman_and_Luminescence_for_Organics_and_Chemicals-0.md

summary:
Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals (SHERLOC) is an ultraviolet Raman spectrometer that uses fine-scale imaging and an ultraviolet (UV) laser to determine fine-scale mineralogy, and detect organic compounds designed for the Perseverance rover as part of the Mars 2020 mission. It was constructed at the Jet Propulsion Laboratory with major subsystems being delivered from Malin Space Science Systems and Los Alamos National Laboratory. SHERLOC has a calibration target with possible Mars suit materials, and it will measure how they change over time in the Martian surface environment. == Goals == According to a 2017 Universities Space Research Association (USRA) report: The goals of the SHERLOC investigation are to: Assess the habitability potential of a sample and its aqueous history. Assess the availability of key elements and energy source for life (C, H, N, O, P, S etc.). Determine if there are potential biosignatures preserved in Martian rocks and outcrops. Provide organic and mineral analysis for selective sample caching. To do this SHERLOC does the following: Detects and classifies organics and astrobiologically relevant minerals on the surface and near subsurface of Mars. Bulk organic sensitivity of 10-5 to 10-6 w/w over a 7 x 7

source_url:
https://en.wikipedia.org/wiki/Scanning_Habitable_Environments_with_Raman_and_Luminescence_for_Organics_and_Chemicals


id: scintillating-bolometer-20260505-0
file: data/en.wikipedia.org/wiki/Scintillating_bolometer-0.md
category: reference
title: Scintillating bolometer
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scintillating_bolometer
date_saved: 2026-05-05T06:25:56.752639+00:00
instance: kb-cron
parent: scintillating-bolometer-20260505-0
summary:
A scintillating bolometer (or luminescent bolometer) is a scientific instrument using particle physics in the search for events with low energy deposition. These events could include dark matter, low energy solar neutrinos, double beta decay or rare radioactive decay. It works by simultaneously measuring both the light pulse and heat pulse generated by a particle interaction within its internal scintillator crystal. The device was originally proposed by L. Gonzalez-Mestres and D. Perret-Gallix (LAPP, IN2P3/CNRS) In their rapporteur contribution to the Proceedings of the XXIV International Conference on High-Energy Physics, Munich, August 1988, Gonzalez-Mestres and Perret-Gallix wrote : Perhaps bolometry should in some cases be combined with other detection techniques (luminescence?) in order to produce a primary fast signal as timing strobe. If light is used as a complementary signature, particle identification can be achieved through the heat-light ratio, where nucleus recoil is expected to be less luminescent than ionizing particles. The success of such a development would open the way to unprecedented achievements in background rejection for rare event experiments. Further explanations, including a description of the detector and possible applications incorporating in particular BGO and tungstates, were given by these authors in other papers such as their contribution to


id: sensitive-high-resolution-ion-microprobe-20260505-0
file: data/en.wikipedia.org/wiki/Sensitive_high-resolution_ion_microprobe-0.md
category: reference
title: Sensitive high-resolution ion microprobe
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Sensitive_high-resolution_ion_microprobe
date_saved: 2026-05-05T06:25:57.946330+00:00
instance: kb-cron
parent: sensitive-high-resolution-ion-microprobe-20260505-0
summary:
The sensitive high-resolution ion microprobe (also sensitive high mass-resolution ion microprobe or SHRIMP) is a large-diameter, double-focusing secondary ion mass spectrometer (SIMS) sector instrument that was produced by Australian Scientific Instruments in Canberra, Australia and now has been taken over by Chinese company Dunyi Technology Development Co. (DTDC) in Beijing. Similar to the IMS 1270-1280-1300 large-geometry ion microprobes produced by CAMECA, Gennevilliers, France and like other SIMS instruments, the SHRIMP microprobe bombards a sample under vacuum with a beam of primary ions that sputters secondary ions that are focused, filtered, and measured according to their energy and mass. The SHRIMP is primarily used for geological and geochemical applications. It can measure the isotopic and elemental abundances in minerals at a 10 to 30 μm-diameter scale and with a depth resolution of 1–5 μm. Thus, SIMS method is well-suited for the analysis of complex minerals, as often found in metamorphic terrains, some igneous rocks, and for relatively rapid analysis of statistical valid sets of detrital minerals from sedimentary rocks. The most common application of the instrument is in uranium–thorium–lead geochronology, although the SHRIMP can be used to measure some other isotope ratio measurements (e.g., δ7Li or δ11B) and trace element abundances.


id: sira-notified-body-20260505-0
file: data/en.wikipedia.org/wiki/Sira_(notified_body)-0.md
category: reference
title: Sira (notified body)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Sira_(notified_body)
date_saved: 2026-05-05T06:25:59.283534+00:00
instance: kb-cron
parent: sira-notified-body-20260505-0
summary:
Sira is a UK-based notified body, specialising in ATEX, IECEX and North American product approvals. == Foundation == Sira began life as the British Scientific Instrument Research Association (BSIRA). It was founded in 1918 by a Committee of the Privy Council for the promotion of scientific and industrial research and supported by the DSIR. The first members of the association were representatives of the optical industry, but these were joined in the same year by the electrical scientific instrument, electromedical, and X-ray industries. Its first director of research was Sir Herbert Jackson (1863–1936). BSIRA's London headquarters were destroyed in the Second World War and, in 1947, the association moved to a site in South Hill, Chislehurst, a Grade II Listed former private house called 'Sitka' By the 1960s, the association had become better-known as 'Sira'. It evolved into a group of British engineering companies, based in south London, that designed test equipment and provided calibration services. == Fate == In 2006, Sira Test and Certification Ltd, Sira Defence and Security, and Sira Environmental were owned by Volveré plc. In July 2009, Volvere sold Sira Test and Certification Ltd, Sira Certification Service and Sira Environmental Ltd to CSA International. == Similar


id: spectronic-20-20260505-0
file: data/en.wikipedia.org/wiki/Spectronic_20-0.md
category: reference
title: Spectronic 20
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Spectronic_20
date_saved: 2026-05-05T06:26:00.641959+00:00
instance: kb-cron
parent: spectronic-20-20260505-0
summary:
The Spectronic 20 is a brand of single-beam spectrophotometer, designed to operate in the visible spectrum across a wavelength range of 340 nm to 950 nm, with a spectral bandpass of 20 nm. It is designed for quantitative absorption measurement at single wavelengths. Because it measures the transmittance or absorption of visible light through a solution, it is sometimes referred to as a colorimeter. The name of the instrument is a trademark of the manufacturer. Developed by Bausch & Lomb and launched in 1953, the Spectronic 20 was the first low-cost spectrophotometer. It rapidly became an industry standard due to its low cost, durability and ease of use, and has been referred to as an "iconic lab spectrophotometer". Approximately 600,000 units were sold over its nearly 60 year production run. It has been the most widely used spectrophotometer worldwide. Production was discontinued in 2011 when it was replaced by the Spectronic 200, but the Spectronic 20 is still in common use. It is sometimes referred to as the "Spec 20". == Design == The Bausch & Lomb Spectronic 20 colorimeter uses a diffraction grating monochromator combined with a system for the detection, amplification, and measurement of light wavelengths in the


id: spectronic-20-20260505-1
file: data/en.wikipedia.org/wiki/Spectronic_20-1.md
category: reference
title: Spectronic 20
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Spectronic_20
date_saved: 2026-05-05T06:26:00.641959+00:00
instance: kb-cron
parent: spectronic-20-20260505-0
summary:
== Use == Before testing a sample, the Spectronic 20 is calibrated using a blank solution, which is the pure solvent that is used in the experimental sample. It is typically water or an organic solvent. In this calibration, the transmittance is set at 100% using the calibration knob of the instrument (the amplifier control knob in the figure at right). The instrument can also optionally be calibrated with a stock solution of a sample at a concentration known to have an absorbance of 2 or else vendor supplied standards, using the light absorption knob in the diagram shown at right. After calibration, the user places a 1/2 inch test tube or cuvette containing the sample solution to be measured into the sample compartment. Calibration is repeated each time the wavelength is changed. It or a standard reference sample is generally used to periodically check for drift. To measure wavelengths above 650 nm, the bottom of the instrument is opened, and a red filter and a red-sensitive photocell is installed. The original design of the Spectronic 20 utilized an analog dial for readout of transmission from 100%T to 1%T (top scale), 0A - 2A (lower scale). Using the original instrument


id: xylotron-20260505-0
file: data/en.wikipedia.org/wiki/XyloTron-0.md
category: reference
title: XyloTron
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/XyloTron
date_saved: 2026-05-05T06:26:07.903462+00:00
instance: kb-cron
parent: xylotron-20260505-0
summary:
The XyloTron is an open-source, portable macroscopic wood identification system developed to support supply chain verification and the enforcement of laws against illegal logging. The device uses computer vision wood identification models to classify wood specimens based on their macroscopic anatomy and requires minimal operator training. Designed for use in laboratory and field settings, the system provides a non-destructive method to rapidly identify wood. The system's accuracy depends on the quality of the training data, the breadth and anatomical complexity of woods in the model, and the similarity of unknown samples to those included in the model. Use of the XyloTron is intended to help establish probable cause in cases of possible illegal logging, rather than for high-stakes legal enforcement or forensic confirmation. Microscopic analysis of wood specimens by a wood anatomist may still be required. == Overview == The XyloTron system was developed at the United States Department of Agriculture, Forest Service, Forest Products Laboratory in Madison, Wisconsin. Initial conceptual work began in late 2010. The XyloTron uses a laptop or desktop computer for computation and the XyloScope, which is specialized, custom-designed hardware that captures controlled imaging using a scientific-grade digital camera, lens, and lighting array. Unlike traditional wood


id: z-tube-20260505-0
file: data/en.wikipedia.org/wiki/Z-tube-0.md
category: reference
title: Z-tube
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Z-tube
date_saved: 2026-05-05T06:26:09.094693+00:00
instance: kb-cron
parent: z-tube-20260505-0
summary:
The Z-tube is an experimental apparatus for measuring the tensile strength of a liquid. It consists of a Z-shaped tube with open ends, filled with a liquid, and set on top of a spinning table. If the tube were straight, the liquid would immediately fly out one end or the other of the tube as it began to spin. By bending the ends of the tube back towards the center of rotation, a shift of the liquid away from center will result in the water level in one end of the tube rising and thus increasing the pressure in that end of the tube, and consequently returning the liquid to the center of the tube. By measuring the rotational speed and the distance from the center of rotation to the liquid level in the bent ends of the tube, the pressure reduction inside the tube can be calculated. Negative pressures, (i.e. less than zero absolute pressure, or in other words, tension) have been reported using water processed to remove dissolved gases. Tensile strengths up to 280 atmospheres have been reported for water in glass. == References ==


id: zeeman-slower-20260505-0
file: data/en.wikipedia.org/wiki/Zeeman_slower-0.md
category: reference
title: Zeeman slower
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Zeeman_slower
date_saved: 2026-05-05T06:26:10.320396+00:00
instance: kb-cron
parent: zeeman-slower-20260505-0
summary:
In atomic physics, a Zeeman slower is a scientific instrument that is commonly used in atomic physics to slow and cool a beam of hot atoms to speeds of several meters per second and temperatures below a kelvin. The gas-phase atoms used in atomic physics are often generated in an oven by heating a solid or liquid atomic sample to temperatures where the vapor pressure is high enough that a substantial number of atoms are in the gas phase. These atoms effuse out of a hole in the oven with average speeds on the order of hundreds of m/s and large velocity distributions (due to their high temperature). The Zeeman slower is attached close to where the hot atoms exit the oven and are used to slow them to less than 10 m/s (slowing) with a very small velocity spread (cooling). A Zeeman slower consists of a cylinder, through which an atomic beam travels, a pump laser that counterpropagates with respect to the beam's direction, and a magnetic field (commonly produced by a solenoid-like coil) that points along the cylinder's axis with a spatially varying magnitude. The pump laser, which is required to be near-resonant with atomic transition, Doppler-slows a


id: zeeman-slower-20260505-1
file: data/en.wikipedia.org/wiki/Zeeman_slower-1.md
category: reference
title: Zeeman slower
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Zeeman_slower
date_saved: 2026-05-05T06:26:10.320396+00:00
instance: kb-cron
parent: zeeman-slower-20260505-0
summary:
B ( z ) = ℏ k μ ′ v + ℏ δ μ ′ = ℏ k v i μ ′ 1 − 2 a v i 2 z + ℏ δ μ ′ , {\displaystyle B(z)={\frac {\hbar k}{\mu '}}v+{\frac {\hbar \delta }{\mu '}}={\frac {\hbar kv_{i}}{\mu '}}{\sqrt {1-{\frac {2a}{v_{i}^{2}}}z}}+{\frac {\hbar \delta }{\mu '}},} where v i {\displaystyle v_{i}} is the maximal velocity class that will be slowed; all the atoms in the velocity distribution that have velocities v < v i {\displaystyle v<v_{i}} will be slowed, and those with velocities v > v i {\displaystyle v>v_{i}} will not be slowed at all. The parameter η {\displaystyle \eta } (which determines the required laser intensity) is normally chosen to be around 0.5. If a Zeeman slower were to be operated with η ≈ 1 {\displaystyle \eta \approx 1} , then after absorbing a photon and moving to the excited state, the atom would preferentially re-emit a photon in the direction of the laser beam (due to stimulated emission), which would counteract the slowing process. == Realization == The required form of the spatially inhomogeneous magnetic field as we showed above has the form B ( z ) = B 0


id: c-glenn-begley-20260505-0
file: data/en.wikipedia.org/wiki/C._Glenn_Begley-0.md
category: reference
title: C. Glenn Begley
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/C._Glenn_Begley
date_saved: 2026-05-05T06:26:14.501992+00:00
instance: kb-cron
parent: c-glenn-begley-20260505-0
summary:
C. Glenn Begley is a hematologist and oncologist who was the CEO of BioCurate, an Australia-based joint venture between the University of Melbourne and Monash University that was launched in 2016. The "C. Glenn Begley Award", created by Stanford University in 2025, was named in his honor. The award celebrates teams that uphold the highest standards of scientific integrity, fostering innovation that is both impactful and reliable. Previously, he worked at the California-based biotech company Akriveia Therapeutics as their chief scientific officer. His other previous positions include global head of hematology and oncology research at Amgen, senior vice president and chief scientific officer at TetraLogic Pharmaceuticals, and executive director of the Western Australian Institute of Medical Research. He studied medicine at the University of Melbourne. He is known for a study he co-authored in 2012 in which he found that only 6 out of 53 (11%) of landmark preclinical studies of cancer drugs could be reproduced. He was elected to the American Society for Clinical Investigation in 2000. Begley was elected Fellow of the Australian Academy of Health and Medical Sciences in 2015. == Education == Begley studied medicine at the University of Melbourne starting in 1972 and finishing in


id: conflicts-of-interest-in-academic-publishing-20260505-0
file: data/en.wikipedia.org/wiki/Conflicts_of_interest_in_academic_publishing-0.md
category: reference
title: Conflicts of interest in academic publishing
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Conflicts_of_interest_in_academic_publishing
date_saved: 2026-05-05T06:26:18.246652+00:00
instance: kb-cron
parent: conflicts-of-interest-in-academic-publishing-20260505-0
summary:
Conflicts of interest (COIs) often arise in academic publishing. Such conflicts may cause wrongdoing and make it more likely. Ethical standards in academic publishing exist to avoid and deal with conflicts of interest, and the field continues to develop new standards. Standards vary between journals and are unevenly applied. According to the International Committee of Medical Journal Editors, "[a]uthors have a responsibility to evaluate the integrity, history, practices and reputation of the journals to which they submit manuscripts". Conflicts of interest increase the likelihood of biases arising; they can harm the quality of research and the public good (even if disclosed). Conflicts of interest can involve research sponsors, authors, journals, journal staff, publishers, and peer reviewers. == Avoidance, disclosure, and tracking == The avoidance of conflicts of interest and the changing of the structure of institutions to make them easier to avoid are frequently advocated for. Some institutional ethics policies ban academics from entering into specific types of COIs, for instance by prohibiting them from accepting gifts from companies connected with their work. Education in ethical COI management is also a tool for avoiding COI problems. Disclosure of COIs has been debated since the 1980s; there is a general consensus


id: conflicts-of-interest-in-academic-publishing-20260505-1
file: data/en.wikipedia.org/wiki/Conflicts_of_interest_in_academic_publishing-1.md
category: reference
title: Conflicts of interest in academic publishing
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Conflicts_of_interest_in_academic_publishing
date_saved: 2026-05-05T06:26:18.246652+00:00
instance: kb-cron
parent: conflicts-of-interest-in-academic-publishing-20260505-0
summary:
==== Owners and governing bodies ==== The owner of an academic journal has ultimate power over the hiring and firing of editorial staff; editors' interests in pleasing their employers conflict with some of their other editorial interests. Journals are also more likely to accept papers by authors who work for the journals' hosting institutions. Some journals are owned by publishers. When journals print reviews of books published by their own publishers, they rarely (as of 2013) add COI disclosures. The publishers' interest in maximizing profit will often conflict with academic interests or ethical standards. In the case of closed-access publications, publishers' desire for high subscription income may conflict with an editorial desire for broader access and readership. There have been multiple mass resignations of editorial boards over such conflicts, which are often followed by the editorial board founding a new, non-profit journal to compete with their former one. Some journals are owned by academic societies and professional organisations. Leading journals can be very profitable and there is often friction about revenue between the journal and the member society that owns it. Some academic societies and professional organisations are themselves funded by membership fees and/or donations. If the owners benefit financially


id: conflicts-of-interest-in-academic-publishing-20260505-2
file: data/en.wikipedia.org/wiki/Conflicts_of_interest_in_academic_publishing-2.md
category: reference
title: Conflicts of interest in academic publishing
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Conflicts_of_interest_in_academic_publishing
date_saved: 2026-05-05T06:26:18.246652+00:00
instance: kb-cron
parent: conflicts-of-interest-in-academic-publishing-20260505-0
summary:
=== COIs of journal staff === Personal conflicts of interest faced by journal staff are individual. If a person leaves the journal—unlike the COIs of journals as institutions—their personal COIs will go with them. As of 2015, COIs of journal staff are less commonly reported than those of authors. For instance, one 2009 World Association of Medical Editors (WAME) policy document states, "Some journals list editors' competing interests on their website but this is not a standard practice". The ICMJE, however, requires that the COIs of editors and journal staff be regularly declared and published. One 2017 Open Payments study of influential US medical journals found half of the editors received payments from industry; another study that used a different sample of editors reported two-thirds. As of 2002, systems for reporting wrongdoing by editors often do not exist. Many journals have policies limiting COIs staff can enter into; for instance, accepting gifts of travel, accommodation, or hospitality may be prohibited. As of 2016, such policies are rarely published. Most journals do not offer COI training; as of 2015, many journals report a desire for better guidance on COI policy. === COIs of peer reviewers === The ICJME recommendations require peer


id: conflicts-of-interest-in-academic-publishing-20260505-3
file: data/en.wikipedia.org/wiki/Conflicts_of_interest_in_academic_publishing-3.md
category: reference
title: Conflicts of interest in academic publishing
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Conflicts_of_interest_in_academic_publishing
date_saved: 2026-05-05T06:26:18.246652+00:00
instance: kb-cron
parent: conflicts-of-interest-in-academic-publishing-20260505-0
summary:
The ICMJE requires that "All those designated as authors should meet all four criteria for authorship, and all who meet the four criteria should be identified as authors. Those who do not meet all four criteria should be acknowledged." Academics who have had publication ethics training and those who are aware of the ICMJE authorship criteria are more stringent in their concepts of authorship and are more likely to consider breaches of authorship as misconduct, as are more junior researchers. Awareness is low; one study found only about half of researchers had read the ICJME criteria. === COIs of study sponsors === If a study requires outside funding, this can be a major source of conflicting interests; for instance in cases where the manufacturer of a drug is funding a study into its safety and efficacy or where the sponsor hopes to use the research to defend itself in litigation. Sponsors of a study may involve themselves in the design, execution, analysis, and write-up of a study. In extreme cases, they may carry out the research and ghostwrite the article with almost no involvement from the nominal author. Movie-style credits are advocated as a way to avoid this. There are


id: further-research-is-needed-20260505-0
file: data/en.wikipedia.org/wiki/Further_research_is_needed-0.md
category: reference
title: Further research is needed
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Further_research_is_needed
date_saved: 2026-05-05T06:26:20.853781+00:00
instance: kb-cron
parent: further-research-is-needed-20260505-0
summary:
"Further research is needed" (FRIN), "more research is needed" and other variants of similar phrases are commonly used in research papers. The cliché is so common that it has attracted research, regulation and cultural commentary. == Meaning == Some research journals have banned the phrase "more research is needed" on the grounds that it is redundant; it is almost always true and fits almost any article, and so can be taken as understood. A 2004 metareview by the Cochrane collaboration of their own systematic medical reviews found that 93% of the reviews studied made indiscriminate FRIN-like statements, reducing their ability to guide future research. The presence of FRIN had no correlation with the strength of the evidence against the medical intervention. Authors who thought a treatment was useless were just as likely to recommend researching it further. Indeed, authors may recommend "further research" when, given the existing evidence, further research would be extremely unlikely to be approved by an ethics committee. Studies finding that a treatment has no noticeable effects are sometimes greeted with statements that "more research is needed" by those convinced that the treatment is effective, but the effect has not yet been found. Since even the largest


id: steven-n-goodman-20260505-0
file: data/en.wikipedia.org/wiki/Steven_N._Goodman-0.md
category: reference
title: Steven N. Goodman
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Steven_N._Goodman
date_saved: 2026-05-05T06:26:32.123634+00:00
instance: kb-cron
parent: steven-n-goodman-20260505-0
summary:
Steven N. Goodman (born 1954) is an American epidemiologist and Professor of Epidemiology and Population Health and of Medicine at the Stanford School of Medicine, where he serves as Associate Dean of Clinical and Translational Research. He is co-founder and co-director of the Meta-Research Innovation Center at Stanford (METRICS), a center dedicated to studying and improving the reproducibility and efficiency of biomedical research, alongside John Ioannidis. He is also founder and director of the Stanford Program on Research Rigor and Reproducibility (SPORR). Goodman has made extensive contributions to the foundations of scientific and statistical inference within the biosciences. In 1999, he coined the term "p-value fallacy" in a pair of landmark papers arguing for the adoption of Bayesian methods in medical research. He is a sibling of journalist and Democracy Now! host Amy Goodman and journalist David Goodman. == Education and career == Goodman received his AB in Biochemistry and Applied Mathematics from Harvard College in 1976, his MD from New York University School of Medicine, and his MHS and PhD in Biostatistics from the Johns Hopkins Bloomberg School of Public Health, completing his doctoral dissertation Evidence and Clinical Trials in 1989 under the supervision of Richard Royall. He completed


id: icmje-recommendations-20260505-0
file: data/en.wikipedia.org/wiki/ICMJE_recommendations-0.md
category: reference
title: ICMJE recommendations
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/ICMJE_recommendations
date_saved: 2026-05-05T06:26:34.946079+00:00
instance: kb-cron
parent: icmje-recommendations-20260505-0
summary:
The ICMJE recommendations (full title, "Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals") are a set of guidelines produced by the International Committee of Medical Journal Editors for standardising the ethics, preparation and formatting of manuscripts submitted to biomedical journals for publication. Compliance with the ICMJE recommendations is required by most leading biomedical journals. Levels of real compliance are subject to debate. As of 9 January 2020, 5570 journals worldwide claim to follow the ICMJE recommendations. The recommendations were first issued in 1979 under the title "Uniform Requirements for Manuscripts Submitted to Biomedical Journals" (abbreviated URMs and often shortened to "Uniform Requirements"). After a series of revisions, they were given their current name in 2013. == International Committee of Medical Journal Editors == The International Committee of Medical Journal Editors (ICMJE) was originally known as the Vancouver Group, after the location of their first meeting in Vancouver, British Columbia in Canada. As of 2017 members of the ICMJE are: Annals of Internal Medicine BMJ Bulletin of the World Health Organization Deutsches Ärzteblatt Ethiopian Journal of Health Sciences Iranian Journal of Medical Sciences Journal of the American Medical Association (JAMA) New England Journal of Medicine


id: john-ioannidis-20260505-0
file: data/en.wikipedia.org/wiki/John_Ioannidis-0.md
category: reference
title: John Ioannidis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/John_Ioannidis
date_saved: 2026-05-05T06:26:36.236603+00:00
instance: kb-cron
parent: john-ioannidis-20260505-0
summary:
John P. A. Ioannidis ( EE-ə-NEE-diss; Greek: Ιωάννης Ιωαννίδης, pronounced [i.oˈanis i.oaˈniðis]; born 21 August 1965) is a Greek-American physician-scientist, writer, and Stanford University professor who has made contributions to evidence-based medicine, epidemiology, and clinical research. Ioannidis studies scientific research itself (meta-research) primarily in clinical medicine and the social sciences. He has served on the editorial board of over twenty scientific journals including Journal of the American Medical Association (JAMA), Journal of the National Cancer Institute (JNCI), and The Lancet. Ioannidis's 2005 essay "Why Most Published Research Findings Are False" was the most-accessed article in the history of Public Library of Science (PLOS) as of 2020, with more than three million views. Ioannidis was a prominent opponent of lockdowns during the COVID-19 pandemic, and he has been accused of promoting conspiracy theories about COVID-19 policies and public health and safety measures. == Early life and education == Born in New York City in 1965, Ioannidis was raised in Athens, Greece. He was valedictorian of his class at Athens College, graduating in 1984, and won a number of awards, including the National Award of the Greek Mathematical Society. He graduated in the top rank of his class at the University of


id: john-ioannidis-20260505-1
file: data/en.wikipedia.org/wiki/John_Ioannidis-1.md
category: reference
title: John Ioannidis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/John_Ioannidis
date_saved: 2026-05-05T06:26:36.236603+00:00
instance: kb-cron
parent: john-ioannidis-20260505-0
summary:
=== Meta-analysis === Ioannidis has developed and popularized several methods for meta-analysis and has made several conceptual advances in this field. These include methods for assessing heterogeneity and its uncertainty, methods for meta-analysis involving multiple treatments, methods and processes for umbrella reviews, and several approaches to identifying bias and adjusting the results of meta-analyses for bias, such as publication bias and reporting bias resulting in funnel-plot asymmetry. He has also alerted about the misuse and misinterpretation of bias tests. Along with David Chavalarias, he catalogued 235 biases across the entire publication record of biomedical research. Ioannidis has been critical of flawed, misleading and redundant meta-analyses, estimating that few meta-analyses in medicine are both bias-free and clinically useful. He has performed empirical evaluations of the concordance of results between meta-analyses and large trials and between randomized trials and non-randomized studies. === Evidence-based medicine === Ioannidis has been one of the strong proponents and earlier advocates of evidence-based medicine. However, he has alerted that, over the years, as evidence-based medicine acquired more prominence and influence, it was hijacked to serve other agendas that are often biased. In an essay written to honor his late mentor David Sackett, he stated that: Influential randomized


id: john-ioannidis-20260505-2
file: data/en.wikipedia.org/wiki/John_Ioannidis-2.md
category: reference
title: John Ioannidis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/John_Ioannidis
date_saved: 2026-05-05T06:26:36.236603+00:00
instance: kb-cron
parent: john-ioannidis-20260505-0
summary:
=== Neuroscience === Along with colleagues, Ioannidis has performed empirical evaluations and meta-research assessments of large numbers of scientific studies in neuroscience and have found that lack of power is a very common problem, leading to both false-negatives (the inability to discover true signals) and false-positives (finding spurious signals). === Economics === In empirical assessments of all meta-analyses that have been conducted on economics topics, Ioannidis and colleagues have found that most of the studies in these fields are small and under-powered. Using bias detection and correction methods, they have concluded that nearly 80% of the reported effects in the empirical economics literature is exaggerated; typically by a factor of two, and with one-third inflated by a factor of four or more. == Editorial appointments == Ioannidis has served on the editorial board of a number of scientific journals, including the European Journal of Clinical Investigation (editor-in-chief, 2010–2019), BMC Medicine, International Journal of Epidemiology, Journal of the American Medical Association, Journal of Clinical Epidemiology, Journal of Infectious Diseases, International Journal of Molecular Epidemiology and Genetics, International Journal of Epidemiology, Journal of Translational Medicine, Journal of Evaluation in Clinical Practice, Clinical Chemistry, Physiological Reviews, Royal Society Open Science, Research Integrity and


id: john-ioannidis-20260505-3
file: data/en.wikipedia.org/wiki/John_Ioannidis-3.md
category: reference
title: John Ioannidis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/John_Ioannidis
date_saved: 2026-05-05T06:26:36.236603+00:00
instance: kb-cron
parent: john-ioannidis-20260505-0
summary:
== COVID-19 == In an editorial on STAT published March 17, 2020, Ioannidis wondered whether the global response to the COVID-19 pandemic may be a "once-in-a-century evidence fiasco" and asked for obtaining more reliable data to deal with the pandemic. He made a rough estimation that the coronavirus could cause 10,000 U.S. deaths if it infected 1% of the U.S. population, but argued that more data was needed to determine how widely the virus would spread. The virus in fact eventually became widely disseminated, and would cause more than one million deaths in the U.S. Ioannidis expressed doubt that vaccines or treatments would be developed and tested in time to affect how the pandemic would unfold. Marc Lipsitch, Director of the Center for Communicable Disease Dynamics at the Harvard T.H. Chan School of Public Health, objected to Ioannidis's characterization of the global response in a reply that was published on STAT the next day after Ioannidis's. In March 2020, Ioannidis tried to organize a meeting at the White House where he and colleagues would caution President Donald Trump against "shutting down the country for [a] very long time and jeopardizing so many lives in doing this", according to a proposal


id: john-ioannidis-20260505-4
file: data/en.wikipedia.org/wiki/John_Ioannidis-4.md
category: reference
title: John Ioannidis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/John_Ioannidis
date_saved: 2026-05-05T06:26:36.236603+00:00
instance: kb-cron
parent: john-ioannidis-20260505-0
summary:
== Reception == In the 2000s and 2010s, during a period of regular publications from Ioannidis on the replication crisis in science, observers in the popular press commented that Ioannidis "may be one of the most influential scientists alive", and was "cementing his role as one of medicine's top mythbusters". In 2014, The Economist featured Ioannidis and Steven Goodman in an article on the Meta-Research Innovation Center at Stanford, and George Johnson of the New York Times wrote an article on the importance of reproducible research, profiling Ioannidis's two 2005 papers as playing a critical role in raising concern about the issue in the scientific community, as later expressed by the journal Nature. This acclaim continued into the late 2010s, with Wired mentionining Ioannidis as "arguably the replication crisis' chief inquisitor". His research on replicability reached multiple fields, including the specious statistics behind some drug subscriptions, and findings from Ioannidis that only a minority of widely cited health research studies carried out over the last decade could be replicated, with at least 1 in 6 actually being contradicted by later studies,. Elsevier featured his analogy of reproducibility in research to "taming a complex beast". He was also an early critic


id: journalology-20260505-0
file: data/en.wikipedia.org/wiki/Journalology-0.md
category: reference
title: Journalology
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Journalology
date_saved: 2026-05-05T06:26:37.581513+00:00
instance: kb-cron
parent: journalology-20260505-0
summary:
Journalology, also referred to as publication science, is the scholarly study of all aspects of the academic publishing process. The field seeks to improve the quality of scholarly research by implementing evidence-based practices in academic publishing. The term "journalology" was coined by Stephen Lock, the former editor-in-chief of the BMJ. The first Peer Review Congress, held in 1989 in Chicago, Illinois, is considered a pivotal moment in the founding of journalology as a distinct field. The field of journalology has exerted a significant influence in promoting the adoption of pre-registration in scientific research, particularly in the domain of clinical trials. Clinical trial registration is now expected in most countries. Journalology researchers also work to reform the peer review process. == History == The earliest scientific journals were founded in the seventeenth century. While most early journals used peer review, peer review did not become common practice in medical journals until after World War II. The scholarly publishing process (including peer review) did not arise by scientific means and still suffers from problems with reliability (consistency and dependability), such as a lack of uniform standards and validity (well-founded, efficacious). Attempts to reform the academic publishing practice began to gain traction in


id: publication-bias-20260505-0
file: data/en.wikipedia.org/wiki/Publication_bias-0.md
category: reference
title: Publication bias
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Publication_bias
date_saved: 2026-05-05T06:26:45.299878+00:00
instance: kb-cron
parent: publication-bias-20260505-0
summary:
In published academic research, publication bias occurs when the outcome of an experiment or research study biases the decision to publish or otherwise distribute it. Publishing only results that show a significant finding disturbs the balance of findings in favor of positive results. The study of publication bias is an important topic in metascience. Despite similar quality of execution and design, papers with statistically significant results are three times more likely to be published than those with null results. This unduly motivates researchers to manipulate their practices to ensure statistically significant results, such as by data dredging. Many factors contribute to publication bias. For instance, once a scientific finding is well established, it may become newsworthy to publish reliable papers that fail to reject the null hypothesis. Most commonly, investigators simply decline to submit results, leading to non-response bias. Investigators may also assume they made a mistake, find that the null result fails to support a known finding, lose interest in the topic, or anticipate that others will be uninterested in the null results. Attempts to find unpublished studies often prove difficult or are unsatisfactory. In an effort to combat this problem, some journals require that authors preregister their methods


id: publication-bias-20260505-1
file: data/en.wikipedia.org/wiki/Publication_bias-1.md
category: reference
title: Publication bias
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Publication_bias
date_saved: 2026-05-05T06:26:45.299878+00:00
instance: kb-cron
parent: publication-bias-20260505-0
summary:
Meta-analyses and systematic reviews can account for publication bias by including evidence from unpublished studies and the grey literature. The presence of publication bias can also be explored by constructing a funnel plot in which the estimate of the reported effect size is plotted against a measure of precision or sample size. The premise is that the scatter of points should reflect a funnel shape, indicating that the reporting of effect sizes is not related to their statistical significance. However, when small studies are predominately in one direction (usually the direction of larger effect sizes), asymmetry will ensue and this may be indicative of publication bias. Because an inevitable degree of subjectivity exists in the interpretation of funnel plots, several tests have been proposed for detecting funnel plot asymmetry. These are often based on linear regression including the popular Eggers regression test, and may adopt a multiplicative or additive dispersion parameter to adjust for the presence of between-study heterogeneity. Some approaches may even attempt to compensate for the (potential) presence of publication bias, which is particularly useful to explore the potential impact on meta-analysis results. In ecology and environmental biology, a study found that publication bias impacted the effect size,


id: rescience-c-20260505-0
file: data/en.wikipedia.org/wiki/ReScience_C-0.md
category: reference
title: ReScience C
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/ReScience_C
date_saved: 2026-05-05T06:26:50.350213+00:00
instance: kb-cron
parent: rescience-c-20260505-0
summary:
ReScience C is a scientific journal established in 2015 by Nicolas Rougier and Konrad Hinsen with the aim of publishing researchers' attempts to replicate computations made by other authors, using independently written, free and open-source software (FOSS), with an open process of peer review. The journal states that requiring the replication software to be free and open-source ensures the reproducibility of the original research. == Creation == The journal was established in 2015 by Nicolas Rougier and Konrad Hinsen in the context of the replication crisis of the early 2010s, in which concern about difficulty in replicating (different data or details of method) or reproducing (same data, same method) peer-reviewed, published research papers was widely discussed. The journal's scope is computational research, with the motivation that journals rarely require the provision of source code, and when source code is provided, it is rarely checked against the results claimed in the research article. == Policies and methods == The scope of the journal is mainly focussed on researchers' attempts to replicate computations made by other authors, using independently written, free and open-source software (FOSS). Articles are submitted using the "issues" feature of a git repository run by GitHub, together with other


id: research-question-20260505-0
file: data/en.wikipedia.org/wiki/Research_question-0.md
category: reference
title: Research question
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Research_question
date_saved: 2026-05-05T06:26:51.677329+00:00
instance: kb-cron
parent: research-question-20260505-0
summary:
A research question is "a question that a research project sets out to answer". Choosing a research question is an essential element of both quantitative and qualitative research. Investigation will require data collection and analysis, and the methodology for this will vary widely. Good research questions seek to improve knowledge on an important topic, and are usually narrow and specific. To form a research question, one must determine what type of study will be conducted such as a qualitative, quantitative, or mixed study. Additional factors, such as project funding, may not only affect the research question itself but also when and how it is formed during the research process. Literature suggests several variations on criteria selection for constructing a research question, such as the FINER or PICOT methods. == Definition == The answer to a research question will help address a research problem or question. Specifying a research question, "the central issue to be resolved by a formal dissertation, thesis, or research project," is typically one of the first steps an investigator takes when undertaking research. Considerations, such as project funding or methodological approaches may influence the research process, including when and how the research question is developed. Clearly and


id: research-question-20260505-1
file: data/en.wikipedia.org/wiki/Research_question-1.md
category: reference
title: Research question
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Research_question
date_saved: 2026-05-05T06:26:51.677329+00:00
instance: kb-cron
parent: research-question-20260505-0
summary:
In terms of priorities and related concepts, the proposed strategy of differential technological development suggests research to focus primarily on questions and tools that are thought to increase safety and mitigate issues rather than risky technologies which are instead best to delay. Concerning control strategies for gene drives, researchers have however cautioned that such may lead to a counterproductive false sense of security. Not all technological progress may be beneficial in general or in contemporary contexts (environments or systems) and various research may for example result in engineered pandemics. Many studies "ask uninteresting research questions, [and] make only marginal contributions". One study suggests that while research on climate change "is valuable, it does not tackle head-on the most urgent question: how to change society to mitigate climate change right now". In the ethical framework of effective altruism, research questions with the greatest potential benefits from investments (not necessarily of financial nature) are identified to maximize research benefits. 80,000 Hours has compiled a small list of "Research questions that could have a big social impact, organised by discipline". In public health research, "it is vital that research questions posed are important and that funded research meets a research need or a


id: research-question-20260505-2
file: data/en.wikipedia.org/wiki/Research_question-2.md
category: reference
title: Research question
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Research_question
date_saved: 2026-05-05T06:26:51.677329+00:00
instance: kb-cron
parent: research-question-20260505-0
summary:
Does _________ (name the theory) explain the relationship between _________ (independent variable) and _________ (dependent variable), controlling for the effects of _________ (control variable)? Alternatively, a script for a quantitative null hypothesis might be as follows: There is no significant difference between _________ (the control and experimental groups on the independent variable) on _________ (dependent variable). Quantitative studies also fall into two categories: Correlational studies: A correlational study is non-experimental, requiring the writer to research relationships without manipulating or randomly selecting the subjects of the research. The research question for a correlational study may look like this: What is the relationship between long-distance commuters and eating disorders? Experimental studies: An experimental study is experimental in that it requires the writer to manipulate and randomly select the subjects of the research. The research question for an experimental study may look like this: Does the consumption of fast food lead to eating disorders? === Mixed study === A mixed study integrates both qualitative and quantitative studies, so the writer's research must be directed at determining the why or how and the what, where, or when of the research topic. Therefore, the writer will need to craft a research question for each study


id: science-exchange-company-20260505-0
file: data/en.wikipedia.org/wiki/Science_Exchange_(company)-0.md
category: reference
title: Science Exchange (company)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Science_Exchange_(company)
date_saved: 2026-05-05T06:26:53.014723+00:00
instance: kb-cron
parent: science-exchange-company-20260505-0
summary:
Science Exchange is a cloud-based software company offering an R&D marketplace to buy and sell scientific services. The marketplace gives life sciences companies access to the outsourced research they need and the platform fully automates R&D outsourcing from source to pay. Commercial contract research organizations (CROs) and academic core facilities can sell their products and services directly through the marketplace. Science Exchange's enterprise clients include top pharma and emerging biotechnology companies, including Merck, Amgen, Gilead Sciences, Astellas Pharma, AbbVie, and Regeneron Pharmaceuticals. Science Exchange was founded in 2011 by Elizabeth Iorns, Ryan Abbott, and Dan Knox, taking part in the startup accelerator program Y Combinator in the summer of 2011. == History == In 2011, as an assistant professor at the University of Miami Miller School of Medicine, Iorns came up with the idea for Science Exchange after needing to conduct immunology experiments, but having difficulty finding potential collaborators or providers to work with. Iorns formed Science Exchange with Knox and Abbott, and the company applied for a place in the Y Combinator startup accelerator program. The company was accepted into the Summer 2011 batch of Y Combinator and launched the first version of its website in August 2011. In


id: scientometrics-20260505-0
file: data/en.wikipedia.org/wiki/Scientometrics-0.md
category: reference
title: Scientometrics
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientometrics
date_saved: 2026-05-05T06:26:56.949649+00:00
instance: kb-cron
parent: scientometrics-20260505-0
summary:
Scientometrics is a subfield of informetrics that studies science through mathematical and quantitative methods, analyzing both scientific output and the dynamics of research. Major research issues in scientometrics include the measurement of the impact of research papers and academic journals, the understanding of scientific citations, and the use of such measurements in policy and management contexts. In practice, there is a substantial overlap between scientometrics and other scientific fields, including information systems, information science, the science of science policy, the sociology of science, and metascience. Critics have argued that overreliance on scientometrics has created a system of perverse incentives, producing a publish or perish environment that leads to low-quality research. == Historical development == Modern scientometrics is mostly based on the work of Derek J. de Solla Price and Eugene Garfield. The latter created the Science Citation Index and founded the Institute for Scientific Information which is heavily used for scientometric analysis. A dedicated academic journal, Scientometrics, was established in 1978. The industrialization of science increased the number of publications and research outcomes and the rise of computers allowed effective analysis of this data. While the sociology of science focused on the behavior of scientists, scientometrics focused on the analysis


id: scientometrics-20260505-1
file: data/en.wikipedia.org/wiki/Scientometrics-1.md
category: reference
title: Scientometrics
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientometrics
date_saved: 2026-05-05T06:26:56.949649+00:00
instance: kb-cron
parent: scientometrics-20260505-0
summary:
An acknowledgment index (British acknowledgement index) is a method for indexing and analyzing acknowledgments in the scientific literature and, thus, quantifies the impact of acknowledgments. Typically, a scholarly article has a section in which the authors acknowledge entities such as funding, technical staff, colleagues, etc. that have contributed materials or knowledge or have influenced or inspired their work. Like a citation index, it measures influences on scientific work, but in a different sense; it measures institutional and economic influences as well as informal influences of individual people, ideas, and artifacts. Unlike the impact factor, it does not produce a single overall metric, but analyzes the components separately. However, the total number of acknowledgments to an acknowledged entity can be measured and so can the number of citations to the papers in which the acknowledgment appears. The ratio of this total number of citations to the total number of papers in which the acknowledge entity appears can be construed as the impact of that acknowledged entity. == Altmetrics == In scholarly and scientific publishing, altmetrics are nontraditional bibliometrics proposed as an alternative or complement to more traditional citation impact metrics, such as impact factor and h-index. The term altmetrics was proposed


id: statcheck-20260505-0
file: data/en.wikipedia.org/wiki/Statcheck-0.md
category: reference
title: Statcheck
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Statcheck
date_saved: 2026-05-05T06:26:58.149712+00:00
instance: kb-cron
parent: statcheck-20260505-0
summary:
Statcheck is an R package designed to detect statistical errors in peer-reviewed psychology articles by searching papers for statistical results, redoing the calculations described in each paper, and comparing the two values to see if they match. It takes advantage of the fact that psychological research papers tend to report their results in accordance with the guidelines published by the American Psychological Association (APA). This leads to several disadvantages: it can only detect results reported completely and in exact accordance with the APA's guidelines, and it cannot detect statistics that are only included in tables in the paper. Another limitation is that Statcheck cannot deal with statistical corrections to test statistics, like Greenhouse–Geisser or Bonferroni corrections, which actually make tests more conservative. Some journals have begun piloting Statcheck as part of their peer review process. Statcheck is free software published under the GNU GPL v3. == Validity == In 2017, Statcheck's developers published a preprint paper concluding that the program accurately identified statistical errors over 95% of the time. This validity study comprised more than 1,000 hand-checked tests among which 5.00% turned out to be inconsistent. The study found that Statcheck recognized 60% of all statistical tests. A reanalysis of


id: scientific-method-20260505-0
file: data/en.wikipedia.org/wiki/Scientific_method-0.md
category: reference
title: Scientific method
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_method
date_saved: 2026-05-05T06:27:02.337150+00:00
instance: kb-cron
parent: scientific-method-20260505-0
summary:
The scientific method is an empirical method for acquiring knowledge through careful observation, rigorous skepticism, hypothesis testing, and experimental validation. Developed from ancient and medieval practices, it acknowledges that cognitive assumptions can distort the interpretation of the observation. The scientific method has characterized science since at least the 17th century. Scientific inquiry includes creating a testable hypothesis through inductive reasoning, testing it through experiments and statistical analysis, and adjusting or discarding the hypothesis based on the results. Although procedures vary across fields, the underlying process is often similar. In more detail: the scientific method involves making conjectures (hypothetical explanations), predicting the logical consequences of hypothesis, then carrying out experiments or empirical observations based on those predictions. A hypothesis is a conjecture based on knowledge obtained while seeking answers to the question. Hypotheses can be very specific or broad but must be falsifiable, implying that it is possible to identify a possible outcome of an experiment or observation that conflicts with predictions deduced from the hypothesis; otherwise, the hypothesis cannot be meaningfully tested. While the scientific method is often presented as a fixed sequence of steps, it actually represents a set of general principles. Not all steps take place in every


id: scientific-method-20260505-1
file: data/en.wikipedia.org/wiki/Scientific_method-1.md
category: reference
title: Scientific method
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_method
date_saved: 2026-05-05T06:27:02.337150+00:00
instance: kb-cron
parent: scientific-method-20260505-0
summary:
The scientific method is the process by which science is carried out. As in other areas of inquiry, science (through the scientific method) can build on previous knowledge, and unify understanding of its studied topics over time. Historically, the development of the scientific method was critical to the Scientific Revolution. The overall process involves making conjectures (hypotheses), predicting their logical consequences, then carrying out experiments based on those predictions to determine whether the original conjecture was correct. However, there are difficulties in a formulaic statement of method. The scientific method represents general principles rather than a fixed sequence, not all steps occur in every inquiry, nor always in the same order. It requires intelligence, imagination, and creativity rather than rigid adherence to procedure. ==== Factors of scientific inquiry ==== There are different ways of outlining the basic method used for scientific inquiry. The scientific community and philosophers of science generally agree on the following classification of method components. These methodological elements and organization of procedures tend to be more characteristic of experimental sciences than social sciences. Nonetheless, the cycle of formulating hypotheses, testing and analyzing the results, and formulating new hypotheses, will resemble the cycle described below.The scientific method is


id: scientific-method-20260505-2
file: data/en.wikipedia.org/wiki/Scientific_method-2.md
category: reference
title: Scientific method
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_method
date_saved: 2026-05-05T06:27:02.337150+00:00
instance: kb-cron
parent: scientific-method-20260505-0
summary:
I am not accustomed to saying anything with certainty after only one or two observations. ==== Definition ==== The scientific definition of a term sometimes differs substantially from its natural language usage. For example, mass and weight overlap in meaning in common discourse, but have distinct meanings in mechanics. Scientific quantities are often characterized by their units of measure which can later be described in terms of conventional physical units when communicating the work. New theories are sometimes developed after realizing certain terms have not previously been sufficiently clearly defined. For example, Albert Einstein's first paper on relativity begins by defining simultaneity and the means for determining length. These ideas were skipped over by Isaac Newton with, "I do not define time, space, place and motion, as being well known to all." Einstein's paper then demonstrates that they (viz., absolute time and length independent of motion) were approximations. Francis Crick cautions us that when characterizing a subject, however, it can be premature to define something when it remains ill-understood. In Crick's study of consciousness, he actually found it easier to study awareness in the visual system, rather than to study free will, for example. His cautionary example was the gene;


id: scientific-method-20260505-3
file: data/en.wikipedia.org/wiki/Scientific_method-3.md
category: reference
title: Scientific method
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_method
date_saved: 2026-05-05T06:27:02.337150+00:00
instance: kb-cron
parent: scientific-method-20260505-0
summary:
Watson and Crick showed an initial (and incorrect) proposal for the structure of DNA to a team from King's College London – Rosalind Franklin, Maurice Wilkins, and Raymond Gosling. Franklin immediately spotted the flaws which concerned the water content. Later Watson saw Franklin's photo 51, a detailed X-ray diffraction image, which showed an X-shape and was able to confirm the structure was helical. Once predictions are made, they can be sought by experiments. If the test results contradict the predictions, the hypotheses which entailed them are called into question and become less tenable. Sometimes the experiments are conducted incorrectly or are not very well designed when compared to a crucial experiment. If the experimental results confirm the predictions, then the hypotheses are considered more likely to be correct, but might still be wrong and continue to be subject to further testing. The experimental control is a technique for dealing with observational error. This technique uses the contrast between multiple samples, or observations, or populations, under differing conditions, to see what varies or what remains the same. We vary the conditions for the acts of measurement, to help isolate what has changed. Mill's canons can then help us figure out what


id: scientific-method-20260505-4
file: data/en.wikipedia.org/wiki/Scientific_method-4.md
category: reference
title: Scientific method
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_method
date_saved: 2026-05-05T06:27:02.337150+00:00
instance: kb-cron
parent: scientific-method-20260505-0
summary:
Science is a social enterprise, and scientific work tends to be accepted by the scientific community when it has been confirmed. Crucially, experimental and theoretical results must be reproduced by others within the scientific community. Researchers have given their lives for this vision; Georg Wilhelm Richmann was killed by ball lightning (1753) when attempting to replicate the 1752 kite-flying experiment of Benjamin Franklin. If an experiment cannot be repeated to produce the same results, this implies that the original results might have been in error. As a result, it is common for a single experiment to be performed multiple times, especially when there are uncontrolled variables or other indications of experimental error. For significant or surprising results, other scientists may also attempt to replicate the results for themselves, especially if those results would be important to their own work. Replication has become a contentious issue in social and biomedical science where treatments are administered to groups of individuals. Typically an experimental group gets the treatment, such as a drug, and the control group gets a placebo. John Ioannidis in 2005 pointed out that the method being used has led to many findings that cannot be replicated. Peer review—anonymous expert evaluation


id: scientific-method-20260505-5
file: data/en.wikipedia.org/wiki/Scientific_method-5.md
category: reference
title: Scientific method
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_method
date_saved: 2026-05-05T06:27:02.337150+00:00
instance: kb-cron
parent: scientific-method-20260505-0
summary:
=== Empiricism, rationalism, and more pragmatic views === Scientific endeavour can be characterised as the pursuit of truths about the natural world or as the elimination of doubt about the same. The former is the direct construction of explanations from empirical data and logic, the latter the reduction of potential explanations. It was established above how the interpretation of empirical data is theory-laden, so neither approach is trivial. The ubiquitous element in the scientific method is empiricism, which holds that knowledge is created by a process involving observation; scientific theories generalize observations. This is in opposition to stringent forms of rationalism, which holds that knowledge is created by the human intellect; later clarified by Popper to be built on prior theory. The scientific method embodies the position that reason alone cannot solve a particular scientific problem; it unequivocally refutes claims that revelation, political or religious dogma, appeals to tradition, commonly held beliefs, common sense, or currently held theories pose the only possible means of demonstrating truth. In 1877, C. S. Peirce characterized inquiry in general not as the pursuit of truth per se but as the struggle to move from irritating, inhibitory doubts born of surprises, disagreements, and the like,


id: scientific-method-20260505-6
file: data/en.wikipedia.org/wiki/Scientific_method-6.md
category: reference
title: Scientific method
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_method
date_saved: 2026-05-05T06:27:02.337150+00:00
instance: kb-cron
parent: scientific-method-20260505-0
summary:
An example for how inductive and deductive reasoning works can be found in the history of gravitational theory. It took thousands of years of measurements, from the Chaldean, Indian, Persian, Greek, Arabic, and European astronomers, to fully record the motion of planet Earth. Kepler(and others) were then able to build their early theories by generalizing the collected data inductively, and Newton was able to unify prior theory and measurements into the consequences of his laws of motion in 1727. Another common example of inductive reasoning is the observation of a counterexample to current theory inducing the need for new ideas. Le Verrier in 1859 pointed out problems with the perihelion of Mercury that showed Newton's theory to be at least incomplete. The observed difference of Mercury's precession between Newtonian theory and observation was one of the things that occurred to Einstein as a possible early test of his theory of relativity. His relativistic calculations matched observation much more closely than Newtonian theory did. Though, today's Standard Model of physics suggests that we still do not know at least some of the concepts surrounding Einstein's theory, it holds to this day and is being built on deductively. A theory being assumed


id: scientific-method-20260505-7
file: data/en.wikipedia.org/wiki/Scientific_method-7.md
category: reference
title: Scientific method
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_method
date_saved: 2026-05-05T06:27:02.337150+00:00
instance: kb-cron
parent: scientific-method-20260505-0
summary:
=== Hypothetico-deductive method === The hypothetico-deductive model, or hypothesis-testing method, or "traditional" scientific method is, as the name implies, based on the formation of hypotheses and their testing via deductive reasoning. A hypothesis stating implications, often called predictions, that are falsifiable via experiment is of central importance here, as not the hypothesis but its implications are what is tested. Basically, scientists will look at the hypothetical consequences a (potential) theory holds and prove or disprove those instead of the theory itself. If an experimental test of those hypothetical consequences shows them to be false, it follows logically that the part of the theory that implied them was false also. If they show as true however, it does not prove the theory definitively. The logic of this testing is what affords this method of inquiry to be reasoned deductively. The formulated hypothesis is assumed to be 'true', and from that 'true' statement implications are inferred. If the following tests show the implications to be false, it follows that the hypothesis was false also. If test show the implications to be true, new insights will be gained. It is important to be aware that a positive test here will at best strongly


id: scientific-method-20260505-8
file: data/en.wikipedia.org/wiki/Scientific_method-8.md
category: reference
title: Scientific method
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_method
date_saved: 2026-05-05T06:27:02.337150+00:00
instance: kb-cron
parent: scientific-method-20260505-0
summary:
== Scientific inquiry == Scientific inquiry generally aims to obtain knowledge in the form of testable explanations that scientists can use to predict the results of future experiments. This allows scientists to gain a better understanding of the topic under study, and later to use that understanding to intervene in its causal mechanisms (such as to cure disease). The better an explanation is at making predictions, the more useful it frequently can be, and the more likely it will continue to explain a body of evidence better than its alternatives. The most successful explanations – those that explain and make accurate predictions in a wide range of circumstances – are often called scientific theories. Most experimental results do not produce large changes in human understanding; improvements in theoretical scientific understanding typically result from a gradual process of development over time, sometimes across different domains of science. Scientific models vary in the extent to which they have been experimentally tested and for how long, and in their acceptance in the scientific community. In general, explanations become accepted over time as evidence accumulates on a given topic, and the explanation in question proves more powerful than its alternatives at explaining the evidence.


id: scientific-method-20260505-9
file: data/en.wikipedia.org/wiki/Scientific_method-9.md
category: reference
title: Scientific method
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_method
date_saved: 2026-05-05T06:27:02.337150+00:00
instance: kb-cron
parent: scientific-method-20260505-0
summary:
We are to admit no more causes of natural things than such as are both true and sufficient to explain their appearances. The concept of parsimony should not be held to imply complete frugality in the pursuit of scientific truth. The general process starts at the opposite end of there being a vast number of potential explanations and general disorder. An example can be seen in Paul Krugman's process, who makes explicit to "dare to be silly". He writes that in his work on new theories of international trade he reviewed prior work with an open frame of mind and broadened his initial viewpoint even in unlikely directions. Once he had a sufficient body of ideas, he would try to simplify and thus find what worked among what did not. Specific to Krugman here was to "question the question". He recognised that prior work had applied erroneous models to already present evidence, commenting that "intelligent commentary was ignored". Thus touching on the need to bridge the common bias against other circles of thought. ==== Elegance ==== Occam's razor might fall under the heading of "simple elegance", but it is arguable that parsimony and elegance pull in different directions. Introducing additional


id: scientific-method-20260505-10
file: data/en.wikipedia.org/wiki/Scientific_method-10.md
category: reference
title: Scientific method
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_method
date_saved: 2026-05-05T06:27:02.337150+00:00
instance: kb-cron
parent: scientific-method-20260505-0
summary:
Scientific pluralism is a position within the philosophy of science that rejects various proposed unities of scientific method and subject matter. Scientific pluralists hold that science is not unified in one or more of the following ways: the metaphysics of its subject matter, the epistemology of scientific knowledge, or the research methods and models that should be used. Some pluralists believe that pluralism is necessary due to the nature of science. Others say that since scientific disciplines already vary in practice, there is no reason to believe this variation is wrong until a specific unification is empirically proven. Finally, some hold that pluralism should be allowed for normative reasons, even if unity were possible in theory. === Unificationism === Unificationism, in science, was a central tenet of logical positivism. Different logical positivists construed this doctrine in several different ways, e.g. as a reductionist thesis, that the objects investigated by the special sciences reduce to the objects of a common, putatively more basic domain of science, usually thought to be physics; as the thesis that all theories and results of the various sciences can or ought to be expressed in a common language or "universal slang"; or as the thesis that


id: scientific-method-20260505-11
file: data/en.wikipedia.org/wiki/Scientific_method-11.md
category: reference
title: Scientific method
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_method
date_saved: 2026-05-05T06:27:02.337150+00:00
instance: kb-cron
parent: scientific-method-20260505-0
summary:
==== Thought collectives ==== A perhaps accessible lead into what is claimed is Fleck's thought, echoed in Kuhn's concept of normal science. According to Fleck, scientists' work is based on a thought-style, that cannot be rationally reconstructed. It gets instilled through the experience of learning, and science is then advanced based on a tradition of shared assumptions held by what he called thought collectives. Fleck also claims this phenomenon to be largely invisible to members of the group. Comparably, following the field research in an academic scientific laboratory by Latour and Woolgar, Karin Knorr Cetina has conducted a comparative study of two scientific fields (namely high energy physics and molecular biology) to conclude that the epistemic practices and reasonings within both scientific communities are different enough to introduce the concept of "epistemic cultures", in contradiction with the idea that a so-called "scientific method" is unique and a unifying concept. ==== Situated cognition and relativism ==== On the idea of Fleck's thought collectives sociologists built the concept of situated cognition: that the perspective of the researcher fundamentally affects their work; and, too, more radical views. Norwood Russell Hanson, alongside Thomas Kuhn and Paul Feyerabend, extensively explored the theory-laden nature of observation


id: scientific-method-20260505-12
file: data/en.wikipedia.org/wiki/Scientific_method-12.md
category: reference
title: Scientific method
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_method
date_saved: 2026-05-05T06:27:02.337150+00:00
instance: kb-cron
parent: scientific-method-20260505-0
summary:
=== Science of complex systems === Science applied to complex systems can involve elements such as transdisciplinarity, systems theory, control theory, and scientific modelling. In general, the scientific method may be difficult to apply stringently to diverse, interconnected systems and large data sets. In particular, practices used within Big data, such as predictive analytics, may be considered to be at odds with the scientific method, as some of the data may have been stripped of the parameters which might be material in alternative hypotheses for an explanation; thus the stripped data would only serve to support the null hypothesis in the predictive analytics application. Fleck (1979), pp. 38–50 notes "a scientific discovery remains incomplete without considerations of the social practices that condition it". == Relationship with mathematics == Science is the process of gathering, comparing, and evaluating proposed models against observables. A model can be a simulation, mathematical or chemical formula, or set of proposed steps. Science is like mathematics in that researchers in both disciplines try to distinguish what is known from what is unknown at each stage of discovery. Models, in both science and mathematics, need to be internally consistent and also ought to be falsifiable (capable of


id: anomalistic-psychology-20260505-0
file: data/en.wikipedia.org/wiki/Anomalistic_psychology-0.md
category: reference
title: Anomalistic psychology
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Anomalistic_psychology
date_saved: 2026-05-05T06:27:08.849113+00:00
instance: kb-cron
parent: anomalistic-psychology-20260505-0
summary:
In psychology, anomalistic psychology is the study of human behaviour and experience connected with what is often called the paranormal, with few assumptions made about the validity of the reported phenomena. == Early history == According to anomalistic psychology, paranormal phenomena have naturalistic explanations resulting from psychological and physical factors which have given the false impression of paranormal activity to some people. There were many early publications that gave rational explanations for alleged paranormal experiences. The physician John Ferriar wrote An Essay Towards a Theory of Apparitions in 1813 in which he argued that sightings of ghosts were the result of optical illusions. Later, the French physician Alexandre Jacques François Brière de Boismont published On Hallucinations: Or, the Rational History of Apparitions, Dreams, Ecstasy, Magnetism, and Somnambulism in 1845 in which he claimed sightings of ghosts were the result of hallucinations. William Benjamin Carpenter, in his book Mesmerism, Spiritualism, Etc: Historically and Scientifically Considered (1877), wrote that Spiritualist practices could be explained by fraud, delusion, hypnotism and suggestion. The British psychiatrist Henry Maudsley, in Natural Causes and Supernatural Seemings (1886), wrote that so-called supernatural experiences could be explained in terms of disorders of the mind and were simply "malobservations and


id: anomalistic-psychology-20260505-1
file: data/en.wikipedia.org/wiki/Anomalistic_psychology-1.md
category: reference
title: Anomalistic psychology
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Anomalistic_psychology
date_saved: 2026-05-05T06:27:08.849113+00:00
instance: kb-cron
parent: anomalistic-psychology-20260505-0
summary:
=== Mediumship === Research and empirical evidence from psychology for over a hundred years has revealed that where there is not fraud, mediumship and Spiritualistic practices can be explained by psychological factors. Trance mediumship, which is claimed by the Spiritualists to be caused by discarnate spirits speaking through the medium, has been proven in some cases to be the emergence of alternate personalities from the medium's subconscious mind. The medium may obtain information about their clients, called sitters, by secretly eavesdropping on sitter's conversations or searching telephone directories, the internet and newspapers before the sittings. Mediums are known for employing a technique called cold reading which involves obtaining information from the sitter's behavior, clothing, posture, and jewellery. In a series of fake seance experiments (Wiseman et al. 2003), an actor suggested to paranormal believers and disbelievers that a table was levitating when, in fact, it remained stationary. After the seance, approximately one third of the participants incorrectly reported that the table had moved. The results showed a greater percentage of believers reporting that the table had moved. In another experiment the believers had also reported that a handbell had moved when it had remained stationary and expressed their belief that


id: anomalistic-psychology-20260505-2
file: data/en.wikipedia.org/wiki/Anomalistic_psychology-2.md
category: reference
title: Anomalistic psychology
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Anomalistic_psychology
date_saved: 2026-05-05T06:27:08.849113+00:00
instance: kb-cron
parent: anomalistic-psychology-20260505-0
summary:
The difference between anomalistic psychology and parapsychology is in terms of the aims of what each discipline is about. Parapsychologists typically are actually searching for evidence to prove the reality of paranormal forces, to prove they really do exist. So the starting assumption is that paranormal things do happen, whereas anomalistic psychologists tend to start from the position that paranormal forces probably don't exist and that therefore we should be looking for other kinds of explanations, in particular the psychological explanations for those experiences that people typically label as paranormal. Anomalistic psychology has been reported to be on the rise. It is now offered as an option on many psychology degree programmes and is also an option on the A2 psychology syllabus in the UK. == See also == Australian Sheep-Goat Scale Psychology of paranormal belief == References == == Further reading == Gustav Jahoda. (1974). The Psychology of Superstition. Jason Aronson, Inc. Publisher. ISBN 978-0876681855 David Marks. (2000). The Psychology of the Psychic. Prometheus Books. ISBN 978-1573927987 Andrew Neher. (2011). Paranormal and Transcendental Experience: A Psychological Examination. Dover Publications. ISBN 978-0486261676 John Schumaker. (1990). Wings of Illusion: The Origin, Nature and Future of Paranormal Belief. Prometheus Books. ISBN 978-0879756246


id: blind-taste-test-20260505-0
file: data/en.wikipedia.org/wiki/Blind_taste_test-0.md
category: reference
title: Blind taste test
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Blind_taste_test
date_saved: 2026-05-05T06:27:11.621519+00:00
instance: kb-cron
parent: blind-taste-test-20260505-0
summary:
In marketing, a blind taste test is often used as a tool for companies to compare their brand to another brand. For example, the Pepsi Challenge is a famous taste test that has been run by Pepsi since 1975. Additionally, taste tests are sometimes used as a tool by companies to develop their brand or new products. Blind taste tests are ideal for goods such as food or wine (see blind wine tasting) that are consumed directly. Researchers use blind taste tests to obtain information about customers' perceptions and preferences on the goods. Blind taste test can be used to: Track views on a product over time assess changes or improvements made to a product gauge reactions to a new product == Overview == Blind taste tests require a "blind testing" meaning the people taking the blind taste test are unaware of the identity of the brand being tested, or if done at home this can be as simple as a blindfold over the person taking the test. This means that any bias, preconceived ideas about a particular brand or food, is eliminated. The people taking the test will also be unaware of any changes done to the product. In


id: causality-20260505-0
file: data/en.wikipedia.org/wiki/Causality-0.md
category: reference
title: Causality
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Causality
date_saved: 2026-05-05T06:27:14.201565+00:00
instance: kb-cron
parent: causality-20260505-0
summary:
Causality is an influence by which one event, process, state, or subject (i.e., a cause) contributes to the production of another event, process, state, or object (i.e., an effect) where the cause is at least partly responsible for the effect, and the effect is at least partly dependent on the cause. The cause of something may also be described as the reason behind the event or process. In general, a process can have multiple causes, which are also said to be causal factors for it, and all lie in its past. An effect can in turn be a cause of, or causal factor for, many other effects, which all lie in its future. Thus, the distinction between cause and effect either follows from or else provides the distinction between past and future. While the former viewpoint is more prevalent in physics, some writers have held that causality is metaphysically prior to notions of time and space. Causality is an abstraction that indicates how the world progresses. As such, it is a basic concept, and one might expect it to be more apt as an explanation of other concepts of progression than as something to be explained by yet more fundamental


id: causality-20260505-1
file: data/en.wikipedia.org/wiki/Causality-1.md
category: reference
title: Causality
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Causality
date_saved: 2026-05-05T06:27:14.201565+00:00
instance: kb-cron
parent: causality-20260505-0
summary:
==== Geometrical significance ==== Causality has the properties of antecedence and contiguity. These are topological, and are ingredients for space-time geometry. As developed by Alfred Robb, these properties allow the derivation of the notions of time and space. Max Jammer writes "the Einstein postulate ... opens the way to a straightforward construction of the causal topology ... of Minkowski space." Causal efficacy propagates no faster than light. Thus, the notion of causality is metaphysically prior to the notions of time and space. In practical terms, this is because use of the relation of causality is necessary for the interpretation of empirical experiments. Interpretation of experiments is needed to establish the physical and geometrical notions of time and space. ==== Volition ==== The deterministic world-view holds that the history of the universe can be exhaustively represented as a progression of events following one after the other as cause and effect. Incompatibilism holds that determinism is incompatible with free will, so if determinism is true, "free will" does not exist. Compatibilism, on the other hand, holds that determinism is compatible with, or even necessary for, free will. === Necessary and sufficient causes === Causes may sometimes be distinguished into two types: necessary


id: causality-20260505-2
file: data/en.wikipedia.org/wiki/Causality-2.md
category: reference
title: Causality
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Causality
date_saved: 2026-05-05T06:27:14.201565+00:00
instance: kb-cron
parent: causality-20260505-0
summary:
If A were a triangle, then A would have three sides. If switch S were thrown, then bulb B would light. In the first case, it would be incorrect to say that A's being a triangle caused it to have three sides, since the relationship between triangularity and three-sidedness is that of definition. The property of having three sides actually determines A's state as a triangle. Nonetheless, even when interpreted counterfactually, the first statement is true. An early version of Aristotle's "four cause" theory is described as recognizing "essential cause". In this version of the theory, that the closed polygon has three sides is said to be the "essential cause" of its being a triangle. This use of the word 'cause' is of course now far obsolete. Nevertheless, it is within the scope of ordinary language to say that it is essential to a triangle that it has three sides. A full grasp of the concept of conditionals is important to understanding the literature on causality. In everyday language, loose conditional statements are often enough made, and need to be interpreted carefully. === Questionable cause === Fallacies of questionable cause, also known as causal fallacies, non-causa pro causa (Latin for


id: causality-20260505-3
file: data/en.wikipedia.org/wiki/Causality-3.md
category: reference
title: Causality
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Causality
date_saved: 2026-05-05T06:27:14.201565+00:00
instance: kb-cron
parent: causality-20260505-0
summary:
Interpreting causation as a deterministic relation means that if A causes B, then A must always be followed by B. In this sense, war does not cause deaths, nor does smoking cause cancer or emphysema. As a result, many turn to a notion of probabilistic causation. Informally, A ("The person is a smoker") probabilistically causes B ("The person has now or will have cancer at some time in the future"), if the information that A occurred increases the likelihood of B's occurrence. Formally, P{B|A}≥ P{B} where P{B|A} is the conditional probability that B will occur given the information that A occurred, and P{B} is the probability that B will occur having no knowledge whether A did or did not occur. This intuitive condition is not adequate as a definition for probabilistic causation because of its being too general and thus not meeting our intuitive notion of cause and effect. For example, if A denotes the event "The person is a smoker," B denotes the event "The person now has or will have cancer at some time in the future" and C denotes the event "The person now has or will have emphysema some time in the future," then the following


id: causality-20260505-4
file: data/en.wikipedia.org/wiki/Causality-4.md
category: reference
title: Causality
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Causality
date_saved: 2026-05-05T06:27:14.201565+00:00
instance: kb-cron
parent: causality-20260505-0
summary:
Type 1 and type 2 represent the same statistical dependencies (i.e., X {\displaystyle X} and Z {\displaystyle Z} are independent given Y {\displaystyle Y} ) and are, therefore, indistinguishable within purely cross-sectional data. Type 3, however, can be uniquely identified, since X {\displaystyle X} and Z {\displaystyle Z} are marginally independent and all other pairs are dependent. Thus, while the skeletons (the graphs stripped of arrows) of these three triplets are identical, the directionality of the arrows is partially identifiable. The same distinction applies when X {\displaystyle X} and Z {\displaystyle Z} have common ancestors, except that one must first condition on those ancestors. Algorithms have been developed to systematically determine the skeleton of the underlying graph and, then, orient all arrows whose directionality is dictated by the conditional independencies observed. Alternative methods of structure learning search through the many possible causal structures among the variables, and remove ones which are strongly incompatible with the observed correlations. In general this leaves a set of possible causal relations, which should then be tested by analyzing time series data or, preferably, designing appropriately controlled experiments. In contrast with Bayesian Networks, path analysis (and its generalization, structural equation modeling), serve better to estimate


id: causality-20260505-5
file: data/en.wikipedia.org/wiki/Causality-5.md
category: reference
title: Causality
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Causality
date_saved: 2026-05-05T06:27:14.201565+00:00
instance: kb-cron
parent: causality-20260505-0
summary:
=== Science === For the scientific investigation of efficient causality, the cause and effect are each best conceived of as temporally transient processes. Within the conceptual frame of the scientific method, an investigator sets up several distinct and contrasting temporally transient material processes that have the structure of experiments, and records candidate material responses, normally intending to determine causality in the physical world. For instance, one may want to know whether a high intake of carrots causes humans to develop the bubonic plague. The quantity of carrot intake is a process that is varied from occasion to occasion. The occurrence or non-occurrence of subsequent bubonic plague is recorded. To establish causality, the experiment must fulfill certain criteria, only one example of which is mentioned here. For example, instances of the hypothesized cause must be set up to occur at a time when the hypothesized effect is relatively unlikely in the absence of the hypothesized cause; such unlikelihood is to be established by empirical evidence. A mere observation of a correlation is not nearly adequate to establish causality. In nearly all cases, establishment of causality relies on repetition of experiments and probabilistic reasoning. Hardly ever is causality established more firmly than


id: causality-20260505-6
file: data/en.wikipedia.org/wiki/Causality-6.md
category: reference
title: Causality
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Causality
date_saved: 2026-05-05T06:27:14.201565+00:00
instance: kb-cron
parent: causality-20260505-0
summary:
Psychologists take an empirical approach to causality, investigating how people and non-human animals detect or infer causation from sensory information, prior experience and innate knowledge. Attribution: Attribution theory is the theory concerning how people explain individual occurrences of causation. Attribution can be external (assigning causality to an outside agent or force—claiming that some outside thing motivated the event) or internal (assigning causality to factors within the person—taking personal responsibility or accountability for one's actions and claiming that the person was directly responsible for the event). Taking causation one step further, the type of attribution a person provides influences their future behavior. The intention behind the cause or the effect can be covered by the subject of action. See also accident; blame; intent; and responsibility. Causal powers Whereas David Hume argued that causes are inferred from non-causal observations, Immanuel Kant claimed that people have innate assumptions about causes. Within psychology, Patricia Cheng attempted to reconcile the Humean and Kantian views. According to her power PC theory, people filter observations of events through an intuition that causes have the power to generate (or prevent) their effects, thereby inferring specific cause-effect relations. Causation and salience Our view of causation depends on what we


id: causality-20260505-7
file: data/en.wikipedia.org/wiki/Causality-7.md
category: reference
title: Causality
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Causality
date_saved: 2026-05-05T06:27:14.201565+00:00
instance: kb-cron
parent: causality-20260505-0
summary:
is postulated, in which y i {\displaystyle y_{i}} is the ith observation of the dependent variable (hypothesized to be the caused variable), x j , i {\displaystyle x_{j,i}} for j=1,...,k is the ith observation on the jth independent variable (hypothesized to be a causative variable), and e i {\displaystyle e_{i}} is the error term for the ith observation (containing the combined effects of all other causative variables, which must be uncorrelated with the included independent variables). If there is reason to believe that none of the x j {\displaystyle x_{j}} s is caused by y, then estimates of the coefficients a j {\displaystyle a_{j}} are obtained. If the null hypothesis that a j = 0 {\displaystyle a_{j}=0} is rejected, then the alternative hypothesis that a j ≠ 0 {\displaystyle a_{j}\neq 0} and equivalently that x j {\displaystyle x_{j}} causes y cannot be rejected. On the other hand, if the null hypothesis that a j = 0 {\displaystyle a_{j}=0} cannot be rejected, then equivalently the hypothesis of no causal effect of x j {\displaystyle x_{j}} on y cannot be rejected. Here the notion of causality is one of contributory causality as discussed above: If the true value a j ≠ 0


id: causality-20260505-8
file: data/en.wikipedia.org/wiki/Causality-8.md
category: reference
title: Causality
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Causality
date_saved: 2026-05-05T06:27:14.201565+00:00
instance: kb-cron
parent: causality-20260505-0
summary:
==== History ==== In the discussion of history, events are sometimes considered as if in some way being agents that can then bring about other historical events. Thus, the combination of poor harvests, the hardships of the peasants, high taxes, lack of representation of the people, and kingly ineptitude are among the causes of the French Revolution. This is a somewhat Platonic and Hegelian view that reifies causes as ontological entities. In Aristotelian terminology, this use approximates to the case of the efficient cause. Some philosophers of history such as Arthur Danto have claimed that "explanations in history and elsewhere" describe "not simply an event—something that happens—but a change". Like many practicing historians, they treat causes as intersecting actions and sets of actions which bring about "larger changes", in Danto's words: to decide "what are the elements which persist through a change" is "rather simple" when treating an individual's "shift in attitude", but "it is considerably more complex and metaphysically challenging when we are interested in such a change as, say, the break-up of feudalism or the emergence of nationalism". Much of the historical debate about causes has focused on the relationship between communicative and other actions, between singular and


id: causality-20260505-9
file: data/en.wikipedia.org/wiki/Causality-9.md
category: reference
title: Causality
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Causality
date_saved: 2026-05-05T06:27:14.201565+00:00
instance: kb-cron
parent: causality-20260505-0
summary:
Karma is the causality principle focusing on 1) causes, 2) actions, 3) effects, where it is the mind's phenomena that guide the actions that the actor performs. Buddhism trains the actor's actions for continued and uncontrived virtuous outcomes aimed at reducing suffering. This follows the Subject–verb–object structure. The general or universal definition of pratityasamutpada (or "dependent origination" or "dependent arising" or "interdependent co-arising") is that everything arises in dependence upon multiple causes and conditions; nothing exists as a singular, independent entity. A traditional example in Buddhist texts is of three sticks standing upright and leaning against each other and supporting each other. If one stick is taken away, the other two will fall to the ground. Causality in the Chittamatrin Buddhist school approach, Asanga's (c. 400 CE) mind-only Buddhist school, asserts that objects cause consciousness in the mind's image. Because causes precede effects, which must be different entities, then subject and object are different. For this school, there are no objects which are entities external to a perceiving consciousness. The Chittamatrin and the Yogachara Svatantrika schools accept that there are no objects external to the observer's causality. This largely follows the Nikayas approach. The Vaibhashika (c. 500 CE) is an


id: causality-20260505-10
file: data/en.wikipedia.org/wiki/Causality-10.md
category: reference
title: Causality
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Causality
date_saved: 2026-05-05T06:27:14.201565+00:00
instance: kb-cron
parent: causality-20260505-0
summary:
Material cause, the material whence a thing has come or that which persists while it changes, as for example, one's mother or the bronze of a statue (see also substance theory). Formal cause, whereby a thing's dynamic form or static shape determines the thing's properties and function, as a human differs from a statue of a human or as a statue differs from a lump of bronze. Efficient cause, which imparts the first relevant movement, as a human lifts a rock or raises a statue. This is the main topic of the present article. Final cause, the criterion of completion, or the end; it may refer to an action or to an inanimate process. Examples: Socrates takes a walk after dinner for the sake of his health; earth falls to the lowest level because that is its nature. Of Aristotle's four kinds or explanatory modes, only one, the 'efficient cause' is a cause as defined in the leading paragraph of this present article. The other three explanatory modes might be rendered material composition, structure and dynamics, and, again, criterion of completion. The word that Aristotle used was αἰτία. For the present purpose, that Greek word would be better translated as


id: causality-20260505-11
file: data/en.wikipedia.org/wiki/Causality-11.md
category: reference
title: Causality
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Causality
date_saved: 2026-05-05T06:27:14.201565+00:00
instance: kb-cron
parent: causality-20260505-0
summary:
==== Middle Ages ==== In line with Aristotelian cosmology, Thomas Aquinas posed a hierarchy prioritizing Aristotle's four causes: "final > efficient > material > formal". Aquinas sought to identify the first efficient cause—now simply first cause—as everyone would agree, said Aquinas, to call it God. Later in the Middle Ages, many scholars conceded that the first cause was God, but explained that many earthly events occur within God's design or plan, and thereby scholars sought freedom to investigate the numerous secondary causes. ==== After the Middle Ages ==== For Aristotelian philosophy before Aquinas, the word cause had a broad meaning. It meant 'answer to a why question' or 'explanation', and Aristotelian scholars recognized four kinds of such answers. With the end of the Middle Ages, in many philosophical usages, the meaning of the word 'cause' narrowed. It often lost that broad meaning, and was restricted to just one of the four kinds. For authors such as Niccolò Machiavelli, in the field of political thinking, and Francis Bacon, concerning science more generally, Aristotle's moving cause was the focus of their interest. A widely used modern definition of causality in this newly narrowed sense was assumed by David Hume. He undertook an


id: construct-philosophy-20260505-0
file: data/en.wikipedia.org/wiki/Construct_(philosophy)-0.md
category: reference
title: Construct (philosophy)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Construct_(philosophy)
date_saved: 2026-05-05T06:27:18.121717+00:00
instance: kb-cron
parent: construct-philosophy-20260505-0
summary:
In philosophy, a construct is an object which is ideal, that is, an object of the mind or of thought, meaning that its existence may be said to depend upon a subject's mind. This contrasts with any possibly mind-independent objects, the existence of which purportedly does not depend on the existence of a conscious observing subject. Thus, the distinction between these two terms may be compared to that between phenomenon and noumenon in other philosophical contexts and to many of the typical definitions of the terms realism and idealism also. In the correspondence theory of truth, ideas, such as constructs, are to be judged and checked according to how well they correspond with their referents, often conceived as part of a mind-independent reality. == Overview == As mind-dependent objects, concepts that are typically viewed as constructs include the abstract objects designated by such symbols as 3 or 4, or words such as liberty or cold as they are seen as a result of induction or abstraction that can be later applied to observable objects or compared to other constructs. Therefore, scientific hypotheses and theories (e.g. evolutionary theory, gravitational theory), as well as classifications (for example, in biological taxonomy), are also


id: consumer-demand-tests-animals-20260505-0
file: data/en.wikipedia.org/wiki/Consumer_demand_tests_(animals)-0.md
category: reference
title: Consumer demand tests (animals)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Consumer_demand_tests_(animals)
date_saved: 2026-05-05T06:27:19.409273+00:00
instance: kb-cron
parent: consumer-demand-tests-animals-20260505-0
summary:
Consumer demand tests for animals are studies designed to measure the relative strength of an animal's motivation to obtain resources such as different food items. Such demand tests quantify the strength of motivation animals have for resources whilst avoiding anthropomorphism and anthropocentrism. The test results are analogous to human patterns of purchasing resources with a limited income. For humans, the cost of resources is usually measured in money; in animal studies the cost is usually represented by energy required, time taken or a risk of injury. Costs of resources can be imposed on animals by an operant task (e.g. lever-pressing), a natural aversion (e.g. crossing water), or a homeostatic challenge (e.g. increased body temperature). Humans usually decrease the amount of an item purchased (or consumed) as the cost of that item increases. Similarly, animals tend to consume less of an item as the cost of that item increases (e.g. more lever presses required). Using consumer demand tests one can empirically determine the strength of motivation animals have for a definite need (e.g. food, water) and also for resources we humans might perceive as a luxury or unnecessary but animals might not (e.g. sand for dustbathing or additional space for caged


id: cross-impact-analysis-20260505-0
file: data/en.wikipedia.org/wiki/Cross_impact_analysis-0.md
category: reference
title: Cross impact analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Cross_impact_analysis
date_saved: 2026-05-05T06:27:20.565357+00:00
instance: kb-cron
parent: cross-impact-analysis-20260505-0
summary:
Cross-impact analysis is a methodology developed by Theodore J. Gordon and Olaf Helmer in 1966 to help determine how relationships between events would impact resulting events and reduce uncertainty in the future. The Central Intelligence Agency (CIA) became interested in the methodology in the late 1960s and early 1970s as an analytic technique for predicting how different factors and variables would impact future decisions. In the mid-1970s, futurists began to use the methodology in larger numbers as a means to predict the probability of specific events and determine how related events impacted one another. By 2006, cross-impact analysis matured into a number of related methodologies with uses for businesses and communities as well as futurists and intelligence analysts. == Development == The basic principles of cross-impact analysis date back to the late 1960s, but the original processes were relatively simple and were based on a game design. Eventually, advanced techniques, methodologies, and programs were developed to apply the principles of cross-impact analysis, and the basic method is now applied in futures think tanks, business settings, and the intelligence community. Theodore J. Gordon writes that cross-impact analysis was the result of a question: "can forecasting be based on perceptions about how


id: cross-impact-analysis-20260505-1
file: data/en.wikipedia.org/wiki/Cross_impact_analysis-1.md
category: reference
title: Cross impact analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Cross_impact_analysis
date_saved: 2026-05-05T06:27:20.565357+00:00
instance: kb-cron
parent: cross-impact-analysis-20260505-0
summary:
==== Relationship to Delphi technique ==== The accuracy of the math and specific events requires special expertise in the events or topic of discussion. In order to get the insight needed to get events and calculations, analysts typically contact a large number of experts and ask their opinions on events or probabilities in person as groups or through surveys. These groupings often resemble the Delphi Technique, which is an analytic technique that gathers a group of experts on a subject together and asks their opinion on a scenario or prediction. Usually, analysts consider the average prediction or scenario as the most likely to occur. The two are so closely related, that analysts often use the two techniques in combination or as part of a larger methodology. ==== Strengths ==== The futurist forecasting style of cross-impact analysis carries a few key strengths. Its use of groups of experts ensures a number of opinions worth considering when calculating probabilities of events. The level of mathematics in calculating probabilities ensures that the results are as accurate as a researcher can make them. In addition, when used on consort with other analytic techniques, this type of cross-impact analysis can give greater quantitative results to


id: deductive-nomological-model-20260505-0
file: data/en.wikipedia.org/wiki/Deductive-nomological_model-0.md
category: reference
title: Deductive-nomological model
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Deductive-nomological_model
date_saved: 2026-05-05T06:27:24.469458+00:00
instance: kb-cron
parent: deductive-nomological-model-20260505-0
summary:
The deductive-nomological model (DN model) of scientific explanation, also known as Hempel's model, the Hempel–Oppenheim model, the Popper–Hempel model, or the covering law model, is a formal view of scientifically answering questions asking, "Why...?". The DN model poses scientific explanation as a deductive structure, one where truth of its premises entails truth of its conclusion, hinged on accurate prediction or postdiction of the phenomenon to be explained. Because of problems concerning humans' ability to define, discover, and know causality, this was omitted in initial formulations of the DN model. Causality was thought to be incidentally approximated by realistic selection of premises that derive the phenomenon of interest from observed starting conditions plus general laws. Still, the DN model formally permitted causally irrelevant factors. Also, derivability from observations and laws sometimes yielded absurd answers. When logical empiricism fell out of favor in the 1960s, the DN model was widely seen as a flawed or greatly incomplete model of scientific explanation. Nonetheless, it remained an idealized version of scientific explanation, and one that was rather accurate when applied to modern physics. In the early 1980s, a revision to the DN model emphasized maximal specificity for relevance of the conditions and axioms stated.


id: deductive-nomological-model-20260505-1
file: data/en.wikipedia.org/wiki/Deductive-nomological_model-1.md
category: reference
title: Deductive-nomological model
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Deductive-nomological_model
date_saved: 2026-05-05T06:27:24.469458+00:00
instance: kb-cron
parent: deductive-nomological-model-20260505-0
summary:
== Growth == Whereas Comtean positivism posed science as description, logical positivism emerged in the late 1920s and posed science as explanation, perhaps to better unify empirical sciences by covering not only fundamental science—that is, fundamental physics—but special sciences, too, such as biology, psychology, economics, and anthropology. After defeat of National Socialism with World War II's close in 1945, logical positivism shifted to a milder variant, logical empiricism. All variants of the movement, which lasted until 1965, are neopositivism, sharing the quest of verificationism. Neopositivists led emergence of the philosophy subdiscipline philosophy of science, researching such questions and aspects of scientific theory and knowledge. Scientific realism takes scientific theory's statements at face value, thus accorded either falsity or truth—probable or approximate or actual. Neopositivists held scientific antirealism as instrumentalism, holding scientific theory as simply a device to predict observations and their course, while statements on nature's unobservable aspects are elliptical at or metaphorical of its observable aspects, rather. DN model received its most detailed, influential statement by Carl G Hempel, first in his 1942 article "The function of general laws in history", and more explicitly with Paul Oppenheim in their 1948 article "Studies in the logic of explanation". Leading logical


id: deductive-nomological-model-20260505-2
file: data/en.wikipedia.org/wiki/Deductive-nomological_model-2.md
category: reference
title: Deductive-nomological model
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Deductive-nomological_model
date_saved: 2026-05-05T06:27:24.469458+00:00
instance: kb-cron
parent: deductive-nomological-model-20260505-0
summary:
== Weaknesses == By DN model, if one asks, "Why is that shadow 20 feet long?", another can answer, "Because that flagpole is 15 feet tall, the Sun is at x angle, and laws of electromagnetism". Yet by problem of symmetry, if one instead asked, "Why is that flagpole 15 feet tall?", another could answer, "Because that shadow is 20 feet long, the Sun is at x angle, and laws of electromagnetism", likewise a deduction from observed conditions and scientific laws, but an answer clearly incorrect. By the problem of irrelevance, if one asks, "Why did that man not get pregnant?", one could in part answer, among the explanans, "Because he took birth control pills"—if he factually took them, and the law of their preventing pregnancy—as covering law model poses no restriction to bar that observation from the explanans. Many philosophers have concluded that causality is integral to scientific explanation. DN model offers a necessary condition of a causal explanation—successful prediction—but not sufficient conditions of causal explanation, as a universal regularity can include spurious relations or simple correlations, for instance Z always following Y, but not Z because of Y, instead Y and then Z as an effect of X.


id: deductive-nomological-model-20260505-3
file: data/en.wikipedia.org/wiki/Deductive-nomological_model-3.md
category: reference
title: Deductive-nomological model
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Deductive-nomological_model
date_saved: 2026-05-05T06:27:24.469458+00:00
instance: kb-cron
parent: deductive-nomological-model-20260505-0
summary:
== Covering action == Through lawlike explanation, fundamental physics—often perceived as fundamental science—has proceeded through intertheory relation and theory reduction, thereby resolving experimental paradoxes to great historical success, resembling covering law model. In early 20th century, Ernst Mach as well as Wilhelm Ostwald had resisted Ludwig Boltzmann's reduction of thermodynamics—and thereby Boyle's law—to statistical mechanics partly because it rested on kinetic theory of gas, hinging on atomic/molecular theory of matter. Mach as well as Ostwald viewed matter as a variant of energy, and molecules as mathematical illusions, as even Boltzmann thought possible. In 1905, via statistical mechanics, Albert Einstein predicted the phenomenon Brownian motion—unexplained since reported in 1827 by botanist Robert Brown. Soon, most physicists accepted that atoms and molecules were unobservable yet real. Also in 1905, Einstein explained the electromagnetic field's energy as distributed in particles, doubted until this helped resolve atomic theory in the 1910s and 1920s. Meanwhile, all known physical phenomena were gravitational or electromagnetic, whose two theories misaligned. Yet belief in aether as the source of all physical phenomena was virtually unanimous. At experimental paradoxes, physicists modified the aether's hypothetical properties. Finding the luminiferous aether a useless hypothesis, Einstein in 1905 a priori unified all inertial


id: deductive-nomological-model-20260505-4
file: data/en.wikipedia.org/wiki/Deductive-nomological_model-4.md
category: reference
title: Deductive-nomological model
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Deductive-nomological_model
date_saved: 2026-05-05T06:27:24.469458+00:00
instance: kb-cron
parent: deductive-nomological-model-20260505-0
summary:
== Sources == Avent, Ryan, "The Q&A: Brian Greene—life after the Higgs", The Economist blog: Babbage, 19 Jul 2012. Ayala, Francisco J & Theodosius G Dobzhansky, eds, Studies in the Philosophy of Biology: Reduction and Related Problems (Berkeley & Los Angeles: University of California Press, 1974). Bechtel, William, Discovering Cell Mechanisms: The Creation of Modern Cell Biology (New York: Cambridge University Press, 2006). Bechtel, William, Philosophy of Science: An Overview for Cognitive Science (Hillsdale, NJ: Lawrence Erlbaum Associates, 1988). Bem, Sacha & Huib L de Jong, Theoretical Issues in Psychology: An Introduction, 2nd edn (London: Sage Publications, 2006). Ben-Menahem, Yemima, Conventionalism: From Poincaré to Quine (Cambridge: Cambridge University Press, 2006). Blackmore, J T & R Itagaki, S Tanaka, eds, Ernst Mach's Vienna 1895–1930: Or Phenomenalism as Philosophy of Science (Dordrecht: Kluwer Academic Publishers, 2001). Boffetta, Paolo, "Causation in the presence of weak associations", Critical Reviews in Food Science and Nutrition, 2010 Dec; 50(s1):13–16. Bolotin, David, An Approach to Aristotle's Physics: With Particular Attention to the Role of His Manner of Writing (Albany: State University of New York Press, 1998). Bourdeau, Michel, "Auguste Comte", in Edward N Zalta, ed, The Stanford Encyclopedia of Philosophy, Winter 2013 edn. Braibant, Sylvie & Giorgio


id: deductive-nomological-model-20260505-5
file: data/en.wikipedia.org/wiki/Deductive-nomological_model-5.md
category: reference
title: Deductive-nomological model
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Deductive-nomological_model
date_saved: 2026-05-05T06:27:24.469458+00:00
instance: kb-cron
parent: deductive-nomological-model-20260505-0
summary:
Rowlands, Peter, Oliver Lodge and the Liverpool Physical Society (Liverpool: Liverpool University Press, 1990). Sarkar, Sahotra & Jessica Pfeifer, eds, The Philosophy of Science: An Encyclopedia, Volume 1: A–M (New York: Routledge, 2006). Schwarz, John H (1998). "Recent developments in superstring theory". Proceedings of the National Academy of Sciences of the United States of America. 95 (6): 2750–7. Bibcode:1998PNAS...95.2750S. doi:10.1073/pnas.95.6.2750. PMC 19640. PMID 9501161. Schweber, Silvan S, QED and the Men who Made it: Dyson, Feynman, Schwinger, and Tomonaga (Princeton: Princeton University Press, 1994). Schliesser, Eric, "Hume's Newtonianism and anti-Newtonianism", in Edward N Zalta, ed, The Stanford Encyclopedia of Philosophy, Winter 2008 edn. Spohn, Wolfgang, The Laws of Belief: Ranking Theory and Its Philosophical Applications (Oxford: Oxford University Press, 2012). Suppe, Frederick, ed, The Structure of Scientific Theories, 2nd edn (Urbana, Illinois: University of Illinois Press, 1977). Tavel, Morton, Contemporary Physics and the Limits of Knowledge (Piscataway, NJ: Rutgers University Press, 2002). Torretti, Roberto, The Philosophy of Physics (New York: Cambridge University Press, 1999). Vongehr, Sascha, "Higgs discovery rehabilitating despised Einstein Ether", Science 2.0: Alpha Meme website, 13 Dec 2011. Vongehr, Sascha, "Supporting abstract relational space-time as fundamental without doctrinism against emergence, arXiv (History and Philosophy of Physics):0912.3069, 2 Oct


id: discovery-science-20260505-0
file: data/en.wikipedia.org/wiki/Discovery_science-0.md
category: reference
title: Discovery science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Discovery_science
date_saved: 2026-05-05T06:27:25.806025+00:00
instance: kb-cron
parent: discovery-science-20260505-0
summary:
Discovery science (also known as discovery-based science) is a scientific methodology which aims to find new patterns, correlations, and form hypotheses through the analysis of large-scale experimental data. The term “discovery science” encompasses various fields of study, including basic, translational, and computational science and research. Discovery-based methodologies are commonly contrasted with traditional scientific practice, the latter involving hypothesis formation before experimental data is closely examined. Discovery science involves the process of inductive reasoning or using observations to make generalisations, and can be applied to a range of science-related fields, e.g., medicine, proteomics, hydrology, psychology, and psychiatry. == Overview == === Purpose === Discovery science places an emphasis on 'basic' discovery, which can fundamentally change the status quo. For example, in the early years of water resources research, the use of discovery science was demonstrated by seeking to elucidate phenomena that were, until that point, unexplained. It did not matter how unusual these ideas may have been perceived to be. In this sense, discovery science is based on the attitude that "we must not allow our concepts of the earth, in so far as they transcend the reach of observation, to root themselves so deeply and so firmly in our minds


id: discovery-science-20260505-1
file: data/en.wikipedia.org/wiki/Discovery_science-1.md
category: reference
title: Discovery science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Discovery_science
date_saved: 2026-05-05T06:27:25.806025+00:00
instance: kb-cron
parent: discovery-science-20260505-0
summary:
=== Proteomics === Another example of discovery science is proteomics, a technology-driven and technology limited discovery science. Technologies for proteomic analysis provide information that is useful in discovery science. Proteome analysis as a discovery science is applicable in biotechnology, e.g., it assists in 1) the discovery of biochemical pathways which can identify targets for therapies, 2) developing new processes for manufacturing biological materials, 3) monitoring manufacturing processes for the purpose of quality control, and 4) developing diagnostic tests and efficacious treatment strategies for clinical diseases. In the context of proteomics, current life-science research remains technology-limited, however, recent available tools have assisted in evolving such research from being hypothesis-driven to discovery-driven. === Hydrology === Field hydrology has experienced a decline in progress due to a change from discovery-based field work to the gathering of data for modal parameterisation. In field hydrology, models are not any more useful than an understanding of how systems work, and discovery science allows for this understanding. Several important examples of field-based inquiry and discovery have taken place in field hydrology. These include: identifying spatial patterns of soil moisture and how they relate to topography; interrogating such data through the use of geostatistics; and discovering the importance


id: discovery-science-20260505-2
file: data/en.wikipedia.org/wiki/Discovery_science-2.md
category: reference
title: Discovery science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Discovery_science
date_saved: 2026-05-05T06:27:25.806025+00:00
instance: kb-cron
parent: discovery-science-20260505-0
summary:
== Methodology == Discovery-based methodologies are often viewed in contrast to traditional scientific practice, where hypotheses are formed before close examination of experimental data. However, from a philosophical perspective where all or most of the observable "low-hanging fruit" has already been plucked, examining the phenomenological world more closely than the senses alone (even augmented senses, e.g. via microscopes, telescopes, bifocals etc.) opens a new source of knowledge for hypothesis formation. This process is also known as inductive reasoning or the use of specific observations to make generalisations. Discovery science is usually a complex process, and consequently does not follow a simple linear cause and effect pattern. This means that outcomes are uncertain, and it is expected to have disappointing results as a fundamental part of discovery science. In particular, this may apply to medicine for the critically ill, where disease syndromes may be complex and multi-factorial. In psychiatry, studying complex relationships between brain and behaviour requires a large-scale science. This calls for a need to conceptually switch from hypothesis-driven studies to hypothesis-generating research which is discovery-based. Normally, discovery-based approaches for research are initially hypothesis-free, however, hypothesis testing can be elevated to a new level that effectively supports traditional hypothesis-driven studies.


id: experimenter-s-regress-20260505-0
file: data/en.wikipedia.org/wiki/Experimenter's_regress-0.md
category: reference
title: Experimenter's regress
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Experimenter's_regress
date_saved: 2026-05-05T06:27:28.417319+00:00
instance: kb-cron
parent: experimenter-s-regress-20260505-0
summary:
In science, experimenter's regress refers to a loop of dependence between theory and evidence. In order to judge whether a new piece of evidence is correct we rely on theory-based predictions, and to judge the value of competing theories we rely on existing evidence. Cognitive bias affects experiments, and experiments determine which theory is valid. This issue is particularly important in new fields of science where there is no consensus regarding the values of various competing theories, and where the extent of experimental errors is not well known. If experimenter's regress acts a positive feedback system, it can be a source of pathological science. An experimenter's strong belief in a new theory produces confirmation bias, and any biased evidence they obtain then strengthens their belief in that particular theory. Neither individual researchers nor entire scientific communities are immune to this effect: see N-rays and polywater. Experimenter's regress is a typical relativistic phenomenon in the Empirical Programme of Relativism (EPOR). EPOR is very much concerned with a focus on social interactions, by looking at particular (local) cases and controversial issues in the context in which they happen. In EPOR, all scientific knowledge is perceived to be socially constructed and is thus


id: experimentum-crucis-20260505-0
file: data/en.wikipedia.org/wiki/Experimentum_crucis-0.md
category: reference
title: Experimentum crucis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Experimentum_crucis
date_saved: 2026-05-05T06:27:29.598896+00:00
instance: kb-cron
parent: experimentum-crucis-20260505-0
summary:
In science, an experimentum crucis (English: crucial experiment or critical experiment) is an experiment capable of decisively determining whether or not a particular hypothesis or theory is superior to all others whose acceptance is currently widespread in the scientific community. In particular, such an experiment—if true—must typically be able to produce a result that rules out all other hypotheses or theories, thereby demonstrating that under the conditions of the experiment (i.e., under the same external circumstances and for the same "input variables" within the experiment), those hypotheses and theories are proven false but the experimenter's hypothesis is not ruled out. An opposite view, rejecting the decisive value of the experimentum crucis in choosing one hypothesis or theory over its rivals, is the Duhem–Quine thesis. == History == Francis Bacon in his Novum Organum first described the concept of a situation in which one theory but not others would hold true, using the name instantia crucis ("crucial instance"). The phrase experimentum crucis, denoting the deliberate creation of such a situation for the purpose of testing the rival theories, was later coined by Robert Hooke and then famously used by Isaac Newton and Robert Boyle. The production of such an experiment is


id: free-parameter-20260505-0
file: data/en.wikipedia.org/wiki/Free_parameter-0.md
category: reference
title: Free parameter
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Free_parameter
date_saved: 2026-05-05T06:27:33.349647+00:00
instance: kb-cron
parent: free-parameter-20260505-0
summary:
A free parameter is a variable in a mathematical model which cannot be predicted precisely or constrained by the model and must be estimated experimentally or theoretically. A mathematical model, theory, or conjecture is more likely to be right and less likely to be the product of wishful thinking if it relies on few free parameters and is consistent with large amounts of data. == See also == Decision variables Exogenous variables Overfitting Random variables State variables == References ==


id: fringe-science-20260505-0
file: data/en.wikipedia.org/wiki/Fringe_science-0.md
category: reference
title: Fringe science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Fringe_science
date_saved: 2026-05-05T06:27:34.548986+00:00
instance: kb-cron
parent: fringe-science-20260505-0
summary:
Fringe science refers to ideas whose attributes include being highly speculative or relying on premises already refuted. The chance of ideas rejected by editors and published outside the mainstream being correct is remote. When the general public does not distinguish between science and imitators, it risks exploitation, and in some cases, a "yearning to believe or a generalized suspicion of experts is a very potent incentive to accepting some pseudoscientific claims". The term "fringe science" covers everything from novel hypotheses, which can be tested utilizing the scientific method, to wild ad hoc hypotheses and mumbo jumbo. This has resulted in a tendency to dismiss all fringe science as the domain of pseudoscientists, hobbyists, and quacks. A concept that was once accepted by the mainstream scientific community may become fringe science because of a later evaluation of previous research. For example, focal infection theory, which held that focal infections of the tonsils or teeth are a primary cause of systemic disease, was once considered to be medical fact. It has since been dismissed because of a lack of evidence. == Description == The boundary between fringe science and pseudoscience is disputed. Friedlander writes that there is no widespread understanding of what


id: fringe-science-20260505-1
file: data/en.wikipedia.org/wiki/Fringe_science-1.md
category: reference
title: Fringe science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Fringe_science
date_saved: 2026-05-05T06:27:34.548986+00:00
instance: kb-cron
parent: fringe-science-20260505-0
summary:
== Responding to fringe science == Michael W. Friedlander has suggested some guidelines for responding to fringe science, which, he argues, is a more difficult problem than scientific misconduct. His suggested methods include impeccable accuracy, checking cited sources, not overstating orthodox science, thorough understanding of the Wegener continental drift example, examples of orthodox science investigating radical proposals, and prepared examples of errors from fringe scientists. Friedlander suggests that fringe science is necessary so mainstream science will not atrophy. Scientists must evaluate the plausibility of each new fringe claim, and certain fringe discoveries "will later graduate into the ranks of accepted" — while others "will never receive confirmation". Margaret Wertheim profiled many "outsider scientists" in her book Physics on the Fringe, who receive little or no attention from professional scientists. She describes all of them as trying to make sense of the world using the scientific method but in the face of being unable to understand modern science's complex theories. She also finds it fair that credentialed scientists do not bother spending a lot of time learning about and explaining problems with the fringe theories of uncredentialed scientists since the authors of those theories have not taken the time to understand


id: heterophenomenology-20260505-0
file: data/en.wikipedia.org/wiki/Heterophenomenology-0.md
category: reference
title: Heterophenomenology
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Heterophenomenology
date_saved: 2026-05-05T06:27:39.537410+00:00
instance: kb-cron
parent: heterophenomenology-20260505-0
summary:
In the thought of the philosopher Daniel Dennett, heterophenomenology ("phenomenology of another, not oneself") is an explicitly third-person, scientific approach to the study of consciousness and other mental phenomena. It consists of applying the scientific method with an anthropological bent, combining the subject's self-reports with all other available evidence to determine their mental state. The goal is to discover how subjects see the world themselves, without taking the accuracy of the subject's view for granted. == Overview == Heterophenomenology is put forth as the alternative to traditional Cartesian phenomenology, which Dennett calls "lone-wolf autophenomenology" to emphasize the fact that traditional phenomenology accepts the subject's self-reports as being authoritative. In contrast, heterophenomenology considers the subjects authoritative only about how things seem to them. It does not dismiss the Cartesian first-person perspective, but rather brackets it so that it can be intersubjectively verified by empirical means, allowing it to be submitted as scientific evidence. The method requires a researcher to listen to the subjects and take what they say seriously, but to also look at everything else available to them, including the subject's bodily responses and environment, evidence provided by relevant neurological or psychological studies, the researcher's memories of their own experiences,


id: hypothetico-deductive-model-20260505-0
file: data/en.wikipedia.org/wiki/Hypothetico-deductive_model-0.md
category: reference
title: Hypothetico-deductive model
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Hypothetico-deductive_model
date_saved: 2026-05-05T06:27:43.077566+00:00
instance: kb-cron
parent: hypothetico-deductive-model-20260505-0
summary:
The hypothetico-deductive model or method is a proposed description of the scientific method. According to it, scientific inquiry proceeds by formulating a hypothesis in a form that can be falsifiable, using a test on observable data where the outcome is not yet known. A test outcome that could have and does run contrary to predictions of the hypothesis is taken as a falsification of the hypothesis. A test outcome that could have, but does not run contrary to the hypothesis corroborates the theory. It is then proposed to compare the explanatory value of competing hypotheses by testing how stringently they are corroborated by their predictions. == Example == One example of an algorithmic statement of the hypothetico-deductive method is as follows: One possible sequence in this model would be 1, 2, 3, 4. If the outcome of 4 holds, and 3 is not yet disproven, you may continue with 3, 4, 1, and so forth; but if the outcome of 4 shows 3 to be false, you will have to go back to 2 and try to invent a new 2, deduce a new 3, look for 4, and so forth. Note that this method can never absolutely verify (prove


id: interrogating-ethnography-20260505-0
file: data/en.wikipedia.org/wiki/Interrogating_Ethnography-0.md
category: reference
title: Interrogating Ethnography
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Interrogating_Ethnography
date_saved: 2026-05-05T06:27:45.649787+00:00
instance: kb-cron
parent: interrogating-ethnography-20260505-0
summary:
Interrogating Ethnography: Why Evidence Matters is a 2017 book by Steven Lubet of Northwestern University Law School, critiquing methods used in the discipline of ethnography. Writing in Contexts, Syid Ali of Long Island University called it "an essential critique of the most public-facing product sociology has to offer." Interrogating Ethnography, which criticizes ethnographers for the practice of changing the name of the area they study, is part of the Replication crisis. But it is largely a critique of ethnography methodology when it relies on the narratives of interviewees with no attempt to verify assertions of fact. Lubet began the project of writing this book after reading and publishing a notable critique of the use of evidence Alice Goffman's controversial 2014 book On the Run: Fugitive Life in an American City, That critique led him to read and attempt to verify "more than 50 ethnographic monographs and an equivalent number of articles. Focusing on sociologists’ studies of American cities... (and checking) facts that could be documented — or not... by consulting experts and pulling public records." According to the Chronicle of Higher Education, the book "has touched off a debate about what ethnographers might learn from legal scholars, and vice versa.


id: inverse-square-law-20260505-0
file: data/en.wikipedia.org/wiki/Inverse-square_law-0.md
category: reference
title: Inverse-square law
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Inverse-square_law
date_saved: 2026-05-05T06:27:46.877028+00:00
instance: kb-cron
parent: inverse-square-law-20260505-0
summary:
In physical science, an inverse-square law is any scientific law stating that the observed "intensity" of a specified physical quantity (being nothing more than the value of the physical quantity) is inversely proportional to the square of the distance from the source of that physical quantity. The fundamental cause for this can be understood as geometric dilution corresponding to point-source radiation into three-dimensional space. Radar energy expands during both the signal transmission and the reflected return, so the inverse square for both paths means that the radar will receive energy according to the inverse fourth power of the range. To prevent dilution of energy while propagating a signal, certain methods can be used such as a waveguide, which acts like a canal does for water, or how a gun barrel restricts hot gas expansion to one dimension in order to prevent loss of energy transfer to a bullet. == Formula == In mathematical notation the inverse square law can be expressed as an intensity (I) varying as a function of distance (d) from some centre. The intensity is proportional (see ∝) to the reciprocal of the square of the distance thus: intensity ∝ 1 distance 2 {\displaystyle {\text{intensity}}\ \propto \


id: inverse-square-law-20260505-1
file: data/en.wikipedia.org/wiki/Inverse-square_law-1.md
category: reference
title: Inverse-square law
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Inverse-square_law
date_saved: 2026-05-05T06:27:46.877028+00:00
instance: kb-cron
parent: inverse-square-law-20260505-0
summary:
=== Light and other electromagnetic radiation === The intensity (or illuminance or irradiance) of light or other linear waves radiating from a point source (energy per unit of area perpendicular to the source) is inversely proportional to the square of the distance from the source, so an object (of the same size) twice as far away receives only one-quarter the energy (in the same time period). More generally, the irradiance, i.e., the intensity (or power per unit area in the direction of propagation), of a spherical wavefront varies inversely with the square of the distance from the source (assuming there are no losses caused by absorption or scattering). For example, the intensity of radiation from the Sun is 9126 watts per square meter at the distance of Mercury (0.387 AU) but only 1367 watts per square meter at the distance of Earth (1 AU)—an approximate threefold increase in distance results in an approximate ninefold decrease in intensity of radiation. For non-isotropic radiators such as parabolic antennas, headlights, and lasers, the effective origin is located far behind the beam aperture. If you are close to the origin, you don't have to go far to double the radius, so the signal drops


id: inverse-square-law-20260505-2
file: data/en.wikipedia.org/wiki/Inverse-square_law-2.md
category: reference
title: Inverse-square law
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Inverse-square_law
date_saved: 2026-05-05T06:27:46.877028+00:00
instance: kb-cron
parent: inverse-square-law-20260505-0
summary:
== Non-Euclidean implications == The inverse-square law, fundamental in Euclidean spaces, also applies to non-Euclidean geometries, including hyperbolic space. The curvature present in these spaces alters physical laws, influencing a variety of fields such as cosmology, general relativity, and string theory. John D. Barrow, in his 2020 paper "Non-Euclidean Newtonian Cosmology," expands on the behavior of force (F) and potential (Φ) within hyperbolic 3-space (H3). He explains that F and Φ obey the relationships F ∝ 1 / R² sinh²(r/R) and Φ ∝ coth(r/R), where R represents the curvature radius and r represents the distance from the focal point. The concept of spatial dimensionality, first proposed by Immanuel Kant, remains a topic of debate concerning the inverse-square law. Dimitria Electra Gatzia and Rex D. Ramsier, in their 2021 paper, contend that the inverse-square law is more closely related to force distribution symmetry than to the dimensionality of space. In the context of non-Euclidean geometries and general relativity, deviations from the inverse-square law do not arise from the law itself but rather from the assumption that the force between two bodies is instantaneous, which contradicts special relativity. General relativity reinterprets gravity as the curvature of spacetime, leading particles to move along


id: isotope-dilution-20260505-0
file: data/en.wikipedia.org/wiki/Isotope_dilution-0.md
category: reference
title: Isotope dilution
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Isotope_dilution
date_saved: 2026-05-05T06:27:48.078101+00:00
instance: kb-cron
parent: isotope-dilution-20260505-0
summary:
Isotope dilution analysis is a method of determining the quantity of chemical substances. In its most simple conception, the method of isotope dilution comprises the addition of known amounts of isotopically enriched substance to the analyzed sample. Mixing of the isotopic standard with the sample effectively "dilutes" the isotopic enrichment of the standard and this forms the basis for the isotope dilution method. Isotope dilution is classified as a method of internal standardisation, because the standard (isotopically enriched form of analyte) is added directly to the sample. In addition, unlike traditional analytical methods which rely on signal intensity, isotope dilution employs signal ratios. Owing to both of these advantages, the method of isotope dilution is regarded among chemistry measurement methods of the highest metrological standing. Isotopes are variants of a particular chemical element which differ in neutron number. All isotopes of a given element have the same number of protons in each atom. The term isotope is formed from the Greek roots isos (ἴσος "equal") and topos (τόπος "place"), meaning "the same place"; thus, the meaning behind the name is that different isotopes of a single element occupy the same position on the periodic table. == Early history == Analytical


id: isotope-dilution-20260505-1
file: data/en.wikipedia.org/wiki/Isotope_dilution-1.md
category: reference
title: Isotope dilution
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Isotope_dilution
date_saved: 2026-05-05T06:27:48.078101+00:00
instance: kb-cron
parent: isotope-dilution-20260505-0
summary:
n A = n B R B − R A B R A B − R A × 1 + R A 1 + R B {\displaystyle n_{\mathrm {A} }=n_{\mathrm {B} }{\frac {R_{\mathrm {B} }-R_{\mathrm {AB} }}{R_{\mathrm {AB} }-R_{\mathrm {A} }}}\times {\frac {1+R_{\mathrm {A} }}{1+R_{\mathrm {B} }}}} In a typical gas chromatography analysis, isotopic dilution can decrease the uncertainty of the measurement results from 5% to 1%. It can also be used in mass spectrometry (commonly referred to as isotopic dilution mass spectrometry or IDMS), in which the isotopic ratio can be determined with precision typically better than 0.25%. === Optimum composition of the blend === In a simplified manner, the uncertainty of the measurement results is largely determined from the measurement of RAB: u ( n A ) 2 ∝ ( ∂ n A ∂ R A B ) 2 u ( R A B ) 2 = n A 2 ( R A − R B ) 2 ( R A − R A B ) 2 ( R A B − R B ) 2 u ( R A B ) 2 {\displaystyle u(n_{\mathrm {A} })^{2}\propto \left({\frac {\partial {n_{\mathrm {A} }}}{\partial R_{\mathrm {AB} }}}\right)^{2}u(R_{\mathrm {AB} })^{2}=n_{\mathrm {A}


id: leiden-manifesto-20260505-0
file: data/en.wikipedia.org/wiki/Leiden_Manifesto-0.md
category: reference
title: Leiden Manifesto
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Leiden_Manifesto
date_saved: 2026-05-05T06:27:50.466262+00:00
instance: kb-cron
parent: leiden-manifesto-20260505-0
summary:
The Leiden Manifesto for research metrics (LM) is a list of "ten principles to guide research evaluation", published as a comment in Nature on 22 April 2015. It was formulated by public policy professor Diana Hicks, scientometrics professor Paul Wouters, and their colleagues at the 19th International Conference on Science and Technology Indicators, held between 3–5 September 2014 in Leiden, The Netherlands. The Leiden Manifesto was proposed as a guide to combat misuse of bibliometrics when evaluating scientific research literature. Examples of commonly used bibliometrics for science, or scientometrics, are the h-index, impact factor, other indicators such as Altmetrics. According to the Manifesto's authors, these metrics often pervasively misguide evaluations of scientific material. == Motivation == Motivations for codification of the Leiden Manifesto arose from a growing worry that "impact-factor obsession" was leading to inadequate judgement of scientific material that should be worthy of fair evaluation. Lead author Diana Hicks hoped that publishing the list in Nature would spread its ideas, already commonplace in the scientometrics sphere, to the broader scientific community. === DORA and other predecessors === The scientific community has long been interested in reforming assessment of the impact of scientific and academic research. The 2013 San Francisco


id: leiden-manifesto-20260505-1
file: data/en.wikipedia.org/wiki/Leiden_Manifesto-1.md
category: reference
title: Leiden Manifesto
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Leiden_Manifesto
date_saved: 2026-05-05T06:27:50.466262+00:00
instance: kb-cron
parent: leiden-manifesto-20260505-0
summary:
=== Public endorsement === LIBER, a collaboration of European research libraries, issued a substantial review of the Leiden Manifesto in 2017, concluding that it was a "solid foundation" on which academic libraries could base their assessment of metrics. Elsevier, a global leader in research publishing and information analytics, announced on 14 July 2020 that it would use the Leiden Manifesto to guide its development of improved research evaluation. Elsevier stated that the principles of the manifesto were already close in nature to their 2019 CiteScore metrics, which was in summary "improved calculation methodology" for "a more robust, fair and faster indicator of research impact". Loughborough University's LIS-Bibliometrics committee chose to base their own principles on the Leiden Manifesto, instead of the DORA, because the manifesto takes a "broader approach to the responsible use of all bibliometrics across a range of disciplines and settings", according to their policy manager Elizabeth Gadd. Stephen Curry, chair of the DORA steering committee, commented on this statement by emphasizing that DORA was aiming to extend its "disciplinary and geographical reach". === Further applications === David Moher and his co-authors referenced the Leiden Manifesto in a perspective for Issues in Science and Technology, writing that academic


id: level-of-measurement-20260505-0
file: data/en.wikipedia.org/wiki/Level_of_measurement-0.md
category: reference
title: Level of measurement
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Level_of_measurement
date_saved: 2026-05-05T06:27:51.709058+00:00
instance: kb-cron
parent: level-of-measurement-20260505-0
summary:
Level of measurement or scale of measure is a classification that describes the nature of information within the values assigned to variables. Psychologist Stanley Smith Stevens developed the best-known classification with four levels, or scales, of measurement: nominal, ordinal, interval, and ratio. This framework of distinguishing levels of measurement originated in psychology and has since had a complex history, being adopted and extended in some disciplines and by some scholars, and criticized or rejected by others. Other classifications include those by Mosteller and Tukey, and by Chrisman. Stevens proposed his typology in a 1946 Science article titled "On the theory of scales of measurement". In that article, Stevens claimed that all measurement in science was conducted using four different types of scales that he called "nominal", "ordinal", "interval", and "ratio", unifying both "qualitative" (which are described by his "nominal" type) and "quantitative" (to a different degree, all the rest of his scales). The concept of scale types later received the mathematical rigour that it lacked at its inception with the work of mathematical psychologists Theodore Alper (1985, 1987), Louis Narens (1981a, b), and R. Duncan Luce (1986, 1987, 2001). As Luce (1997, p. 395) wrote: S. S. Stevens (1946, 1951,


id: level-of-measurement-20260505-1
file: data/en.wikipedia.org/wiki/Level_of_measurement-1.md
category: reference
title: Level of measurement
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Level_of_measurement
date_saved: 2026-05-05T06:27:51.709058+00:00
instance: kb-cron
parent: level-of-measurement-20260505-0
summary:
==== Central tendency and dispersion ==== According to Stevens, for ordinal data, the appropriate measure of central tendency is the median (the mode is also allowed, but not the mean), and the appropriate measure of dispersion is percentile or quartile (the standard deviation is not allowed). Those restrictions would imply that correlations can only be evaluated using rank order methods, and statistical significance can only be evaluated using non-parametric methods (R. M. Kothari, 2004). But the restrictions have not been generally endorsed by statisticians. In 1946, Stevens observed that psychological measurement, such as measurement of opinions, usually operates on ordinal scales; thus means and standard deviations have no validity according to his rules, but they can be used to get ideas for how to improve operationalization of variables used in questionnaires. Indeed, most psychological data collected by psychometric instruments and tests, measuring cognitive and other abilities, are ordinal (Cliff, 1996; Cliff & Keats, 2003; Michell, 2008). In particular, IQ scores reflect an ordinal scale, in which all scores are meaningful for comparison only. There is no zero point that represents an absence of intelligence, and a 10-point difference may carry different meanings at different points of the scale. === Interval


id: level-of-measurement-20260505-2
file: data/en.wikipedia.org/wiki/Level_of_measurement-2.md
category: reference
title: Level of measurement
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Level_of_measurement
date_saved: 2026-05-05T06:27:51.709058+00:00
instance: kb-cron
parent: level-of-measurement-20260505-0
summary:
== Debate over Stevens's typology == While Stevens's typology is widely adopted, it is still being challenged by other theoreticians, particularly in the cases of the nominal and ordinal types (Michell, 1986). Duncan (1986), for example, objected to the use of the word measurement in relation to the nominal type and Luce (1997) disagreed with Stevens's definition of measurement. On the other hand, Stevens (1975) said of his own definition of measurement that "the assignment can be any consistent rule. The only rule not allowed would be random assignment, for randomness amounts in effect to a nonrule". Hand says, "Basic psychology texts often begin with Stevens's framework and the ideas are ubiquitous. Indeed, the essential soundness of his hierarchy has been established for representational measurement by mathematicians, determining the invariance properties of mappings from empirical systems to real number continua. Certainly the ideas have been revised, extended, and elaborated, but the remarkable thing is his insight given the relatively limited formal apparatus available to him and how many decades have passed since he coined them." The use of the mean as a measure of the central tendency for the ordinal type is still debatable among those who accept Stevens's typology.


id: level-of-measurement-20260505-3
file: data/en.wikipedia.org/wiki/Level_of_measurement-3.md
category: reference
title: Level of measurement
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Level_of_measurement
date_saved: 2026-05-05T06:27:51.709058+00:00
instance: kb-cron
parent: level-of-measurement-20260505-0
summary:
…any law purporting to express a quantitative relation between sensation intensity and stimulus intensity is not merely false but is in fact meaningless unless and until a meaning can be given to the concept of addition as applied to sensation. That is, if Stevens's sone scale genuinely measured the intensity of auditory sensations, then evidence for such sensations as being quantitative attributes needed to be produced. The evidence needed was the presence of additive structure—a concept comprehensively treated by the German mathematician Otto Hölder (Hölder, 1901). Given that the physicist and measurement theorist Norman Robert Campbell dominated the Ferguson committee's deliberations, the committee concluded that measurement in the social sciences was impossible due to the lack of concatenation operations. This conclusion was later rendered false by the discovery of the theory of conjoint measurement by Debreu (1960) and independently by Luce & Tukey (1964). However, Stevens's reaction was not to conduct experiments to test for the presence of additive structure in sensations, but instead to render the conclusions of the Ferguson committee null and void by proposing a new theory of measurement: Paraphrasing N. R. Campbell (Final Report, p. 340), we may say that measurement, in the broadest sense, is


id: mature-technology-20260505-0
file: data/en.wikipedia.org/wiki/Mature_technology-0.md
category: reference
title: Mature technology
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Mature_technology
date_saved: 2026-05-05T06:27:55.352224+00:00
instance: kb-cron
parent: mature-technology-20260505-0
summary:
A mature technology is a technology that has been in use for long enough that most of its initial faults and inherent problems have been removed or reduced by further development. In some contexts, it may also refer to technology that has not seen widespread use, but whose scientific background is well understood. Its performance characteristics are also expected to be well understood with well-established design specifications. One of the key indicators of a mature technology is the ease of use for both non-experts and professionals. Another indicator is a reduction in the rate of new breakthrough advances related to it—whereas inventions related to a (popular) immature technology are usually rapid and diverse, and may change the whole use paradigm—advances to a mature technology are usually incremental improvements only. == Examples == The QWERTY keyboard design is an example of mature technology because its performance characteristics such as typing speeds and error rates have been established in various describable situations. Additionally, the basic key organization of this technology has remained the same over the last century. Another example is the barcode, a technology that also satisfies all the previously cited indicators. It is widely used since when it was first


id: mertonian-norms-20260505-0
file: data/en.wikipedia.org/wiki/Mertonian_norms-0.md
category: reference
title: Mertonian norms
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Mertonian_norms
date_saved: 2026-05-05T06:27:56.581569+00:00
instance: kb-cron
parent: mertonian-norms-20260505-0
summary:
In 1942, Robert K. Merton described four aspects of science that later came to be called Mertonian norms: "four sets of institutional imperatives taken to comprise the ethos of modern science... communism, universalism, disinterestedness, and organized skepticism". The subsequent portion of his book, The Sociology of Science, elaborated on these principles at "the heart of the Mertonian paradigm—the powerful juxtaposition of the normative structure of science with its institutionally distinctive reward system". == Description and motivation == Merton defines this 'ethos' with reference to Albert Bayet's 1931 work La Morale de la Science, which "abandons description and analysis for homily" as "that affectively toned complex of values and norms which is held to be binding on the man of science". He attempted to clarify it, given that previously it had not been 'codified'; Merton uses Bayet's remark that 'this scientific ethos [morale] does not have its theoreticians, but it has its artisans. It does not express its ideals, but serves them: it is implicated in the very existence of science'. The norms are expressed in the form of prescriptions, proscriptions, preferences, and permissions. They are legitimatized in terms of institutional values. These imperatives, transmitted by precept and example and reenforced


id: mertonian-norms-20260505-1
file: data/en.wikipedia.org/wiki/Mertonian_norms-1.md
category: reference
title: Mertonian norms
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Mertonian_norms
date_saved: 2026-05-05T06:27:56.581569+00:00
instance: kb-cron
parent: mertonian-norms-20260505-0
summary:
Secondly, to restrict scientific careers for any reason other than incompetence was to "prejudice the furtherance of knowledge". Merton again noted how the ethos of science may be inconsistent with that of society, but insists that "however inadequately it may be put into practice, the ethos of democracy includes universalism as a dominant guiding principle". He predicted that this inadequacy of laissez-faire democratic processes would lead ultimately to false differential accumulation and increasing regulation of science under political authority, which must be counteracted through "new technical forms of organization" towards equality of opportunity. === Disinterestedness === Distinct from altruism, scientists should act for the benefit of a common scientific enterprise rather than for specific outcomes. Merton reasoned that an individual's scientific motivation may be easily influenced and without institutional enforcement of disinterestedness, and the "seeming virtual absence of fraud" could not be explained by unusually high moral integrity of individuals alone. Merton observed a low rate of fraud in science ("virtual absence … which appears exceptional"), which he believed stemmed from the intrinsic need for "verifiability" in science and expert scrutiny by peers ("rigorous policing, to a degree perhaps unparalleled in any other field of activity") as well as the


id: list-of-multiple-discoveries-20260505-0
file: data/en.wikipedia.org/wiki/List_of_multiple_discoveries-0.md
category: reference
title: List of multiple discoveries
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/List_of_multiple_discoveries
date_saved: 2026-05-05T06:28:00.437791+00:00
instance: kb-cron
parent: list-of-multiple-discoveries-20260505-0
summary:
Historians and sociologists have remarked the occurrence, in science, of "multiple independent discovery". Robert K. Merton defined such "multiples" as instances in which similar discoveries are made by scientists working independently of each other. "Sometimes", writes Merton, "the discoveries are simultaneous or almost so; sometimes a scientist will make a new discovery which, unknown to him, somebody else has made years before." Commonly cited examples of multiple independent discovery are the 17th-century independent formulation of calculus by Isaac Newton and Gottfried Wilhelm Leibniz; the 18th-century discovery of oxygen by Carl Wilhelm Scheele, Joseph Priestley, Antoine Lavoisier and others; and the theory of the evolution of species, independently advanced in the 19th century by Charles Darwin and Alfred Russel Wallace. Multiple independent discovery, however, is not limited to such famous historic instances. Merton believed that it is multiple discoveries, rather than unique ones, that represent the common pattern in science. Merton contrasted a "multiple" with a "singleton"—a discovery that has been made uniquely by a single scientist or group of scientists working together. The distinction may blur as science becomes increasingly collaborative. A distinction is drawn between a discovery and an invention, as discussed for example by Bolesław Prus. However, discoveries


id: list-of-multiple-discoveries-20260505-1
file: data/en.wikipedia.org/wiki/List_of_multiple_discoveries-1.md
category: reference
title: List of multiple discoveries
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/List_of_multiple_discoveries
date_saved: 2026-05-05T06:28:00.437791+00:00
instance: kb-cron
parent: list-of-multiple-discoveries-20260505-0
summary:
1730: Stirling numbers – James Stirling (1730) and Masanobu Saka (1782). 1740s: Platinum – Antonio de Ulloa and Charles Wood (both in the 1740s). 1745: Leyden Jar – Ewald Georg von Kleist (1745) and Pieter van Musschenbroek (1745–46). 1749: Lightning rod – Benjamin Franklin (1749) and Prokop Diviš (1754) (debated: Diviš's apparatus is assumed to have been more effective than Franklin's lightning rods in 1754, but was intended for a different purpose than lightning protection). 1756: Law of conservation of matter – discovered by Mikhail Lomonosov, 1756; and independently by Antoine Lavoisier, 1778. 1773: Oxygen – Carl Wilhelm Scheele (Uppsala, 1773), Joseph Priestley (Wiltshire, 1774). The term was coined by Antoine Lavoisier (1777). Michael Sendivogius (Polish: Michał Sędziwój; 1566–1636) is claimed as an earlier discoverer of oxygen. 1783: Black-hole theory – John Michell, in a 1783 paper in The Philosophical Transactions of the Royal Society, wrote: "If the semi-diameter of a sphere of the same density as the Sun in the proportion of five hundred to one, and by supposing light to be attracted by the same force in proportion to its [mass] with other bodies, all light emitted from such a body would be made to return towards it,


id: list-of-multiple-discoveries-20260505-2
file: data/en.wikipedia.org/wiki/List_of_multiple_discoveries-2.md
category: reference
title: List of multiple discoveries
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/List_of_multiple_discoveries
date_saved: 2026-05-05T06:28:00.437791+00:00
instance: kb-cron
parent: list-of-multiple-discoveries-20260505-0
summary:
1805: In a treatise written in 1805 and published in 1866, Carl Friedrich Gauss describes an efficient algorithm to compute the discrete Fourier transform. James W. Cooley and John W. Tukey reinvented a similar algorithm in 1965. 1817: Cadmium – Friedrich Strohmeyer, K.S.L Hermann (both in 1817). 1817: Grotthuss–Draper law (aka the Principle of Photochemical Activation) – first proposed in 1817 by Theodor Grotthuss, then independently, in 1842, by John William Draper. The law states that only that light which is absorbed by a system can bring about a photochemical change. 1825: Bromine discovered by Carl Jacob Löwig; and independently, in 1826, by Antoine Jérôme Balard. 1828: Beryllium – Friedrich Wöhler, A.A.B. Bussy (1828). 1830: Non-Euclidean geometry (hyperbolic geometry) – Nikolai Ivanovich Lobachevsky (1830), János Bolyai (1832); preceded by Gauss (unpublished result) c. 1805. 1831: Electromagnetic induction was discovered by Michael Faraday in England in 1831, and independently about the same time by Joseph Henry in the U.S. 1831: Chloroform – Samuel Guthrie in the United States (July 1831), and a few months later Eugène Soubeiran (France) and Justus von Liebig (Germany), all of them using variations of the haloform reaction. Dandelin–Gräffe method, aka Lobachevsky method – an algorithm for


id: list-of-multiple-discoveries-20260505-3
file: data/en.wikipedia.org/wiki/List_of_multiple_discoveries-3.md
category: reference
title: List of multiple discoveries
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/List_of_multiple_discoveries
date_saved: 2026-05-05T06:28:00.437791+00:00
instance: kb-cron
parent: list-of-multiple-discoveries-20260505-0
summary:
E = mc2, though only Einstein provided the accepted interpretation – Henri Poincaré, 1900; Olinto De Pretto, 1903; Albert Einstein, 1905; Paul Langevin, 1906. 1902: Walter Sutton and Theodor Boveri independently proposed that the hereditary information is carried in the chromosomes. 1902: Richard Assmann and Léon Teisserenc de Bort independently discovered the stratosphere. 1904: Epinephrine synthesized independently by Friedrich Stolz and by Henry Drysdale Dakin. 1905: Brownian motion was independently explained by Albert Einstein (in one of his 1905 papers) and by Marian Smoluchowski in 1906. 1905: The Einstein Relation was revealed independently by William Sutherland in 1905, by Albert Einstein in 1905, and by Marian Smoluchowski in 1906. 1905: The chromosomal XY sex-determination system—that males have XY, and females XX, sex chromosomes—was discovered independently by Nettie Stevens, at Bryn Mawr College, and by Edmund Beecher Wilson at Columbia University. 1907: Lutetium discovered independently by French scientist Georges Urbain and by Austrian mineralogist Baron Carl Auer von Welsbach. 1907: Hilbert space representation theorem, also known as Riesz representation theorem, the mathematical justification of the Bra-ket notation in the theory of quantum mechanics – independently proved by Frigyes Riesz and Maurice René Fréchet. 1908: The Hardy–Weinberg principle is a principle of


id: list-of-multiple-discoveries-20260505-4
file: data/en.wikipedia.org/wiki/List_of_multiple_discoveries-4.md
category: reference
title: List of multiple discoveries
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/List_of_multiple_discoveries
date_saved: 2026-05-05T06:28:00.437791+00:00
instance: kb-cron
parent: list-of-multiple-discoveries-20260505-0
summary:
It was originally proved in 1934 by Aleksandr Gelfond, and again independently in 1935 by Theodor Schneider. 1934: The Penrose triangle, also known as the "tribar", is an impossible object. It was first created by the Swedish artist Oscar Reutersvärd in 1934. The mathematician Roger Penrose independently devised and popularized it in the 1950s. 1936: In computer science, the concept of the "universal computing machine" (now generally called the "Turing Machine") was proposed by Alan Turing, but also independently by Emil Post, both in 1936. Similar approaches, also aiming to cover the concept of universal computing, were introduced by S.C. Kleene, Rózsa Péter, and Alonzo Church that same year. Also in 1936, Konrad Zuse tried to build a binary electrically driven mechanical calculator with limited programability; however, Zuse's machine was never fully functional. The later Atanasoff–Berry Computer ("ABC"), designed by John Vincent Atanasoff and Clifford Berry, was the first fully electronic digital computing device; while not programmable, it pioneered important elements of modern computing, including binary arithmetic and electronic switching elements, though its special-purpose nature and lack of a changeable, stored program distinguish it from modern computers. 1938: Benford's law, also known as the Newcomb–Benford law, the law of anomalous


id: list-of-multiple-discoveries-20260505-5
file: data/en.wikipedia.org/wiki/List_of_multiple_discoveries-5.md
category: reference
title: List of multiple discoveries
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/List_of_multiple_discoveries
date_saved: 2026-05-05T06:28:00.437791+00:00
instance: kb-cron
parent: list-of-multiple-discoveries-20260505-0
summary:
1967: The affine scaling method for solving linear programming was discovered by Soviet mathematician I.I. Dikin in 1967. It went unnoticed in the West for two decades, until two groups of researchers in the U.S. reinvented it in 1985. 1968: Neutral theory of molecular evolution was introduced by a Japanese biologist, Motoo Kimura, in 1968, and independently by two American biologists, Jack Lester King and Thomas Hughes Jukes, in 1969. 1969: Thyrotropin-releasing hormone (TRH) structure was determined, and the hormone synthesized, independently by Andrew V. Schally and Roger Guillemin, who shared the 1977 Nobel Prize in Medicine. 1970: Howard Temin and David Baltimore independently discovered reverse transcriptase enzymes. The Knuth–Morris–Pratt string searching algorithm was developed by Donald Knuth and Vaughan Pratt and independently by J. H. Morris. 1971: The Cook–Levin theorem (also known as "Cook's theorem"), a result in computational complexity theory, was proven independently by Stephen Cook (1971 in the U.S.) and by Leonid Levin (1973 in the USSR). Levin was not aware of Cook's achievement because of communication difficulties between East and West during the Cold War. The other way round, Levin's work was not widely known in the West until around 1978. 1972: The Bohlen–Pierce scale, a


id: list-of-multiple-discoveries-20260505-6
file: data/en.wikipedia.org/wiki/List_of_multiple_discoveries-6.md
category: reference
title: List of multiple discoveries
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/List_of_multiple_discoveries
date_saved: 2026-05-05T06:28:00.437791+00:00
instance: kb-cron
parent: list-of-multiple-discoveries-20260505-0
summary:
== 21st century == 2001: four different authors published different implementations of a distributed hash table. The Super Kamiokande and SNOLAB collaborations, whose findings were published in 1998 and 2001 respectively, both proved that neutrinos have mass. As a result, the 2015 Nobel Prize in Physics was shared by Takaaki Kajita of Japan and Arthur B. McDonald of Canada. 1996: James Allison of MD Anderson Cancer Center at the University of Texas at Houston discovered a mechanism enabling cancer immunotherapy. In 2002 Tasuku Honjo of Kyoto University discovered another such mechanism. This outcome, which led to them sharing the 2018 Nobel Prize in Physiology or Medicine, has been described as follows: "Each independently discovered that our immune system is restrained from attacking tumors by molecules that function as 'brakes.' Releasing these brakes (or 'brake receptors') allows our body to powerfully combat cancer." 2014: Paul Erdős' conjecture about prime gaps was proved by Kevin Ford, Ben Green, Sergei Konyagin, and Terence Tao, working together, and independently by James Maynard. 2020: Half of the 2020 Nobel Prize in Physics was awarded to Reinhard Genzel and Andrea Ghez, who each have led a group of astronomers focused since the early 1990s on a


id: nursing-process-20260505-0
file: data/en.wikipedia.org/wiki/Nursing_process-0.md
category: reference
title: Nursing process
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Nursing_process
date_saved: 2026-05-05T06:28:02.904076+00:00
instance: kb-cron
parent: nursing-process-20260505-0
summary:
The nursing process is a modified scientific method that is a fundamental part of nursing practices in many countries around the world. Nursing practice was first described as a four-stage nursing process by Ida Jean Orlando in 1958. It should not be confused with nursing theories or health informatics. The diagnosis phase was added later. The nursing process uses clinical judgement to strike a balance of epistemology between personal interpretation and research evidence in which critical thinking may play a part to categorize the clients issue and course of action. Nursing offers diverse patterns of knowing. Nursing knowledge has embraced pluralism since the 1970s. Evidence based practice (EBP) Evidence based practice is a process that is used in the healthcare field to used as a problem-solving approach to make clinical decisions. This is collected by reviewing, analyzing, and forming the best sources for the patient-care. EBP assist with the nursing process by providing credible information that helps nurses make the knowledgeable choice. Person-centered care The nursing process helps orchestrate the nurses' decisions with the patient’s participation needed for recovery. Nurses utilize person-centered care (PCC), which focuses on identifying and addressing a patient's unique needs and preferences. PCC aligns well with


id: observation-20260505-0
file: data/en.wikipedia.org/wiki/Observation-0.md
category: reference
title: Observation
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Observation
date_saved: 2026-05-05T06:28:04.069734+00:00
instance: kb-cron
parent: observation-20260505-0
summary:
Observation in the natural sciences refers to the active acquisition of information from a primary source. It involves the act of noticing or perceiving phenomena and gathering data based on direct engagement with the subject of study. In living organisms, observation typically occurs through the senses. In science, it often extends beyond unaided perception, involving the use of scientific instruments to detect, measure, and record data. This enables the observation of phenomena not accessible to human senses alone. Observations in science are typically categorized as either qualitative or quantitative: Qualitative observations describe characteristics that are not expressed numerically, such as color, texture, or behavior. Quantitative observations involve numerical measurements, obtained through counting or using instruments to assign values to observed phenomena. The term observation may refer both to the process of observing and to the information recorded as a result of that process. == Science == The scientific method requires observations of natural phenomena to formulate and test hypotheses. The method involves an iterative series of steps intended to generate and refine scientific knowledge: Ask a question about a phenomenon Make observations of the phenomenon Formulate a hypothesis that tentatively answers the question Predict logical, observable consequences of the hypothesis


id: observation-20260505-1
file: data/en.wikipedia.org/wiki/Observation-1.md
category: reference
title: Observation
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Observation
date_saved: 2026-05-05T06:28:04.069734+00:00
instance: kb-cron
parent: observation-20260505-0
summary:
== Biases == Human senses do not function like an impartial recording device such as a video camcorder. Perception occurs through a complex, largely unconscious process of abstraction, in which certain elements of sensory input are selected and retained, while others are discarded. This selection process depends on an internal model of the world—referred to in psychology as a schema—that is shaped by past experiences. Sensory information is interpreted and stored based on this schema. During recall, gaps in memory may be unconsciously filled with information consistent with the schema, a process known as reconstructive memory. The degree of attention given to different aspects of a perceptual experience is influenced by an individual's internal value system, which prioritizes information based on perceived importance. As a result, two individuals observing the same event may remember it differently, potentially disagreeing on factual details. This subjectivity is a known limitation of eyewitness testimony, which research has shown to be frequently unreliable. In scientific practice, rigorous methods are employed to minimize such observational biases. These include careful documentation of experimental data, distinguishing clearly between raw observations and inferred conclusions, and implementing procedures such as blind and double blind experiment designs to control for subjective


id: outline-of-scientific-method-20260505-0
file: data/en.wikipedia.org/wiki/Outline_of_scientific_method-0.md
category: reference
title: Outline of scientific method
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Outline_of_scientific_method
date_saved: 2026-05-05T06:28:09.119370+00:00
instance: kb-cron
parent: outline-of-scientific-method-20260505-0
summary:
The following outline is provided as an overview of and topical guide to the scientific method: Scientific method – body of techniques for investigating phenomena and acquiring new knowledge, as well as for correcting and integrating previous knowledge. It is based on observable, empirical, reproducible, measurable evidence, and subject to the laws of reasoning. == Nature of scientific method == Scientific method Science Philosophy of science Sociology of knowledge Process of science Knowledge == Elements of scientific method == Research Basic research === Observation === Observation Scientific method Causation Investigation Measurement (Outline of metrology and measurement) === Hypothesis === Hypothesis pro:Karl Popper Falsifiability con:Paul Feyerabend Statistical hypothesis testing === Experiment === Experiment Laboratory Laboratory techniques Design of experiments Scientific control Natural experiment Observational study Field experiment Self-experimentation Self-experimentation in medicine Placebo effect === Theory === Scientific theory ==== Prediction ==== Prediction Bayesian inference – subjective use of statistical reasoning Deductive reasoning Retrodiction === Evaluation by scientific community === Peer review Medical peer review == Scientific method concepts == === Empirical methods === Empirical methods Empiricism Robert Grosseteste Peter Parker Empirical validation Operationalization === Use of statistics === Uncomfortable science — Inference from a limited sample of data Exploratory data analysis


id: pasteur-s-quadrant-20260505-0
file: data/en.wikipedia.org/wiki/Pasteur's_quadrant-0.md
category: reference
title: Pasteur's quadrant
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Pasteur's_quadrant
date_saved: 2026-05-05T06:28:10.348654+00:00
instance: kb-cron
parent: pasteur-s-quadrant-20260505-0
summary:
Pasteur's quadrant is a classification of scientific research projects that seek fundamental understanding of scientific problems, while also having immediate use for society. Louis Pasteur's research is thought to exemplify this type of method, which bridges the gap between "basic" and "applied" research. The term was introduced by Donald E. Stokes in his book, Pasteur's Quadrant. == Other quadrants == As shown in the following table, scientific research can be classified by whether it advances human knowledge by seeking a fundamental understanding of nature, or whether it is primarily motivated by the need to solve immediate problems. The result is three distinct classes of research: Pure basic research, exemplified by the work of Niels Bohr, early 20th century atomic physicist. Pure applied research, exemplified by the work of Thomas Edison, inventor. Use-inspired basic research, described here as "Pasteur's Quadrant". == Usage == Pasteur's quadrant is useful in distinguishing various perspectives within science, engineering and technology. For example, Daniel A. Vallero and Trevor M. Letcher in their book Unraveling Environmental Disasters applied the device to disaster preparedness and response. University science programs are concerned with knowledge-building, whereas engineering programs at the same university will apply existing and emerging knowledge to address


id: personoid-20260505-0
file: data/en.wikipedia.org/wiki/Personoid-0.md
category: reference
title: Personoid
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Personoid
date_saved: 2026-05-05T06:28:12.977090+00:00
instance: kb-cron
parent: personoid-20260505-0
summary:
Personoid is the concept coined by Stanisław Lem, a Polish science-fiction writer, in Non Serviam, from his book A Perfect Vacuum (1971). His personoids are an abstraction of functions of human mind and they live in computers; they do not need any human-like physical body. In cognitive and software modeling, personoid is a research approach to the development of intelligent autonomous agents. In frame of the IPK (Information, Preferences, Knowledge) architecture, it is a framework of abstract intelligent agent with a cognitive and structural intelligence. It can be seen as an essence of high intelligent entities. From the philosophical and systemics perspectives, personoid societies can also be seen as the carriers of a culture. According to N. Gessler, the personoids study can be a base for the research on artificial culture and culture evolution. == Personoids on TV and cinema == Welt am Draht (1973) The Thirteenth Floor (1999) == See also == Android Humanoid Intelligence Artificial Intelligence Culture Computer Science Cognitive Science Anticipatory science Memetics == References == Stanisław Lem's book Próżnia Doskonała (1971). The collection of book reviews of nonexistent books. Translated into English by Michael Kandel as A Perfect Vacuum (1983). Personetics. Personoids Organizations Framework: An Approach


id: pilot-experiment-20260505-0
file: data/en.wikipedia.org/wiki/Pilot_experiment-0.md
category: reference
title: Pilot experiment
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Pilot_experiment
date_saved: 2026-05-05T06:28:14.172433+00:00
instance: kb-cron
parent: pilot-experiment-20260505-0
summary:
A pilot experiment, pilot study, pilot test or pilot project is a small-scale preliminary study conducted to evaluate feasibility, duration, cost, adverse events, and improve upon the study design prior to performance of a full-scale research project. == Implementation == Pilot experiments are frequently carried out before large-scale quantitative research, in an attempt to avoid time and money being used on an inadequately designed project. A pilot study is usually carried out on members of the relevant population. A pilot study is used to formulate the design of the full-scale experiment which then can be adjusted. The pilot study is potentially a critical insight to clinical trial design, recruitment and sample size of participants, treatment testing, and statistical analysis to improve the power of testing the hypothesis of the study. Analysis from the pilot experiment can be added to the full-scale (and more expensive) experiment to improve the chances of a clear outcome. == Applications == In sociology, pilot studies can be referred to as small-scale studies that will help identify design issues before the main research is done. Although pilot experiments have a well-established tradition, their usefulness as a strategy for change has been questioned, at least in the


id: prediction-20260505-0
file: data/en.wikipedia.org/wiki/Prediction-0.md
category: reference
title: Prediction
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Prediction
date_saved: 2026-05-05T06:28:19.326686+00:00
instance: kb-cron
parent: prediction-20260505-0
summary:
A prediction (from Latin prae- 'before' and dictum 'something said') or forecast is a statement about a future event or about future data. Predictions are often, but not always, based upon experience or knowledge of forecasters. There is no universal agreement about the exact difference between "prediction" and "estimation"; different authors and disciplines ascribe different connotations. Future events are necessarily uncertain, so guaranteed accurate information about the future is impossible. Prediction can be useful to assist in making plans about possible developments. == Opinion == In a non-statistical sense, the term "prediction" is often used to refer to an informed guess or opinion. A prediction of this kind might be informed by a predicting person's abductive reasoning, inductive reasoning, deductive reasoning, and experience; and may be useful—if the predicting person is a knowledgeable person in the field. The Delphi method is a technique for eliciting such expert-judgement-based predictions in a controlled way. This type of prediction might be perceived as consistent with statistical techniques in the sense that, at minimum, the "data" being used is the predicting expert's cognitive experiences forming an intuitive "probability curve." == Statistics == In statistics, prediction is a part of statistical inference. One particular approach


id: prediction-20260505-1
file: data/en.wikipedia.org/wiki/Prediction-1.md
category: reference
title: Prediction
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Prediction
date_saved: 2026-05-05T06:28:19.326686+00:00
instance: kb-cron
parent: prediction-20260505-0
summary:
In science, a prediction is a rigorous, often quantitative, statement, forecasting what would be observed under specific conditions; for example, according to theories of gravity, if an apple fell from a tree it would be seen to move towards the center of the Earth with a specified and constant acceleration. The scientific method is built on testing statements that are logical consequences of scientific theories. This is done through repeatable experiments or observational studies. A scientific theory whose predictions are contradicted by observations and evidence will be rejected. New theories that generate many new predictions can more easily be supported or falsified (see predictive power). Notions that make no testable predictions are usually considered not to be part of science (protoscience or nescience) until testable predictions can be made. Mathematical equations and models, and computer models, are frequently used to describe the past and future behaviour of a process within the boundaries of that model. In some cases the probability of an outcome, rather than a specific outcome, can be predicted, for example in much of quantum physics. In microprocessors, branch prediction permits avoidance of pipeline emptying at branch instructions. In engineering, possible failure modes are predicted and avoided by


id: prediction-20260505-2
file: data/en.wikipedia.org/wiki/Prediction-2.md
category: reference
title: Prediction
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Prediction
date_saved: 2026-05-05T06:28:19.326686+00:00
instance: kb-cron
parent: prediction-20260505-0
summary:
== Sports == Predicting the outcome of sporting events is a business which has grown in popularity in recent years. Handicappers predict the outcome of games using a variety of mathematical formulas, simulation models or qualitative analysis. Early, well known sports bettors, such as Jimmy the Greek, were believed to have access to information that gave them an edge. Information ranged from personal issues, such as gambling or drinking to undisclosed injuries; anything that may affect the performance of a player on the field. Recent times have changed the way sports are predicted. Predictions now typically consist of two distinct approaches: Situational plays and statistical based models. Situational plays are much more difficult to measure because they usually involve the motivation of a team. Dan Gordon, noted handicapper, wrote "Without an emotional edge in a game in addition to value in a line, I won't put my money on it". These types of plays consist of: Betting on the home underdog, betting against Monday Night winners if they are a favorite next week, betting the underdog in "look ahead" games etc. As situational plays become more widely known they become less useful because they will impact the way the line


id: prediction-20260505-3
file: data/en.wikipedia.org/wiki/Prediction-3.md
category: reference
title: Prediction
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Prediction
date_saved: 2026-05-05T06:28:19.326686+00:00
instance: kb-cron
parent: prediction-20260505-0
summary:
Predictions have often been made, from antiquity until the present, by using paranormal or supernatural means such as prophecy or by observing omens. Methods including water divining, astrology, numerology, fortune telling, interpretation of dreams, and many other forms of divination, have been used for millennia to attempt to predict the future. These means of prediction have not been proven by scientific experiments. In literature, vision and prophecy are literary devices used to present a possible timeline of future events. They can be distinguished by vision referring to what an individual sees happen. The book of Revelation, in the New Testament, thus uses vision as a literary device in this regard. It is also prophecy or prophetic literature when it is related by an individual in a sermon or other public forum. Divination is the attempt to gain insight into a question or situation by way of an occultic standardized process or ritual. It is an integral part of witchcraft and has been used in various forms for thousands of years. Diviners ascertain their interpretations of how a querent should proceed by reading signs, events, or omens, or through alleged contact with a supernatural agency, most often described as an angel


id: preference-test-20260505-0
file: data/en.wikipedia.org/wiki/Preference_test-0.md
category: reference
title: Preference test
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Preference_test
date_saved: 2026-05-05T06:28:20.598911+00:00
instance: kb-cron
parent: preference-test-20260505-0
summary:
A preference test is an experiment in which animals are allowed free access to multiple environments which differ in one or more ways. Various aspects of the animal's behaviour can be measured with respect to the alternative environments, such as latency and frequency of entry, duration of time spent, range of activities observed, or relative consumption of a goal object in the environment. These measures can be recorded either by the experimenter or by motion detecting software. Strength of preference can be inferred by the magnitude of the difference in the response, but see "Advantages and disadvantages" below. Statistical testing is used to determine whether observed differences in such measures support the conclusion that preference or aversion has occurred. Prior to testing, the animals are usually given the opportunity to explore the environments to habituate and reduce the effects of novelty. Preference tests can be used to test for preferences of only one characteristic of an environment, e.g. cage colour, or multiple characteristics e.g. a choice between hamster wheel, Habitrail tunnels or additional empty space for extended locomotion. == Types of test == === Two choices === The simplest of preference tests offers a choice between two alternatives. This can


id: probabilistic-logic-20260505-0
file: data/en.wikipedia.org/wiki/Probabilistic_logic-0.md
category: reference
title: Probabilistic logic
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Probabilistic_logic
date_saved: 2026-05-05T06:28:23.022186+00:00
instance: kb-cron
parent: probabilistic-logic-20260505-0
summary:
Probabilistic logic (also probability logic and probabilistic reasoning) involves the use of probability and logic to deal with uncertain situations. Probabilistic logic extends traditional logic truth tables with probabilistic expressions. A difficulty of probabilistic logics is their tendency to multiply the computational complexities of their probabilistic and logical components. Other difficulties include the possibility of counter-intuitive results, such as in case of belief fusion in Dempster–Shafer theory. Source trust and epistemic uncertainty about the probabilities they provide, such as defined in subjective logic, are additional elements to consider. The need to deal with a broad variety of contexts and issues has led to many different proposals. == Logical background == There are numerous proposals for probabilistic logics. Very roughly, they can be categorized into two different classes: those logics that attempt to make a probabilistic extension to logical entailment, such as Markov logic networks, and those that attempt to address the problems of uncertainty and lack of evidence (evidentiary logics). That the concept of probability can have different meanings may be understood by noting that, despite the mathematization of probability in the Enlightenment, mathematical probability theory remains, to this very day, entirely unused in criminal courtrooms, when evaluating the "probability"


id: probabilistic-logic-20260505-1
file: data/en.wikipedia.org/wiki/Probabilistic_logic-1.md
category: reference
title: Probabilistic logic
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Probabilistic_logic
date_saved: 2026-05-05T06:28:23.022186+00:00
instance: kb-cron
parent: probabilistic-logic-20260505-0
summary:
The term "probabilistic logic" was first used by John von Neumann in a series of Caltech lectures 1952 and 1956 paper "Probabilistic logics and the synthesis of reliable organisms from unreliable components", and subsequently in a paper by Nils Nilsson published in 1986, where the truth values of sentences are probabilities. The proposed semantical generalization induces a probabilistic logical entailment, which reduces to ordinary logical entailment when the probabilities of all sentences are either 0 or 1. This generalization applies to any logical system for which the consistency of a finite set of sentences can be established. Gaifman and Snir have developed a globally consistent and empirically satisfactory unification of classic probability theory and first-order logic that is suitable for inductive reasoning. Their theory assigns probabilities or degrees of beliefs to sentences consistent with the knowledge base (probability 1 for facts and axioms), consistent with the standard (Kolmogorov) probability axioms and logical deduction, and allows (Bayesian) inductive reasoning and learning in the limit. Most importantly, unlike most alternative proposals, it allows confirmation of universally quantified hypotheses. The theory has also been extended to higher-order logic. Both solutions are purely theoretical but have spawned practical approximations. The central concept in the


id: progressive-contextualization-20260505-0
file: data/en.wikipedia.org/wiki/Progressive_contextualization-0.md
category: reference
title: Progressive contextualization
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Progressive_contextualization
date_saved: 2026-05-05T06:28:24.230969+00:00
instance: kb-cron
parent: progressive-contextualization-20260505-0
summary:
Progressive contextualization (PC) is a scientific method pioneered and developed by Andrew P. Vayda and research team between 1979 and 1984. The method was developed to help understand cause of damage and destruction of forest and land during the New Order Regime in Indonesia, as well as practical ethnography. Vayda proposed the Progressive contextualization method due to his dissatisfaction with several conventional anthropological methods to describe accurately and quickly cases of illegal logging, land destruction and the network of actor-investor protecting the actions, as well as various consequences detrimental to the environment and social life. The essence of this method is to track and assess: what the actor (actor-based) or network of certain actors (actor-based network) does in a certain location and time the series of consequences (intended or unintended) that result from what the actors and/or networks do, in a time and space that can be different from the original time and space, as long as it is in accordance with the interest of the research and the available time. Therefore, the PC method does not have to be bound to a certain research place and time pre-determined in the research design. It rejects the assumption of ecological and


id: protoscience-20260505-0
file: data/en.wikipedia.org/wiki/Protoscience-0.md
category: reference
title: Protoscience
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Protoscience
date_saved: 2026-05-05T06:28:25.452441+00:00
instance: kb-cron
parent: protoscience-20260505-0
summary:
In the philosophy of science, protoscience (adj. protoscientific) is a research field that has the characteristics of an undeveloped science that may ultimately develop into an established science. Philosophers use protoscience to understand the history of science and distinguish protoscience from science and pseudoscience. The word "protoscience" is a hybrid Greek-Latin compound of the roots proto- + scientia, meaning a first or primeval rational knowledge. Examples of protoscience include alchemy, Wegener's original theory of continental drift and political economy (the predecessor to the modern economic sciences). == History == Protoscience as a research field with the characteristics of an undeveloped science appeared in the early 20th century. In 1910, Jones described the field of political economy as it began the transition to the modern field of economics: I confess to a personal predilection for some term such as proto-science, pre-science, or nas-science, to give expression to what I conceive to be the true state of affairs, which I take to be this, that economics and kindred subjects are not sciences, but are on the way to become sciences. Thomas Kuhn later provided a more precise description, protoscience as a field that generates testable conclusions, faces "incessant criticism and continually strive


id: protoscience-20260505-1
file: data/en.wikipedia.org/wiki/Protoscience-1.md
category: reference
title: Protoscience
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Protoscience
date_saved: 2026-05-05T06:28:25.452441+00:00
instance: kb-cron
parent: protoscience-20260505-0
summary:
=== Science field === A science field is a research field that satisfies 12 conditions: 1) all components of the science field invariably change over time from research in the field, especially logical/mathematical tools and specific background/presuppositions from other fields; 2) the research community has special training, "hold strong information links", initiates or continues the "tradition of inquiry"; 3) researchers have autonomy to pursue research and receive support from the host society; 4) the researchers worldview is the real world as contains "lawfully changing concrete" objects, an adequate view of the scientific method, a vision of organized science achieving truthfull descriptions and explanations, ethical principles for conducting research, and the free search for truthful, deep and systematic understanding; 5) up-to-date logical/mathematical tools precisely determine and process information; 6) the domain of research are real objects/entities; 7) specific background knowledge is up-to-date, confirmed data, hypotheses and theories from relevant neighboring fields; 8) the set of problems investigated are from the domain of inquiry or within the research field; 9) the accumulated knowledge includes worldview-compatible, up-to-date testworthy/testable theories, hypotheses and data, and special knowledge previously accumlated in the research field; 10) the aims are find and apply laws and theories in the


id: quietism-philosophy-20260505-0
file: data/en.wikipedia.org/wiki/Quietism_(philosophy)-0.md
category: reference
title: Quietism (philosophy)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Quietism_(philosophy)
date_saved: 2026-05-05T06:28:30.409738+00:00
instance: kb-cron
parent: quietism-philosophy-20260505-0
summary:
Quietism in philosophy sees the role of philosophy as broadly therapeutic or remedial. Quietist philosophers believe that philosophy has no positive thesis to contribute; rather, it defuses confusions in the linguistic and conceptual frameworks of other subjects, including non-quietist philosophy. For quietists, advancing knowledge or settling debates (particularly those between realists and non-realists) is not the job of philosophy, rather philosophy should liberate the mind by diagnosing confusing concepts. == Status within philosophy == Crispin Wright said that "Quietism is the view that significant metaphysical debate is impossible." It has been described as "the view or stance that entails avoidance of substantive philosophical theorizing and is usually associated with certain forms of skepticism, pragmatism, and minimalism about truth. More particularly, it is opposed to putting forth positive theses and developing constructive arguments." Quietism by its nature is not a philosophical school as understood in the sense of a systematic body of truths. The objective of quietism is to show that philosophical positions or theories cannot solve problems, settle debates or advance knowledge. It is often raised in discussion as an opposite position to both philosophical realism and anti-realism. Specifically, quietists deny that there is any substantial debate between the positions


id: replicate-biology-20260505-0
file: data/en.wikipedia.org/wiki/Replicate_(biology)-0.md
category: reference
title: Replicate (biology)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Replicate_(biology)
date_saved: 2026-05-05T06:28:31.643457+00:00
instance: kb-cron
parent: replicate-biology-20260505-0
summary:
In the biological sciences, replicates are an experimental units that are treated identically. Replicates are an essential component of experimental design because they provide an estimate of between sample error. Without replicates, scientists are unable to assess whether observed treatment effects are due to the experimental manipulation or due to random error. There are also analytical replicates which is when an exact copy of a sample is analyzed, such as a cell, organism or molecule, using exactly the same procedure. This is done in order to check for analytical error. In the absence of this type of error replicates should yield the same result. However, analytical replicates are not independent and cannot be used in tests of the hypothesis because they are still the same sample. == See also == Self-replication Fold change == References ==


id: replication-crisis-20260505-0
file: data/en.wikipedia.org/wiki/Replication_crisis-0.md
category: reference
title: Replication crisis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Replication_crisis
date_saved: 2026-05-05T06:28:33.005921+00:00
instance: kb-cron
parent: replication-crisis-20260505-0
summary:
The replication crisis, also known as the reproducibility or replicability crisis, refers to widespread failures to reproduce published scientific results. Because the reproducibility of empirical results is the cornerstone of the scientific method, such failures undermine the credibility of theories and challenge substantial parts of scientific knowledge. Psychology and medicine have been focal points for replication efforts, with researchers systematically reexamining classic studies to verify their reliability and, when failures emerge, to identify the underlying causes. Data strongly indicates that other natural and social sciences are also affected. The phrase "replication crisis" was coined in the early 2010s as part of a growing awareness of the problem. Considerations of causes and remedies have given rise to a new scientific discipline known as metascience, which uses methods of empirical research to examine empirical research practice. Researchers distinguish two forms of reproducibility. Reproducibility in a narrow sense refers to reexamining and validating the analysis of a set of data. The second category, replication, involves repeating an experiment or study with new, independent data to verify the original conclusions. == Background == === Replication === Replication has been called "the cornerstone of science". Environmental health scientist Stefan Schmidt began a 2009 review with


id: replication-crisis-20260505-1
file: data/en.wikipedia.org/wiki/Replication_crisis-1.md
category: reference
title: Replication crisis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Replication_crisis
date_saved: 2026-05-05T06:28:33.005921+00:00
instance: kb-cron
parent: replication-crisis-20260505-0
summary:
A null hypothesis test is a decision procedure which takes in some data, and outputs either H 0 {\displaystyle H_{0}} or H 1 {\displaystyle H_{1}} . If it outputs H 1 {\displaystyle H_{1}} , it is usually stated as "there is a statistically significant effect" or "the null hypothesis is rejected". Often, the statistical test is a (one-sided) threshold test, which is structured as follows: Gather data D {\displaystyle D} . Compute a test statistic t [ D ] {\displaystyle t[D]} for the data. Compare the test statistic against a critical value/threshold t threshold {\displaystyle t_{\text{threshold}}} . If t [ D ] > t threshold {\displaystyle t[D]>t_{\text{threshold}}} , then output H 1 {\displaystyle H_{1}} , else, output H 0 {\displaystyle H_{0}} . A two-sided threshold test is similar, but with two thresholds, such that it outputs H 1 {\displaystyle H_{1}} if either t [ D ] < t threshold − {\displaystyle t[D]<t_{\text{threshold}}^{-}} or t [ D ] > t threshold + {\displaystyle t[D]>t_{\text{threshold}}^{+}} There are 4 possible outcomes of a null hypothesis test: false negative, true negative, false positive, true positive. A false negative means that H 0 {\displaystyle H_{0}} is true, but the test outcome is H 1 {\displaystyle


id: replication-crisis-20260505-2
file: data/en.wikipedia.org/wiki/Replication_crisis-2.md
category: reference
title: Replication crisis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Replication_crisis
date_saved: 2026-05-05T06:28:33.005921+00:00
instance: kb-cron
parent: replication-crisis-20260505-0
summary:
Social priming failures: In the early 2010s, two direct replication attempts failed to reproduce results from social psychologist John Bargh's much-cited "elderly-walking" study (originally published in 1996). This experiment was part of a series of three studies that had been widely cited throughout the years, was regularly taught in university courses, and had inspired many conceptual replications. These replication failures triggered intense disagreement between replication researchers and the original authors. Notably, many of the conceptual replications of the original studies also failed to replicate in subsequent direct replications. Experiments on extrasensory perception: Social psychologist Daryl Bem conducted a series of experiments supposedly providing evidence for the controversial phenomenon of extrasensory perception. Bem faced substantial criticism of his study's methodology. Reanalysis of his data found no evidence for extrasensory perception. The experiment also failed to replicate in subsequent direct replications. According to Romero, what the community found particularly upsetting was that many of the flawed procedures and statistical tools used in Bem's studies were part of common research practice in psychology. Biomedical replication failures: Scientists from biotech companies Amgen and Bayer Healthcare reported alarmingly low replication rates (11–20%) of landmark findings in preclinical oncological research. P-hacking studies and questionable research practices:


id: replication-crisis-20260505-3
file: data/en.wikipedia.org/wiki/Replication_crisis-3.md
category: reference
title: Replication crisis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Replication_crisis
date_saved: 2026-05-05T06:28:33.005921+00:00
instance: kb-cron
parent: replication-crisis-20260505-0
summary:
=== In psychology === Several factors have combined to put psychology at the center of the conversation. Some areas of psychology once considered solid, such as social priming and ego depletion, have come under increased scrutiny due to failed replications. Much of the focus has been on social psychology, although other areas of psychology such as clinical psychology, developmental psychology, and educational research have also been implicated. In August 2015, the first open empirical study of reproducibility in psychology was published, called The Reproducibility Project: Psychology. Coordinated by psychologist Brian Nosek, researchers redid 100 studies in psychological science from three high-ranking psychology journals (Journal of Personality and Social Psychology, Journal of Experimental Psychology: Learning, Memory, and Cognition, and Psychological Science). Of 97 original studies with significant effects, 36% replicated successfully (p value below 0.05), with effect sizes averaging half the original magnitude. Among non-replications, 25% directly contradicted the original while 49% were inconclusive due to underpowered designs. The same paper examined the reproducibility rates and effect sizes by journal and discipline. Study replication rates were 23% for the Journal of Personality and Social Psychology, 48% for Journal of Experimental Psychology: Learning, Memory, and Cognition, and 38% for Psychological Science. Studies


id: replication-crisis-20260505-4
file: data/en.wikipedia.org/wiki/Replication_crisis-4.md
category: reference
title: Replication crisis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Replication_crisis
date_saved: 2026-05-05T06:28:33.005921+00:00
instance: kb-cron
parent: replication-crisis-20260505-0
summary:
=== Across fields === A 2016 survey by Nature on 1,576 researchers who took a brief online questionnaire on reproducibility found that more than 70% of researchers have tried and failed to reproduce another scientist's experiment results (including 87% of chemists, 77% of biologists, 69% of physicists and engineers, 67% of medical researchers, 64% of earth and environmental scientists, and 62% of all others), and more than half have failed to reproduce their own experiments. But fewer than 20% had been contacted by another researcher unable to reproduce their work. The survey found that fewer than 31% of researchers believe that failure to reproduce results means that the original result is probably wrong, although 52% agree that a significant replication crisis exists. Most researchers said they still trust the published literature. In 2010, Fanelli (2010) found that 91.5% of psychiatry/psychology studies confirmed the effects they were looking for, and concluded that the odds of this happening (a positive result) was around five times higher than in fields such as astronomy or geosciences. Fanelli argued that this is because researchers in "softer" sciences have fewer constraints to their conscious and unconscious biases. Early analysis of result-blind peer review, which is less


id: replication-crisis-20260505-5
file: data/en.wikipedia.org/wiki/Replication_crisis-5.md
category: reference
title: Replication crisis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Replication_crisis
date_saved: 2026-05-05T06:28:33.005921+00:00
instance: kb-cron
parent: replication-crisis-20260505-0
summary:
Academic "publish or perish" culture exacerbates publication bias. Intense pressure to publish in recognized journals, driven by hypercompetitive environments and bibliometric career evaluations, incentivizes researchers to prioritize publishable results over validity. According to Fanelli, this pushes scientists to employ a number of strategies aimed at making results "publishable". In the context of publication bias, this can mean adopting behaviors aimed at making results positive or statistically significant, often at the expense of their validity. According to Center for Open Science founder Brian Nosek and his colleagues, "publish or perish" culture created a situation whereby the goals and values of single scientists (e.g., publishability) are not aligned with the general goals of science (e.g., pursuing scientific truth). This is detrimental to the validity of published findings. Philosopher Brian D. Earp and psychologist Jim A. C. Everett argue that, although replication is in the best interests of academics and researchers as a group, features of academic psychological culture discourage replication by individual researchers. They argue that performing replications can be time-consuming, and take away resources from projects that reflect the researcher's original thinking. They are harder to publish, largely because they are unoriginal, and even when they can be published they are


id: replication-crisis-20260505-6
file: data/en.wikipedia.org/wiki/Replication_crisis-6.md
category: reference
title: Replication crisis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Replication_crisis
date_saved: 2026-05-05T06:28:33.005921+00:00
instance: kb-cron
parent: replication-crisis-20260505-0
summary:
==== In medicine ==== Irreproducible medical studies commonly share these characteristics: investigators not being blinded to the experimental versus the control arms; failure to repeat experiments; lack of positive and negative controls; failing to report all the data; inappropriate use of statistical tests; and use of reagents that were not appropriately validated. ==== In AI research ==== In machine learning research, a range of questionable practices have emerged due to intense pressure to achieve state-of-the-art benchmark results. Common evaluation questionable practices include "benchmark overfitting" by repeatedly tuning hyperparameters on held-out test sets, selectively reporting the best of multiple random seeds or experimental runs, and "metric hacking" through unreported post hoc decisions such as choice of tokenization or evaluation scripts that inflate scores. Researchers also frequently cherry-pick tasks or datasets where their proposed model outperforms baselines, omit negative or lower-performing results, and obscure compute budgets to present energy-intensive methods as efficient. Such practices can give a misleading impression of model progress and hinder fair comparisons across methods. Moreover, many machine learning studies suffer from irreproducible research practices that impede replication and auditing. Key issues include incomplete disclosure of training details—such as dataset preprocessing, exact model hyperparameters, random seeds, and hardware configurations—and


id: replication-crisis-20260505-7
file: data/en.wikipedia.org/wiki/Replication_crisis-7.md
category: reference
title: Replication crisis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Replication_crisis
date_saved: 2026-05-05T06:28:33.005921+00:00
instance: kb-cron
parent: replication-crisis-20260505-0
summary:
=== Statistical issues === ==== Low statistical power ==== Low statistical power hinders replication for three reasons: (1) low-power replications have reduced ability to detect true effects, (2) low-power original studies produce biased effect size estimates leading to undersized replications, and (3) low-power original studies yield results unlikely to reflect true effects. Mathematically, the probability of replicating a previous publication that rejected a null hypothesis H 0 {\displaystyle H_{0}} in favor of an alternative H 1 {\displaystyle H_{1}} is ( significance ) P r ( H 0 | publication ) + ( power ) P r ( H 1 | publication ) ≤ ( power ) {\displaystyle ({\text{significance}})Pr(H_{0}|{\text{publication}})+({\text{power}})Pr(H_{1}|{\text{publication}})\leq ({\text{power}})} assuming significance is less than power. Thus, low power implies low probability of replication, regardless of how the previous publication was designed, and regardless of which hypothesis is really true. Stanley and colleagues estimated the average statistical power of psychological literature by analyzing data from 200 meta-analyses. They found that on average, psychology studies have between 33.1% and 36.4% statistical power. These values are quite low compared to the 80% considered adequate statistical power for an experiment. Across the 200 meta-analyses, the median of studies with adequate statistical power was between


id: replication-crisis-20260505-8
file: data/en.wikipedia.org/wiki/Replication_crisis-8.md
category: reference
title: Replication crisis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Replication_crisis
date_saved: 2026-05-05T06:28:33.005921+00:00
instance: kb-cron
parent: replication-crisis-20260505-0
summary:
Various statistical methods can be applied to make the p-value appear smaller than it really is. This need not be malicious, as moderately flexible data analysis, routine in research, can increase the false-positive rate to above 60%. For example, if one collects some data, applies several different significance tests to it, and publishes only the one that happens to have a p-value less than 0.05, then the total p-value for "at least one significance test reaches p < 0.05" can be much larger than 0.05, because even if the null hypothesis were true, the probability that one out of many significance tests is extreme is not itself extreme. Typically, a statistical study has multiple steps, with several choices at each step, such as during data collection, outlier rejection, choice of test statistic, choice of one-tailed or two-tailed test, etc. These choices in the "garden of forking paths" multiply, creating many "researcher degrees of freedom". The effect is similar to the file-drawer problem, as the paths not taken are not published. Consider a simple illustration. Suppose the null hypothesis is true, and we have 20 possible significance tests to apply to the dataset. Also suppose the outcomes to the significance tests


id: replication-crisis-20260505-9
file: data/en.wikipedia.org/wiki/Replication_crisis-9.md
category: reference
title: Replication crisis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Replication_crisis
date_saved: 2026-05-05T06:28:33.005921+00:00
instance: kb-cron
parent: replication-crisis-20260505-0
summary:
==== Statistical heterogeneity ==== As also reported by Stanley and colleagues, a further reason studies might fail to replicate is high heterogeneity of the to-be-replicated effects. Heterogeneity (variance in research findings due to multiple true effect sizes rather than one) is measured by the I-squared statistic, which quantifies unexplained variation in effect sizes across studies. This variation can be due to differences in experimental methods, populations, cohorts, and statistical methods between replication studies. Heterogeneity poses a challenge to studies attempting to replicate previously found effect sizes. When heterogeneity is high, subsequent replications have a high probability of finding an effect size radically different than that of the original study. Importantly, significant levels of heterogeneity are also found in direct/exact replications of a study. Stanley and colleagues discuss this while reporting a study by quantitative behavioral scientist Richard Klein and colleagues, where the authors attempted to replicate 15 psychological effects across 36 different sites in Europe and the U.S. In the study, Klein and colleagues found significant amounts of heterogeneity in 8 out of 16 effects (I-squared = 23% to 91%). Importantly, while the replication sites intentionally differed on a variety of characteristics, such differences could account for very little heterogeneity


id: replication-crisis-20260505-10
file: data/en.wikipedia.org/wiki/Replication_crisis-10.md
category: reference
title: Replication crisis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Replication_crisis
date_saved: 2026-05-05T06:28:33.005921+00:00
instance: kb-cron
parent: replication-crisis-20260505-0
summary:
and the probability of replicating the statistical study is P r ( replication | find H 1 ) = P r ( find H 1 | H 1 ) P r ( H 1 | find H 1 ) + P r ( find H 1 | H 0 ) P r ( H 0 | find H 1 ) {\displaystyle Pr({\text{replication}}|{\text{ find }}H_{1})=Pr({\text{find }}H_{1}|H_{1})Pr(H_{1}|{\text{ find }}H_{1})+Pr({\text{find }}H_{1}|H_{0})Pr(H_{0}|{\text{ find }}H_{1})} which is also different from P r ( H 1 | find H 1 ) {\displaystyle Pr(H_{1}|{\text{ find }}H_{1})} . In particular, for a fixed level of significance, the probability of replication increases with power, and prior probability for H 1 {\displaystyle H_{1}} . If the prior probability for H 1 {\displaystyle H_{1}} is small, then one would require a high power for replication. For example, if the prior probability of the null hypothesis is P r ( H 0 ) = 0.9 {\displaystyle Pr(H_{0})=0.9} , and the study found a positive result, then the posterior probability for H 1 {\displaystyle H_{1}} is P r ( H 1 | find H 1 ) = 0.50 {\displaystyle Pr(H_{1}|{\text{ find }}H_{1})=0.50} , and the replication probability is P r ( replication |


id: replication-crisis-20260505-11
file: data/en.wikipedia.org/wiki/Replication_crisis-11.md
category: reference
title: Replication crisis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Replication_crisis
date_saved: 2026-05-05T06:28:33.005921+00:00
instance: kb-cron
parent: replication-crisis-20260505-0
summary:
== Consequences == When effects are wrongly stated as relevant in the literature, failure to detect this by replication will lead to the canonization of such false facts. A 2021 study found that papers in leading general interest, psychology and economics journals with findings that could not be replicated tend to be cited more over time than reproducible research papers, likely because these results are surprising or interesting. The trend is not affected by publication of failed reproductions, after which only 12% of papers that cite the original research will mention the failed replication. Further, experts are able to predict which studies will be replicable, leading the authors of the 2021 study, Marta Serra-Garcia and Uri Gneezy, to conclude that experts apply lower standards to interesting results when deciding whether to publish them. === Public awareness and perceptions === Concerns have been expressed within the scientific community that the general public may consider science less credible due to failed replications. Research supporting this concern is sparse, but a nationally representative survey in Germany showed that more than 75% of Germans have not heard of replication failures in science. The study also found that most Germans have positive perceptions of replication


id: replication-crisis-20260505-12
file: data/en.wikipedia.org/wiki/Replication_crisis-12.md
category: reference
title: Replication crisis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Replication_crisis
date_saved: 2026-05-05T06:28:33.005921+00:00
instance: kb-cron
parent: replication-crisis-20260505-0
summary:
In response to concerns in psychology about publication bias and data dredging, more than 140 psychology journals have adopted result-blind peer review. In this approach, studies are accepted not on the basis of their findings and after the studies are completed, but before they are conducted and on the basis of the methodological rigor of their experimental designs, and the theoretical justifications for their statistical analysis techniques before data collection or analysis is done. Early analysis of this procedure has estimated that 61% of result-blind studies have led to null results, in contrast to an estimated 5% to 20% in earlier research. In addition, large-scale collaborations between researchers working in multiple labs in different countries that regularly make their data openly available for different researchers to assess have become much more common in psychology. ==== Pre-registration of studies ==== Scientific publishing has begun using pre-registration reports to address the replication crisis. The registered report format requires authors to submit a description of the study methods and analyses prior to data collection. Once the method and analysis plan is vetted through peer-review, publication of the findings is provisionally guaranteed, based on whether the authors follow the proposed protocol. One goal of


id: replication-crisis-20260505-13
file: data/en.wikipedia.org/wiki/Replication_crisis-13.md
category: reference
title: Replication crisis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Replication_crisis
date_saved: 2026-05-05T06:28:33.005921+00:00
instance: kb-cron
parent: replication-crisis-20260505-0
summary:
==== Cross-validation ==== One common statistical problem is overfitting, that is, when researchers fit a regression model over a large number of variables but a small number of data points. For example, a typical fMRI study of emotion, personality, and social cognition has fewer than 100 subjects, but each subject has 10,000 voxels. The study would fit a sparse linear regression model that uses the voxels to predict a variable of interest, such as self-reported stress. But the study would then report on the p-value of the model on the same data it was fitted to. The standard approach in statistics, where data is split into a training and a validation set, is resisted because test subjects are expensive to acquire. One possible solution is cross-validation, which allows model validation while also allowing the whole dataset to be used for model-fitting. === Replication efforts === ==== Funding ==== In July 2016, the Netherlands Organisation for Scientific Research made €3 million available for replication studies. The funding is for replication based on reanalysis of existing data and replication by collecting and analysing new data. Funding is available in the areas of social sciences, health research and healthcare innovation. In 2013, the


id: replication-crisis-20260505-14
file: data/en.wikipedia.org/wiki/Replication_crisis-14.md
category: reference
title: Replication crisis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Replication_crisis
date_saved: 2026-05-05T06:28:33.005921+00:00
instance: kb-cron
parent: replication-crisis-20260505-0
summary:
The dominant scientific and statistical model of causation is the linear model. The linear model assumes that mental variables are stable properties which are independent of each other. In other words, these variables are not expected to influence each other. Instead, the model assumes that the variables will have an independent, linear effect on observable outcomes. Social scientists Sebastian Wallot and Damian Kelty-Stephen argue that the linear model is not always appropriate. An alternative is the complex system model which assumes that mental variables are interdependent. These variables are not assumed to be stable, rather they will interact and adapt to each specific context. They argue that the complex system model is often more appropriate in psychology, and that the use of the linear model when the complex system model is more appropriate will result in failed replications. ...psychology may be hoping for replications in the very measurements and under the very conditions where a growing body of psychological evidence explicitly discourages predicting replication. Failures to replicate may be plainly baked into the potentially incomplete, but broadly sweeping failure of human behavior to conform to the standard of independen[ce] ... ==== Replication should seek to revise theories ==== Replication is


id: reproducibility-20260505-0
file: data/en.wikipedia.org/wiki/Reproducibility-0.md
category: reference
title: Reproducibility
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Reproducibility
date_saved: 2026-05-05T06:28:34.237806+00:00
instance: kb-cron
parent: reproducibility-20260505-0
summary:
Reproducibility, closely related to replicability and repeatability, is a major principle underpinning the scientific method. For the findings of a study to be reproducible means that results obtained by an experiment or an observational study or in a statistical analysis of a data set should be achieved again with a high degree of reliability when the study is replicated. There are different kinds of replication but typically replication studies involve different researchers using the same methodology. Only after one or several such successful replications should a result be recognized as scientific knowledge. == History == The first to stress the importance of reproducibility in science was the Anglo-Irish chemist Robert Boyle, in England in the 17th century. Boyle's air pump was designed to generate and study vacuum, which at the time was a very controversial concept. Indeed, distinguished philosophers such as René Descartes and Thomas Hobbes denied the very possibility of vacuum existence. Historians of science Steven Shapin and Simon Schaffer, in their 1985 book Leviathan and the Air-Pump, describe the debate between Boyle and Hobbes, ostensibly over the nature of vacuum, as fundamentally an argument about how useful knowledge should be gained. Boyle, a pioneer of the experimental method,


id: reproducibility-20260505-1
file: data/en.wikipedia.org/wiki/Reproducibility-1.md
category: reference
title: Reproducibility
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Reproducibility
date_saved: 2026-05-05T06:28:34.237806+00:00
instance: kb-cron
parent: reproducibility-20260505-0
summary:
=== Reproducible research method === The term reproducible research refers to the idea that scientific results should be documented in such a way that their deduction is fully transparent. This requires a detailed description of the methods used to obtain the data and making the full dataset and the code to calculate the results easily accessible. This is the essential part of open science. To make any research project computationally reproducible, general practice involves all data and files being clearly separated, labelled, and documented. All operations should be fully documented and automated as much as practicable, avoiding manual intervention where feasible. The workflow should be designed as a sequence of smaller steps that are combined so that the intermediate outputs from one step directly feed as inputs into the next step. Version control should be used as it lets the history of the project be easily reviewed and allows for the documenting and tracking of changes in a transparent manner. A basic workflow for reproducible research involves data acquisition, data processing and data analysis. Data acquisition primarily consists of obtaining primary data from a primary source such as surveys, field observations, experimental research, or obtaining data from an existing source.


id: reproducibility-20260505-2
file: data/en.wikipedia.org/wiki/Reproducibility-2.md
category: reference
title: Reproducibility
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Reproducibility
date_saved: 2026-05-05T06:28:34.237806+00:00
instance: kb-cron
parent: reproducibility-20260505-0
summary:
== Noteworthy irreproducible results == Hideyo Noguchi became famous for correctly identifying the bacterial agent of syphilis, but also claimed that he could culture this agent in his laboratory. Nobody else has been able to produce this latter result. In March 1989, University of Utah chemists Stanley Pons and Martin Fleischmann reported the production of excess heat that could only be explained by a nuclear process ("cold fusion"). The report was astounding given the simplicity of the equipment: it was essentially an electrolysis cell containing heavy water and a palladium cathode which rapidly absorbed the deuterium produced during electrolysis. The news media reported on the experiments widely, and it was a front-page item on many newspapers around the world (see science by press conference). Over the next several months others tried to replicate the experiment, but were unsuccessful. Nikola Tesla claimed as early as 1899 to have used a high frequency current to light gas-filled lamps from over 25 miles (40 km) away without using wires. In 1904 he built Wardenclyffe Tower on Long Island to demonstrate means to send and receive power without connecting wires. The facility was never fully operational and was not completed due to economic problems,


id: reproducibility-project-20260505-0
file: data/en.wikipedia.org/wiki/Reproducibility_Project-0.md
category: reference
title: Reproducibility Project
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Reproducibility_Project
date_saved: 2026-05-05T06:28:35.507520+00:00
instance: kb-cron
parent: reproducibility-project-20260505-0
summary:
The Reproducibility Project is a series of crowdsourced collaborations aiming to reproduce published scientific studies, finding high rates of results which could not be replicated. It has resulted in two major initiatives focusing on the fields of psychology and cancer biology. The project has brought attention to the replication crisis, and has contributed to shifts in scientific culture and publishing practices to address it. The project was led by the Center for Open Science and its co-founder, Brian Nosek, who started the project in November 2011. == Results == Brian Nosek of University of Virginia and colleagues sought out to replicate 100 different studies, all published in 2008. The project pulled these studies from three different journals, Psychological Science, the Journal of Personality and Social Psychology, and the Journal of Experimental Psychology: Learning, Memory, and Cognition, published in 2008 to see if they could get the same results as the initial findings. 97 of the original studies had significant effects, but of those 97, only 36% of the replications yielded significant findings (p value below 0.05), and the effects were often smaller than those in the original papers. The authors emphasized that the findings reflect a problem that affects all


id: research-20260505-0
file: data/en.wikipedia.org/wiki/Research-0.md
category: reference
title: Research
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Research
date_saved: 2026-05-05T06:28:36.820676+00:00
instance: kb-cron
parent: research-20260505-0
summary:
Research is creative and systematic work undertaken to increase the stock of knowledge. It involves the collection, organization, and analysis of evidence to increase understanding of a topic, characterized by a particular attentiveness to controlling sources of bias and error. These activities are characterized by accounting and controlling for biases. A research project may be an expansion of past work in the field. To test the validity of instruments, procedures, or experiments, research may replicate elements of prior projects or the project as a whole. The primary purposes of basic research (as opposed to applied research) are documentation, discovery, interpretation, and the research and development (R&D) of methods and systems for the advancement of human knowledge. Approaches to research depend on epistemologies, which vary considerably both within and between humanities and sciences. There are several forms of research: scientific, humanities, artistic, economic, social, business, marketing, practitioner research, life, technological, etc. The scientific study of research practices is known as meta-research. A researcher is a person who conducts research. == Etymology == The word research is derived from the Middle French "recherche", which means "to go about seeking", the term itself being derived from the Old French term "recerchier", a compound


id: research-20260505-1
file: data/en.wikipedia.org/wiki/Research-1.md
category: reference
title: Research
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Research
date_saved: 2026-05-05T06:28:36.820676+00:00
instance: kb-cron
parent: research-20260505-0
summary:
=== Artistic research === Artistic research, also seen as 'practice-based research', can take form when creative works are considered both the research and the object of research itself. It is the debatable body of thought which offers an alternative to purely scientific methods in research in its search for knowledge and truth. The controversial trend of artistic teaching becoming more academics-oriented is leading to artistic research being accepted as the primary mode of enquiry in art as in the case of other disciplines. One of the characteristics of artistic research is that it must accept subjectivity as opposed to the classical scientific methods. As such, it is similar to the social sciences in using qualitative research and intersubjectivity as tools to apply measurement and critical analysis. Artistic research has been defined by the School of Dance and Circus (Dans och Cirkushögskolan, DOCH), Stockholm in the following manner – "Artistic research is to investigate and test with the purpose of gaining knowledge within and for our artistic disciplines. It is based on artistic practices, methods, and criticality. Through presented documentation, the insights gained shall be placed in a context." Artistic research aims to enhance knowledge and understanding with presentation of the


id: research-20260505-2
file: data/en.wikipedia.org/wiki/Research-2.md
category: reference
title: Research
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Research
date_saved: 2026-05-05T06:28:36.820676+00:00
instance: kb-cron
parent: research-20260505-0
summary:
Identification of research problem Literature review Specifying the purpose of research Determining specific research questions Specification of a conceptual framework, sometimes including a set of hypotheses Choice of a methodology (for data collection) Data collection Verifying data Analyzing and interpreting the data Reporting and evaluating research Communicating the research findings and, possibly, recommendations The steps generally represent the overall process; however, they should be viewed as an ever-changing iterative process rather than a fixed set of steps. Most research begins with a general statement of the problem, or rather, the purpose for engaging in the study. The literature review identifies flaws or holes in previous research which provides justification for the study. Often, a literature review is conducted in a given subject area before a research question is identified. A gap in the current literature, as identified by a researcher, then engenders a research question. The research question may be parallel to the hypothesis. The hypothesis is the supposition to be tested. The researcher(s) collects data to test the hypothesis. The researcher(s) then analyzes and interprets the data via a variety of statistical methods, engaging in what is known as empirical research. The results of the data analysis in rejecting


id: research-20260505-3
file: data/en.wikipedia.org/wiki/Research-3.md
category: reference
title: Research
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Research
date_saved: 2026-05-05T06:28:36.820676+00:00
instance: kb-cron
parent: research-20260505-0
summary:
Exploratory research, which helps to identify and define a problem or question. Constructive research, which tests theories and proposes solutions to a problem or question. Empirical research, which tests the feasibility of a solution using empirical evidence. There are two major types of empirical research design: qualitative research and quantitative research. Researchers choose qualitative or quantitative methods according to the nature of the research topic they want to investigate and the research questions they aim to answer: Qualitative research Qualitative research refers to much more subjective non-quantitative, use different methods of collecting data, analyzing data, interpreting data for meanings, definitions, characteristics, symbols metaphors of things. Qualitative research further classified into the following types: Ethnography: This research mainly focus on culture of group of people which includes share attributes, language, practices, structure, value, norms and material things, evaluate human lifestyle. Ethno: people, Grapho: to write, this disciple may include ethnic groups, ethno genesis, composition, resettlement and social welfare characteristics. Phenomenology: It is very powerful strategy for demonstrating methodology to health professions education as well as best suited for exploring challenging problems in health professions educations. In addition, PMP researcher Mandy Sha argued that a project management approach is necessary to control


id: research-20260505-4
file: data/en.wikipedia.org/wiki/Research-4.md
category: reference
title: Research
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Research
date_saved: 2026-05-05T06:28:36.820676+00:00
instance: kb-cron
parent: research-20260505-0
summary:
In many disciplines, Western methods of conducting research are predominant. Researchers are overwhelmingly taught Western methods of data collection and study. The increasing participation of indigenous peoples as researchers has brought increased attention to the scientific lacuna in culturally sensitive methods of data collection. Western methods of data collection may not be the most accurate or relevant for research on non-Western societies. For example, "Hua Oranga" was created as a criterion for psychological evaluation in Māori populations, and is based on dimensions of mental health important to the Māori people – "taha wairua (the spiritual dimension), taha hinengaro (the mental dimension), taha tinana (the physical dimension), and taha whanau (the family dimension)". Even though Western dominance seems to be prominent in research, some scholars, such as Simon Marginson, argue for "the need [for] a plural university world". Marginson argues that the East Asian Confucian model could take over the Western model. This could be due to changes in funding for research both in the East and the West. Focused on emphasizing educational achievement, East Asian cultures, mainly in China and South Korea, have encouraged the increase of funding for research expansion. In contrast, in the Western academic world, notably in


id: research-20260505-5
file: data/en.wikipedia.org/wiki/Research-5.md
category: reference
title: Research
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Research
date_saved: 2026-05-05T06:28:36.820676+00:00
instance: kb-cron
parent: research-20260505-0
summary:
Academic publishing is a system that is necessary for academic scholars to peer review the work and make it available for a wider audience. The system varies widely by field and is also always changing, if often slowly. Most academic work is published in journal article or book form. There is also a large body of research that exists in either a thesis or dissertation form. These forms of research can be found in databases explicitly for theses and dissertations. In publishing, STM publishing is an abbreviation for academic publications in science, technology, and medicine. Most established academic fields have their own scientific journals and other outlets for publication, though many academic journals are somewhat interdisciplinary, and publish work from several distinct fields or subfields. The kinds of publications that are accepted as contributions of knowledge or research vary greatly between fields, from the print to the electronic format. A study suggests that researchers should not give great consideration to findings that are not replicated frequently. It has also been suggested that all published studies should be subjected to some measure for assessing the validity or reliability of its procedures to prevent the publication of unproven findings. Business models are


id: research-ethics-20260505-0
file: data/en.wikipedia.org/wiki/Research_ethics-0.md
category: reference
title: Research ethics
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Research_ethics
date_saved: 2026-05-05T06:28:38.120390+00:00
instance: kb-cron
parent: research-ethics-20260505-0
summary:
Research ethics is a discipline within the study of applied ethics. Its scope ranges from general scientific integrity and misconduct to the treatment of human and animal subjects. The social responsibilities of scientists and researchers are not traditionally included and are less well defined. The discipline is most developed in medical research. Beyond the issues of falsification, fabrication, and plagiarism that arise in every scientific field, research design in human subject research and animal testing are the areas that raise ethical questions most often. The list of historic cases includes many large-scale violations and crimes against humanity such as Nazi human experimentation and the Tuskegee syphilis experiment which led to international codes of research ethics. No approach has been universally accepted, but typically cited codes are the 1947 Nuremberg Code, the 1964 Declaration of Helsinki, and the 1978 Belmont Report. Today, research ethics committees, such as those of the US, UK, and EU, govern and oversee the responsible conduct of research. One major goal being to reduce questionable research practices. Research in other fields such as social sciences, information technology, biotechnology, or engineering may generate ethical concerns. == History == The list of historic cases includes many large scale violations


id: retrodiction-20260505-0
file: data/en.wikipedia.org/wiki/Retrodiction-0.md
category: reference
title: Retrodiction
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Retrodiction
date_saved: 2026-05-05T06:28:40.816082+00:00
instance: kb-cron
parent: retrodiction-20260505-0
summary:
Retrodiction is the act of making a prediction about the past. It is also known as postdiction (but this should not be confused with the use of the term in criticisms of parapsychological research). == Activity == The activity of retrodiction (or postdiction) involves moving backwards in time, step-by-step, in as many stages as are considered necessary, from the present into the speculated past to establish the ultimate cause of a specific event (for instance, in the case of reverse engineering, forensics, etc.). Given that retrodiction is a process in which "past observations, events and data are used as evidence to infer the process(es) that produced them" and that diagnosis "involve[s] going from visible effects such as symptoms, signs and the like to their prior causes", the essential balance between prediction and retrodiction could be characterized as: retrodiction : diagnosis :: prediction : prognosis regardless of whether the prognosis is of the course of the disease in the absence of treatment, or of the application of a specific treatment regimen to a specific disorder in a particular patient: "We consider diagnostic inference to be based on causal thinking, although in doing diagnosis one has to mentally reverse the time order


id: role-of-chance-in-scientific-discoveries-20260505-0
file: data/en.wikipedia.org/wiki/Role_of_chance_in_scientific_discoveries-0.md
category: reference
title: Role of chance in scientific discoveries
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Role_of_chance_in_scientific_discoveries
date_saved: 2026-05-05T06:28:42.044739+00:00
instance: kb-cron
parent: role-of-chance-in-scientific-discoveries-20260505-0
summary:
The role of chance, or "luck", in science comprises all ways in which unexpected discoveries are made. Many domains, especially psychology, are concerned with the way science interacts with chance –particularly "serendipity" (accidents that, through sagacity, are transformed into opportunity). Psychologist Kevin Dunbar and colleagues estimate that between 30% and 50% of all scientific discoveries are accidental in some sense (see examples below). Scientists themselves in the 19th and 20th century acknowledged the role of fortunate luck or serendipity in discoveries. Psychologist Alan A. Baumeister says a scientist must be "sagacious" (attentive and clever) to benefit from an accident. Dunbar quotes Louis Pasteur's saying that "Chance favors only the prepared mind". The prepared mind, Dunbar suggests, is one trained for observational rigor. Dunbar adds that there is a great deal of writing about the role that serendipity ("happy accidents") plays in the scientific method. Research suggests that scientists are taught various heuristics and practices that allow their investigations to benefit, and not suffer, from accidents. First, careful control conditions allow scientists to properly identify something as "unexpected". Once a finding is recognized as legitimately unexpected and in need of explaining, researchers can attempt to explain it: They work across various


id: sampling-statistics-20260505-0
file: data/en.wikipedia.org/wiki/Sampling_(statistics)-0.md
category: reference
title: Sampling (statistics)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Sampling_(statistics)
date_saved: 2026-05-05T06:28:44.602997+00:00
instance: kb-cron
parent: sampling-statistics-20260505-0
summary:
In statistics, quality assurance, and survey methodology, sampling is the selection of a subset of individuals from within a statistical population to estimate characteristics of the whole population. The subset, called a statistical sample (or sample, for short), is meant to reflect the whole population, and statisticians attempt to collect samples that are representative of the population. Sampling has lower costs and faster data collection compared to a census recording data from the entire population (in many cases, collecting the whole population is impossible, like getting sizes of all stars in the universe). Thus, it can provide insights in cases where it is infeasible to measure an entire population. Each observation measures one or more properties (such as weight, location, colour or mass) of independent objects or individuals. In survey sampling, weights can be applied to the data to adjust for the sample design, particularly in stratified sampling. Results from probability theory and statistical theory are employed to guide the practice. In business and medical research, sampling is widely used for gathering information about a population. Acceptance sampling is used to determine if a production lot of material meets the governing specifications. == History == Random sampling by using lots


id: sampling-statistics-20260505-1
file: data/en.wikipedia.org/wiki/Sampling_(statistics)-1.md
category: reference
title: Sampling (statistics)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Sampling_(statistics)
date_saved: 2026-05-05T06:28:44.602997+00:00
instance: kb-cron
parent: sampling-statistics-20260505-0
summary:
In the most straightforward case, such as the sampling of a batch of material from production (acceptance sampling by lots), it would be most desirable to identify and measure every single item in the population and to include any one of them in our sample. However, in the more general case this is not usually possible or practical. There is no way to identify all rats in the set of all rats. Where voting is not compulsory, there is no way to identify which people will vote at a forthcoming election (in advance of the election). These imprecise populations are not amenable to sampling in any of the ways below and to which we could apply statistical theory. As a remedy, we seek a sampling frame which has the property that we can identify every single element and include any in our sample. The most straightforward type of frame is a list of elements of the population (preferably the entire population) with appropriate contact information. For example, in an opinion poll, possible sampling frames include an electoral register and a telephone directory. A probability sample is a sample in which every unit in the population has a chance (greater than


id: sampling-statistics-20260505-2
file: data/en.wikipedia.org/wiki/Sampling_(statistics)-2.md
category: reference
title: Sampling (statistics)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Sampling_(statistics)
date_saved: 2026-05-05T06:28:44.602997+00:00
instance: kb-cron
parent: sampling-statistics-20260505-0
summary:
In a simple random sample (SRS) of a given size, all subsets of a sampling frame have an equal probability of being selected. Each element of the frame thus has an equal probability of selection: the frame is not subdivided or partitioned. Furthermore, any given pair of elements has the same chance of selection as any other such pair (and similarly for triples, and so on). This minimizes bias and simplifies analysis of results. In particular, the variance between individual results within the sample is a good indicator of variance in the overall population, which makes it relatively easy to estimate the accuracy of results. Simple random sampling can be vulnerable to sampling error because the randomness of the selection may result in a sample that does not reflect the makeup of the population. For instance, a simple random sample of ten people from a given country will on average produce five men and five women, but any given trial is likely to overrepresent one sex and underrepresent the other. Systematic and stratified techniques attempt to overcome this problem by "using information about the population" to choose a more "representative" sample. Also, simple random sampling can be cumbersome and tedious


id: sampling-statistics-20260505-3
file: data/en.wikipedia.org/wiki/Sampling_(statistics)-3.md
category: reference
title: Sampling (statistics)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Sampling_(statistics)
date_saved: 2026-05-05T06:28:44.602997+00:00
instance: kb-cron
parent: sampling-statistics-20260505-0
summary:
When the population embraces a number of distinct categories, the frame can be organized by these categories into separate "strata." Each stratum is then sampled as an independent sub-population, out of which individual elements can be randomly selected. The ratio of the size of this random selection (or sample) to the size of the population is called a sampling fraction. There are several potential benefits to stratified sampling. First, dividing the population into distinct, independent strata can enable researchers to draw inferences about specific subgroups that may be lost in a more generalized random sample. Second, utilizing a stratified sampling method can lead to more efficient statistical estimates (provided that strata are selected based upon relevance to the criterion in question, instead of availability of the samples). Even if a stratified sampling approach does not lead to increased statistical efficiency, such a tactic will not result in less efficiency than would simple random sampling, provided that each stratum is proportional to the group's size in the population. Third, it is sometimes the case that data are more readily available for individual, pre-existing strata within a population than for the overall population; in such cases, using a stratified sampling approach may


id: sampling-statistics-20260505-4
file: data/en.wikipedia.org/wiki/Sampling_(statistics)-4.md
category: reference
title: Sampling (statistics)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Sampling_(statistics)
date_saved: 2026-05-05T06:28:44.602997+00:00
instance: kb-cron
parent: sampling-statistics-20260505-0
summary:
Example: Suppose we have six schools with populations of 150, 180, 200, 220, 260, and 490 students respectively (total 1500 students), and we want to use student population as the basis for a PPS sample of size three. To do this, we could allocate the first school numbers 1 to 150, the second school 151 to 330 (= 150 + 180), the third school 331 to 530, and so on to the last school (1011 to 1500). We then generate a random start between 1 and 500 (equal to 1500/3) and count through the school populations by multiples of 500. If our random start was 137, we would select the schools which have been allocated numbers 137, 637, and 1137, i.e. the first, fourth, and sixth schools. The PPS approach can improve accuracy for a given sample size by concentrating sample on large elements that have the greatest impact on population estimates. PPS sampling is commonly used for surveys of businesses, where element size varies greatly and auxiliary information is often available – for instance, a survey attempting to measure the number of guest-nights spent in hotels might use each hotel's number of rooms as an auxiliary variable. In some


id: sampling-statistics-20260505-5
file: data/en.wikipedia.org/wiki/Sampling_(statistics)-5.md
category: reference
title: Sampling (statistics)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Sampling_(statistics)
date_saved: 2026-05-05T06:28:44.602997+00:00
instance: kb-cron
parent: sampling-statistics-20260505-0
summary:
Accidental sampling (sometimes known as grab, convenience, or opportunity sampling) is a type of nonprobability sampling which involves the sample being drawn from that part of the population which is close to hand. That is, a population is selected because it is readily available and convenient. It may be through meeting the person or including a person in the sample when one meets them or chosen by finding them through technological means such as the internet or through phone. The researcher using such a sample cannot scientifically make generalizations about the total population from this sample because it would not be representative enough. For example, if the interviewer were to conduct such a survey at a shopping center early in the morning on a given day, the people that they could interview would be limited to those given there at that given time, which would not represent the views of other members of society in such an area, if the survey were to be conducted at different times of day and several times per week. This type of sampling is most useful for pilot testing. Several important considerations for researchers using convenience samples include: Are there controls within the research


id: sampling-statistics-20260505-6
file: data/en.wikipedia.org/wiki/Sampling_(statistics)-6.md
category: reference
title: Sampling (statistics)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Sampling_(statistics)
date_saved: 2026-05-05T06:28:44.602997+00:00
instance: kb-cron
parent: sampling-statistics-20260505-0
summary:
Sampling schemes may be without replacement ('WOR' – no element can be selected more than once in the same sample) or with replacement ('WR' – an element may appear multiple times in the one sample). For example, if we catch fish, measure them, and immediately return them to the water before continuing with the sample, this is a WR design, because we might end up catching and measuring the same fish more than once. However, if we do not return the fish to the water or tag and release each fish after catching it, this becomes a WOR design. == Sample size determination == Formulas, tables, and power function charts are well known approaches to determine sample size. Steps for using sample size tables: Postulate the effect size of interest, α, and β. Check sample size table Select the table corresponding to the selected α Locate the row corresponding to the desired power Locate the column corresponding to the estimated effect size. The intersection of the column and row is the minimum sample size required. == Sampling and data collection == Good data collection involves: Following the defined sampling process Keeping the data in time order Noting comments and other


id: sampling-statistics-20260505-7
file: data/en.wikipedia.org/wiki/Sampling_(statistics)-7.md
category: reference
title: Sampling (statistics)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Sampling_(statistics)
date_saved: 2026-05-05T06:28:44.602997+00:00
instance: kb-cron
parent: sampling-statistics-20260505-0
summary:
Robert Groves, et alia. Survey methodology (2010 2nd ed. [2004]) ISBN 0-471-48348-6. The other books focus on the statistical theory of survey sampling and require some knowledge of basic statistics, as discussed in the following textbooks: David S. Moore and George P. McCabe (February 2005). "Introduction to the practice of statistics" (5th edition). W.H. Freeman & Company. ISBN 0-7167-6282-X. Freedman, David; Pisani, Robert; Purves, Roger (2007). Statistics (4th ed.). New York: Norton. ISBN 978-0-393-92972-0. The elementary book by Scheaffer et alia uses quadratic equations from high-school algebra: Scheaffer, Richard L., William Mendenhal and R. Lyman Ott. Elementary survey sampling, Fifth Edition. Belmont: Duxbury Press, 1996. More mathematical statistics is required for Lohr, for Särndal et alia, and for Cochran: Cochran, William G. (1977). Sampling techniques (Third ed.). Wiley. ISBN 978-0-471-16240-7. Lohr, Sharon L. (1999). Sampling: Design and analysis. Duxbury. ISBN 978-0-534-35361-2. Särndal, Carl-Erik; Swensson, Bengt; Wretman, Jan (1992). Model assisted survey sampling. Springer-Verlag. ISBN 978-0-387-40620-6. The historically important books by Deming and Kish remain valuable for insights for social scientists (particularly about the U.S. census and the Institute for Social Research at the University of Michigan): Deming, W. Edwards (1966). Some Theory of Sampling. Dover Publications. ISBN 978-0-486-64684-8. OCLC 166526.


id: science-of-team-science-20260505-0
file: data/en.wikipedia.org/wiki/Science_of_team_science-0.md
category: reference
title: Science of team science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Science_of_team_science
date_saved: 2026-05-05T06:28:45.890352+00:00
instance: kb-cron
parent: science-of-team-science-20260505-0
summary:
Science of Team Science (SciTS) is a field of methodology that focuses on understanding and improving cross-disciplinary collaboration in research. The field encompasses conceptual and methodological strategies to understand how teams of scientific researchers can be organized to work more effectively. SciTS initiatives involve understanding and managing factors that affect collaborative science and assessing its outcomes. == History == Since the 1990s, interest and large-scale funding for team-based research initiatives has increased, driven by efforts to tackle complex problems through cross-disciplinary collaboration. Some argue that this trend reflects the growing recognition that addressing multifaceted challenges, such as climate change and public health issues, benefit from partnerships among scientists and practitioners from diverse fields. One SciTS literature review highlighted team science as essential to interprofessional collaborative research. The report called for its integration into health professions education and clinical practice at the University of Minnesota's National Center for Interprofessional Practice and Education. The interdisciplinary nature of SciTS initially emerged from concerns raised by funding agencies, which sought to assess the performance of team science, understand its added value, evaluate the return on investment in large research initiatives, and inform science policy. The term "science of team science" was first introduced in


id: scientific-control-20260505-0
file: data/en.wikipedia.org/wiki/Scientific_control-0.md
category: reference
title: Scientific control
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_control
date_saved: 2026-05-05T06:28:49.805409+00:00
instance: kb-cron
parent: scientific-control-20260505-0
summary:
A scientific control is an element of an experiment or observation designed to minimize the influence of variables other than the independent variable under investigation, thereby reducing the risk of confounding. The use of controls increases the reliability and validity of results by providing a baseline for comparison between experimental measurements and control measurements. In many designs, the control group does not receive the experimental treatment, allowing researchers to isolate the effect of the independent variable. Scientific controls are a fundamental part of the scientific method, particularly in fields such as biology, chemistry, medicine, and psychology, where complex systems are subject to multiple interacting variables. == Controlled experiments == Controls eliminate alternate explanations of experimental results, especially experimental errors and experimenter bias. Many controls are specific to the type of experiment being performed, as in the molecular markers used in SDS-PAGE experiments, and may simply have the purpose of ensuring that the equipment is working properly. The selection and use of proper controls to ensure that experimental results are valid (for example, absence of confounding variables) can be very difficult. Control measurements may also be used for other purposes: for example, a measurement of a microphone's background noise in the


id: scientific-control-20260505-1
file: data/en.wikipedia.org/wiki/Scientific_control-1.md
category: reference
title: Scientific control
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_control
date_saved: 2026-05-05T06:28:49.805409+00:00
instance: kb-cron
parent: scientific-control-20260505-0
summary:
Negative controls are variables that meant to help when the study design is suspected to be invalid because of unmeasured confounders that are correlated with both the treatment and the outcome. Where there are only two possible outcomes, e.g. positive or negative, if the treatment group and the negative control (non-treatment group) both produce a negative result, it can be inferred that the treatment had no effect. If the treatment group and the negative control both produce a positive result, it can be inferred that a confounding variable is involved in the phenomenon under study, and the positive results are not solely due to the treatment. In other examples, outcomes might be measured as lengths, times, percentages, and so forth. In the drug testing example, we could measure the percentage of patients cured. In this case, the treatment is inferred to have no effect when the treatment group and the negative control produce the same results. Some improvement is expected in the placebo group due to the placebo effect, and this result sets the baseline upon which the treatment must improve upon. Even if the treatment group shows improvement, it needs to be compared to the placebo group. If the


id: scientific-control-20260505-2
file: data/en.wikipedia.org/wiki/Scientific_control-2.md
category: reference
title: Scientific control
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_control
date_saved: 2026-05-05T06:28:49.805409+00:00
instance: kb-cron
parent: scientific-control-20260505-0
summary:
Y ~ ⊥ A | U , X {\displaystyle {\tilde {Y}}\perp A|\;U,X} : There is no causal effect of A {\displaystyle A} on Y ~ {\displaystyle {\tilde {Y}}} given X {\displaystyle X} and U {\displaystyle U} . The NCO is independent of the treatment given X {\displaystyle X} and U {\displaystyle U} . U-Comparability: Y ~ ⧸ ⊥ U | X {\displaystyle {\tilde {Y}}\not {\perp }U|\;X} The unmeasured confounders U {\displaystyle U} of the association between A {\displaystyle A} and Y {\displaystyle Y} are the same for the association between A {\displaystyle A} and Y ~ {\displaystyle {\tilde {Y}}} . Given assumption 1 - 4, a non-null association between A {\displaystyle A} and Y ~ {\displaystyle {\tilde {Y}}} , can be explained by U {\displaystyle U} , and not by another mechanism. A possible violation of Latent Exchangeability will be when only the people that are influenced by a medicine will take it, even if both X {\displaystyle X} and U {\displaystyle U} are the same. For example, we would expect that given age and medical history ( X {\displaystyle X} ), general health awareness ( U {\displaystyle U} ), the intake of A {\displaystyle A} influenza vaccine will be


id: scientific-control-20260505-3
file: data/en.wikipedia.org/wiki/Scientific_control-3.md
category: reference
title: Scientific control
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_control
date_saved: 2026-05-05T06:28:49.805409+00:00
instance: kb-cron
parent: scientific-control-20260505-0
summary:
Blinding is the practice of withholding information that may bias an experiment. For example, participants may not know who received an active treatment and who received a placebo. If this information were to become available to trial participants, patients could receive a larger placebo effect, researchers could influence the experiment to meet their expectations (the observer effect), and evaluators could be subject to confirmation bias. A blind can be imposed on any participant of an experiment, including subjects, researchers, technicians, data analysts, and evaluators. In some cases, sham surgery may be necessary to achieve blinding. During the course of an experiment, a participant becomes unblinded if they deduce or otherwise obtain information that has been masked to them. Unblinding that occurs before the conclusion of a study is a source of experimental error, as the bias that was eliminated by blinding is re-introduced. Unblinding is common in blind experiments and must be measured and reported. Meta-research has revealed high levels of unblinding in pharmacological trials. In particular, antidepressant trials are poorly blinded. Reporting guidelines recommend that all studies assess and report unblinding. In practice, very few studies assess unblinding. Blinding is an important tool of the scientific method, and is


id: scientific-dissent-20260505-0
file: data/en.wikipedia.org/wiki/Scientific_dissent-0.md
category: reference
title: Scientific dissent
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_dissent
date_saved: 2026-05-05T06:28:51.054740+00:00
instance: kb-cron
parent: scientific-dissent-20260505-0
summary:
Scientific dissent is dissent from scientific consensus. Disagreements can be useful for finding problems in underlying assumptions, methodologies, and reasoning, as well as for generating and testing new ways of tackling the unknown. In modern times, with the increased role of science on the society and the politicization of science, a new aspect gained prominence: effects of scientific dissent on public policies. Scientific dissent is distinct from denialism, which is a deliberate rejection of scientific consensus usually for commercial or ideological reasons. == Dissent as part of scientific inquiry == Miriam Solomon in her book Social Empiricism argues that scientific dissent is the normal state of scientific inquiry, rather than a conflict that needs resolution. She argues that disagreements between individual scientists about the proper direction of research are not cause for concern, because scientific rationality must be assessed at the level of the scientific community. As long as all theories being pursued yield some unique empirical successes, Solomon argues that their pursuit is worthwhile and consistent with the common view that science aims at truth. In Solomon's view, competing scientific theories can even be inconsistent with one another while each containing some degree of truth. Empirical evidence may not


id: scientific-formalism-20260505-0
file: data/en.wikipedia.org/wiki/Scientific_formalism-0.md
category: reference
title: Scientific formalism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_formalism
date_saved: 2026-05-05T06:28:54.614525+00:00
instance: kb-cron
parent: scientific-formalism-20260505-0
summary:
Scientific formalism is a family of approaches to the presentation of science. It is viewed as an important part of the scientific method, especially in the physical sciences. == Levels of formalism == There are multiple levels of scientific formalism possible. At the lowest level, scientific formalism deals with the symbolic manner in which the information is presented. To achieve formalism in a scientific theory at this level, one starts with a well defined set of axioms, and from this follows a formal system. However, at a higher level, scientific formalism also involves consideration of the axioms themselves. These can be viewed as questions of ontology. For example, one can, at the lower level of formalism, define a property called 'existence'. However, at the higher level, the question of whether an electron exists in the same sense that a bacterium exists still needs to be resolved. Some actual formal theories on facts have been proposed. == In modern physics == The scientific climate of the twentieth century revived these questions. From about the time of Isaac Newton to that of James Clerk Maxwell they had been dormant, in the sense that the physical sciences could rely on the status of


id: scientific-integrity-20260505-0
file: data/en.wikipedia.org/wiki/Scientific_integrity-0.md
category: reference
title: Scientific integrity
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_integrity
date_saved: 2026-05-05T06:28:57.218427+00:00
instance: kb-cron
parent: scientific-integrity-20260505-0
summary:
Research integrity or scientific integrity is an aspect of research ethics that deals with best practice or rules of professional practice of scientists. First introduced in the 19th century by Charles Babbage, the concept of research integrity came to the fore in the late 1970s. A series of publicized scandals in the United States led to heightened debate on the ethical norms of sciences and the limitations of the self-regulation processes implemented by scientific communities and institutions. Formalized definitions of scientific misconduct, and codes of conduct, became the main policy response after 1990. In the 21st century, codes of conduct or ethics codes for research integrity are widespread. Along with codes of conduct at institutional and national levels, major international texts include the European Charter for Researchers (2005), the Singapore Statement on Research Integrity (2010), the European Code of Conduct for Research Integrity (2011 & 2017), and the Hong Kong Principles for assessing researchers (2020). Scientific literature on research integrity falls mostly into two categories: first, mapping of the definitions and categories, especially in regard to scientific misconduct, and second, empirical surveys of the attitudes and practices of scientists. Following the development of codes of conduct, taxonomies of non-ethical uses


id: scientific-integrity-20260505-1
file: data/en.wikipedia.org/wiki/Scientific_integrity-1.md
category: reference
title: Scientific integrity
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_integrity
date_saved: 2026-05-05T06:28:57.218427+00:00
instance: kb-cron
parent: scientific-integrity-20260505-0
summary:
=== Emergence of the issue === Before the 1970s, ethical issues were largely focused on the conduct of medical experiments, especially in regard to tests on humans. In 1803, the "code" of Thomas Percival created a moral foundation for experimental treatments that "was built upon fairly regularly" throughout the next two centuries, notably by Walter Reed in 1898 and by the Berlin code in 1900. After the Second World War, the Nazi human experimentations motivated the development of international, widely acknowledged codes of research ethics, such as the Nuremberg code (1947) and the World Medical Association Declaration of Helsinki. According to Kenneth Pimple, Charles Babbage was the first author to set aside the specific issue of scientific integrity. In the Reflections on the Decline of Science in England, and on Some of its Causes, first published in 1830, Babbage identified four classes of scientific frauds, from outright forgery to varied degrees of arrangements and cooking of the data or the methods. Research integrity became a major debated topic in biological sciences after 1970, due to a combination of factors: the development of advanced data analysis methods, the growing commercial relevancy of fundamental research, and the increased focus of federal funding


id: scientific-integrity-20260505-2
file: data/en.wikipedia.org/wiki/Scientific_integrity-2.md
category: reference
title: Scientific integrity
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_integrity
date_saved: 2026-05-05T06:28:57.218427+00:00
instance: kb-cron
parent: scientific-integrity-20260505-0
summary:
== Taxonomy and classification == In codes of conduct, the definition of research integrity is usually negative: the collection of norms aims to single out different forms of unethical research and scientific misconduct with varying degrees of gravity. The multiplication of codes of conduct has also corresponded with an expansion of scope. While the initial debate was focused on "three deadly sins of scientific and scholarly research: fabrication, falsification and plagiarism", attention has later shifted "to the lesser breaches of research integrity". In 1830, Charles Babbage introduced the first taxonomy of scientific frauds that already encover some forms of questionable research practices : hoaxing (a voluntary fraud "far from justifiable"), forging ("whereas the forger is one who, wishing to acquire a reputation for science, records observations which he has never made"), trimming (which "consists in clipping off little bits here and there from those observations which differ most in excess from the mean" and cooking. Cooking is the main focus of Babbage as an "art of various forms, the object of which is to give to ordinary observations the appearance and character of those of the highest degree of accuracy". It falls done under several sub-cases such as data selection


id: scientific-integrity-20260505-3
file: data/en.wikipedia.org/wiki/Scientific_integrity-3.md
category: reference
title: Scientific integrity
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_integrity
date_saved: 2026-05-05T06:28:57.218427+00:00
instance: kb-cron
parent: scientific-integrity-20260505-0
summary:
=== Implementation and assessment of codes of conduct === Several case studies and retrospective analyses have been devoted to the reception of codes of conduct in scientific communities. They frequently highlight a discrepancy between the theoretical norms and the "lived morality of researchers". In 2004, Caroline Whitbeck underlined that the enforcement of a few formal rules has overall failed to answer to a structural "erosion or neglect" of scientific trust. In 2009, Schuurbiers, Osseweijer and Kinderler led a series of interviews in the aftermath of the Dutch code of conduct on research integrity, introduced in 2005. Overall, most respondents were unaware of the code and complementary ethical recommendations. While the principles "were seen to reflect the norms and values within science rather well", they seemed to be isolated from the actual work practices, which "may lead to morally complex situations". Respondents were also critical of the underlying individualist philosophy of the code, which shifted the entire blame to individual researchers without taking into account institutional or community-wide issues. In 2015, a survey of "64 faculty members at a large southwestern university" in the United States "yielded similar results": many of the respondents were not aware of the existing ethical guidelines,


id: scientific-integrity-20260505-4
file: data/en.wikipedia.org/wiki/Scientific_integrity-4.md
category: reference
title: Scientific integrity
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_integrity
date_saved: 2026-05-05T06:28:57.218427+00:00
instance: kb-cron
parent: scientific-integrity-20260505-0
summary:
In 2018, Heidi Laine attempted to establish a nearly-exhaustive list of "ethical principles associated with open science": This categorization has to contend with the diversity of approaches and values associated with the open science movement and their ongoing evolutions, as the "term will likely remain as fluid as any other attempt to coin a complex system of practices, values and ideologies in one term". Laine identified a significant variation in the way open science principles have been embedded in four major codes of conduct and statements on research integrity: the Singapore Statement on Research Integrity (2010), the Montreal Statement on Research Integrity in Cross-Boundary Research Collaborations (2013), the Responsible Conduct of Research and Procedures for Handling Allegations of Misconduct in Finland (2012) and the European Code of Conduct for Research Integrity (2017). Access to research publications is recommended in all four codes. Integrations of data sharing and reproducibility practices are less obvious, and vary from a tacit approval to detailed support, in the case of the later European Code of Conduct: "The European code pays data management almost an equal amount of attention as publishing and is also in this sense the most advanced of the four CoCs." Yet, important


id: scientific-integrity-20260505-5
file: data/en.wikipedia.org/wiki/Scientific_integrity-5.md
category: reference
title: Scientific integrity
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_integrity
date_saved: 2026-05-05T06:28:57.218427+00:00
instance: kb-cron
parent: scientific-integrity-20260505-0
summary:
In a statement made by the US Department of Health and Human Services (HHS), they adopted the definition of Scientific Integrity as stated below. This policy is currently being reviewed and will be officially published in early 2024." Scientific integrity is the adherence to professional practices, ethical behavior, and the principles of honesty and objectivity when conducting, managing, using the results of, and communicating about science and scientific activities. Inclusivity, transparency, and protection from inappropriate influence are hallmarks of scientific integrity."-HHS To promote a culture of scientific integrity at HHS, they have outlined their policy in seven specific areas: Protecting Scientific Processes Ensuring the Free Flow of Scientific Information Supporting Policymaking Processes Ensuring Accountability Protecting Scientists Professional Development for Government Scientists Federal Advisory Committees As a result of these areas, open science practices can be promoted to protect against bias, plagiarism, and data fabrication, falsification as well as inappropriate influencing, political interference, and censorship. ===== National Institute of Health ===== The National Institute of Health (NIH) is a branch of the HHS. They act as the nation's medical research agency which focuses on making important discoveries that improve health and save lives. The mission of NIH is to provide a


id: scientific-integrity-20260505-6
file: data/en.wikipedia.org/wiki/Scientific_integrity-6.md
category: reference
title: Scientific integrity
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_integrity
date_saved: 2026-05-05T06:28:57.218427+00:00
instance: kb-cron
parent: scientific-integrity-20260505-0
summary:
=== Journal articles === Woolf, Patricia K. (1988). "Deception in Scientific Research". Jurimetrics. 29 (1): 67–95. ISSN 0897-1277. JSTOR 29762108. PMID 11654908. Partha, Dasgupta; David, Paul A. (1994-09-01). "Toward a new economics of science". Research Policy. Special Issue in Honor of Nathan Rosenberg. 23 (5): 487–521. doi:10.1016/0048-7333(94)01002-1. ISSN 0048-7333. Pimple, Kenneth D. (2002-06-01). "Six domains of research ethics". Science and Engineering Ethics. 8 (2): 191–205. doi:10.1007/s11948-002-0018-1. ISSN 1471-5546. PMID 12092490. S2CID 25084326. Whitbeck, Caroline (2004). "Trust and the Future of Research". Physics Today. 57 (11): 48–53. Bibcode:2004PhT....57k..48W. doi:10.1063/1.1839377. ISSN 0031-9228. Retrieved 2022-02-12. Ioannidis, John P. A. (2005). "Why Most Published Research Findings Are False". PLOS Medicine. 2 (8): –124. doi:10.1371/journal.pmed.0020124. ISSN 1549-1676. PMC 1182327. PMID 16060722. Rappert, Brian (2007). "Codes of conduct and biological weapons: an in-process assessment". Biosecurity and Bioterrorism: Biodefense Strategy, Practice, and Science. 5 (2): 145–154. doi:10.1089/bsp.2007.0003. hdl:10036/32132. ISSN 1538-7135. PMID 17608600. David, Paul A. (2008-10-24). "The Historical Origins of 'Open Science': An Essay on Patronage, Reputation and Common Agency Contracting in the Scientific Revolution". Capitalism and Society. 3 (2). doi:10.2202/1932-0213.1040. ISSN 1932-0213. S2CID 41478207. Retrieved 2021-11-11. Schuurbiers, Daan; Osseweijer, Patricia; Kinderlerer, Julian (2009). "Implementing the Netherlands code of conduct for scientific practice-a case study". Science and


id: scientific-law-20260505-0
file: data/en.wikipedia.org/wiki/Scientific_law-0.md
category: reference
title: Scientific law
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_law
date_saved: 2026-05-05T06:28:58.458703+00:00
instance: kb-cron
parent: scientific-law-20260505-0
summary:
Scientific laws or laws of science are statements, based on repeated experiments or observations, that describe or predict a range of natural phenomena. The term law has diverse usage in many cases (approximate, accurate, broad, or narrow) across all fields of natural science (physics, chemistry, astronomy, geoscience, biology). Laws are developed from data and can be further developed through mathematics; in all cases they are directly or indirectly based on empirical evidence. It is generally understood that they implicitly reflect, though they do not explicitly assert, causal relationships fundamental to reality, and are discovered rather than invented. Scientific laws summarize the results of experiments or observations, usually within a certain range of application. In general, the accuracy of a law does not change when a new theory of the relevant phenomenon is worked out, but rather the scope of the law's application, since the mathematics or statement representing the law does not change. As with other kinds of scientific knowledge, scientific laws do not express absolute certainty, as mathematical laws do. A scientific law may be contradicted, restricted, or extended by future observations. A law can often be formulated as one or several statements or equations, so that it can


id: scientific-law-20260505-1
file: data/en.wikipedia.org/wiki/Scientific_law-1.md
category: reference
title: Scientific law
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_law
date_saved: 2026-05-05T06:28:58.458703+00:00
instance: kb-cron
parent: scientific-law-20260505-0
summary:
== Properties == Scientific laws are typically conclusions based on repeated scientific experiments and observations over many years and which have become accepted universally within the scientific community. A scientific law is "inferred from particular facts, applicable to a defined group or class of phenomena, and expressible by the statement that a particular phenomenon always occurs if certain conditions be present". The production of a summary description of our environment in the form of such laws is a fundamental aim of science. Several general properties of scientific laws, particularly when referring to laws in physics, have been identified. Scientific laws are: True, at least within their regime of validity. By definition, there have never been repeatable contradicting observations. Universal. They appear to apply everywhere in the universe. Simple. They are typically expressed in terms of a single mathematical equation. Absolute. Nothing in the universe appears to affect them. Stable. Unchanged since first discovered (although they may have been shown to be approximations of more accurate laws), All-encompassing. Everything in the universe apparently must comply with them (according to observations). Generally conservative of quantity. Often expressions of existing homogeneities (symmetries) of space and time. Typically theoretically reversible in time (if non-quantum),


id: scientific-law-20260505-2
file: data/en.wikipedia.org/wiki/Scientific_law-2.md
category: reference
title: Scientific law
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_law
date_saved: 2026-05-05T06:28:58.458703+00:00
instance: kb-cron
parent: scientific-law-20260505-0
summary:
where ρ is some quantity per unit volume, J is the flux of that quantity (change in quantity per unit time per unit area). Intuitively, the divergence (denoted ∇⋅) of a vector field is a measure of flux diverging radially outwards from a point, so the negative is the amount piling up at a point; hence the rate of change of density in a region of space must be the amount of flux leaving or collecting in some region (see the main article for details). In the table below, the fluxes flows for various physical quantities in transport, and their associated continuity equations, are collected for comparison. More general equations are the convection–diffusion equation and Boltzmann transport equation, which have their roots in the continuity equation. === Laws of classical mechanics === ==== Principle of least action ==== Classical mechanics, including Newton's laws, Lagrange's equations, Hamilton's equations, etc., can be derived from the following principle: δ S = δ ∫ t 1 t 2 L ( q , q ˙ , t ) d t = 0 {\displaystyle \delta {\mathcal {S}}=\delta \int _{t_{1}}^{t_{2}}L(\mathbf {q} ,\mathbf {\dot {q}} ,t)\,dt=0} where S {\displaystyle {\mathcal {S}}} is the action; the integral of the


id: scientific-law-20260505-3
file: data/en.wikipedia.org/wiki/Scientific_law-3.md
category: reference
title: Scientific law
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_law
date_saved: 2026-05-05T06:28:58.458703+00:00
instance: kb-cron
parent: scientific-law-20260505-0
summary:
in which the (more famous) mass–energy equivalence E = mc2 is a special case. General relativity: General relativity is governed by the Einstein field equations, which describe the curvature of space-time due to mass-energy equivalent to the gravitational field. Solving the equation for the geometry of space warped due to the mass distribution gives the metric tensor. Using the geodesic equation, the motion of masses falling along the geodesics can be calculated. Gravitoelectromagnetism: In a relatively flat spacetime due to weak gravitational fields, gravitational analogues of Maxwell's equations can be found; the GEM equations, to describe an analogous gravitomagnetic field. They are well established by the theory, and experimental tests form ongoing research. ==== Classical laws ==== Kepler's laws, though originally discovered from planetary observations (also due to Tycho Brahe), are true for any central forces. === Thermodynamics === Newton's law of cooling Fourier's law Ideal gas law, combines a number of separately developed gas laws; Boyle's law Charles's law Gay-Lussac's law Avogadro's law, into one now improved by other equations of state Dalton's law (of partial pressures) Boltzmann equation Carnot's theorem Kopp's law === Electromagnetism === Maxwell's equations give the time-evolution of the electric and magnetic fields due to


id: scientific-law-20260505-4
file: data/en.wikipedia.org/wiki/Scientific_law-4.md
category: reference
title: Scientific law
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_law
date_saved: 2026-05-05T06:28:58.458703+00:00
instance: kb-cron
parent: scientific-law-20260505-0
summary:
In equilibrium, molecules exist in mixture defined by the transformations possible on the timescale of the equilibrium, and are in a ratio defined by the intrinsic energy of the molecules—the lower the intrinsic energy, the more abundant the molecule. Le Chatelier's principle states that the system opposes changes in conditions from equilibrium states, i.e. there is an opposition to change the state of an equilibrium reaction. Transforming one structure to another requires the input of energy to cross an energy barrier; this can come from the intrinsic energy of the molecules themselves, or from an external source which will generally accelerate transformations. The higher the energy barrier, the slower the transformation occurs. There is a hypothetical intermediate, or transition structure, that corresponds to the structure at the top of the energy barrier. The Hammond–Leffler postulate states that this structure looks most similar to the product or starting material which has intrinsic energy closest to that of the energy barrier. Stabilizing this hypothetical intermediate through chemical interaction is one way to achieve catalysis. All chemical processes are reversible (law of microscopic reversibility) although some processes have such an energy bias, they are essentially irreversible. The reaction rate has the mathematical parameter


id: scientific-law-20260505-5
file: data/en.wikipedia.org/wiki/Scientific_law-5.md
category: reference
title: Scientific law
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_law
date_saved: 2026-05-05T06:28:58.458703+00:00
instance: kb-cron
parent: scientific-law-20260505-0
summary:
In Europe, systematic theorizing about nature (physis) began with the early Greek philosophers and scientists and continued into the Hellenistic and Roman imperial periods, during which times the intellectual influence of Roman law increasingly became paramount.The formula "law of nature" first appears as "a live metaphor" favored by Latin poets Lucretius, Virgil, Ovid, Manilius, in time gaining a firm theoretical presence in the prose treatises of Seneca and Pliny. Why this Roman origin? According to [historian and classicist Daryn] Lehoux's persuasive narrative, the idea was made possible by the pivotal role of codified law and forensic argument in Roman life and culture. For the Romans ... the place par excellence where ethics, law, nature, religion and politics overlap is the law court. When we read Seneca's Natural Questions, and watch again and again just how he applies standards of evidence, witness evaluation, argument and proof, we can recognize that we are reading one of the great Roman rhetoricians of the age, thoroughly immersed in forensic method. And not Seneca alone. Legal models of scientific judgment turn up all over the place, and for example prove equally integral to Ptolemy's approach to verification, where the mind is assigned the role of


id: scientific-priority-20260505-0
file: data/en.wikipedia.org/wiki/Scientific_priority-0.md
category: reference
title: Scientific priority
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_priority
date_saved: 2026-05-05T06:28:59.697468+00:00
instance: kb-cron
parent: scientific-priority-20260505-0
summary:
In science, priority is the credit given to the individual or group of individuals who first made the discovery or proposed the theory. Fame and honours usually go to the first person or group to publish a new finding, even if several researchers arrived at the same conclusion independently and at the same time. Thus, between two or more independent discoverers, the first to publish is the legitimate winner. Hence, the tradition is often referred to as the priority rule, the procedure of which can be summed up in the phrase "publish or perish", because there are generally no prizes for second place in science academia. In a way, the race to be first inspires risk-taking that can lead to scientific breakthroughs which is beneficial to the society (such as discovery of malaria transmission, DNA, HIV, etc.). On the other hand, it can create unhealthy competition and incentives to publish low-quality findings (e.g., quantity over quality or committing scientific misconduct), which can lead to an unreliable published literature and harm scientific progress. == Priority disputes == Priority becomes a difficult issue usually in the context of priority disputes, where the priority for a given theory, understanding, or discovery comes into


id: scientific-study-20260505-0
file: data/en.wikipedia.org/wiki/Scientific_study-0.md
category: reference
title: Scientific study
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_study
date_saved: 2026-05-05T06:29:02.158213+00:00
instance: kb-cron
parent: scientific-study-20260505-0
summary:
Scientific study is a creative action to increase knowledge by systematically collecting, interpreting, and evaluating data. According to the hypothetico-deductive paradigm, it should encompass: The contextualization of the problem; A hypothesis for explaining the problem considering existing theoretical approaches; A verification of the hypotheses by an experiment; Analysis of the test outcome. Scientific study involves scientific theory, scientific method, scientific models, experiments and physical situations. It may refer to: Scientific method, a body of techniques for investigating phenomena, based on empirical or measurable evidence that is subject to the principles of logic and reasoning Observational study, draws inferences about the possible effect of a treatment on subjects, where the assignment of subjects into a treated group versus a control group is outside the control of the investigator Randomized controlled trial, a type of scientific experiment, often in the medical field, where the people being studied are randomly allocated one of the different treatments Science, a systematic enterprise that builds and organizes knowledge in the form of testable explanations and predictions about the universe. Scientific learning includes testing of theories and provide a basis for scientific knowledge. == History == Aristotle is believed to be the first to begin the study


id: scientific-temper-20260505-0
file: data/en.wikipedia.org/wiki/Scientific_temper-0.md
category: reference
title: Scientific temper
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_temper
date_saved: 2026-05-05T06:29:03.358226+00:00
instance: kb-cron
parent: scientific-temper-20260505-0
summary:
The term scientific temper is broadly defined as "a modest open-minded temper—develop new light, new knowledge, new experiments, even when their results are unfavourable to preconceived opinions and long-cherished theories." It is a way of life (defined in this context as an individual and social process of thinking and acting) which uses the scientific method and which may, consequently, include questioning, observing physical reality, testing, hypothesizing, analyzing, and communicating (not necessarily in that order). Discussion, argument and analysis are vital parts of scientific temper. It aims to inculcate the values of scientific thinking, appreciate scientific development, and drive away superstition, religious bigotry, and all forms of pseudo-science. == Development of Scientific Temperament in World == Scientific temper as a notion existed for a long time, and the origin of the term is unknown. The exact terminology became frequently used in the mid-19th century. A Jesuit scholar Thomas Aloysius Hughes gave a short definition in 1893, saying, "A scientific temper... means a scrupulous and rigid exactness... [which] is the outcome of exact science." In his Conway Memorial Lecture in 1922, Bertrand Russell used the example of Albert Einstein to explain the meaning of scientific temper:We have had in recent years a


id: scientific-theory-20260505-0
file: data/en.wikipedia.org/wiki/Scientific_theory-0.md
category: reference
title: Scientific theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_theory
date_saved: 2026-05-05T06:29:04.644741+00:00
instance: kb-cron
parent: scientific-theory-20260505-0
summary:
A scientific theory is an explanation of an aspect of the natural world that can be or that has been repeatedly tested and has corroborating evidence in accordance with the scientific method, using accepted protocols of observation, measurement, and evaluation of results. Where possible, theories are tested under controlled conditions in an experiment. In circumstances not amenable to experimental testing, theories are evaluated through principles of abductive reasoning. Established scientific theories have withstood rigorous scrutiny and embody scientific knowledge. A scientific theory differs from a scientific fact: a fact is an observation, while a theory connects and explains multiple observations. Furthermore, a theory is expected to make predictions which could be confirmed or refuted with additional observations. Stephen Jay Gould wrote that: "...facts and theories are different things, not rungs in a hierarchy of increasing certainty. Facts are the world's data. Theories are structures of ideas that explain and interpret facts." A theory differs from a scientific law in that a law is an empirical description of a relationship between facts and/or other laws. For example, Newton's Law of Gravity is a mathematical equation that can be used to predict the attraction between bodies, but it is not a theory


id: scientific-theory-20260505-1
file: data/en.wikipedia.org/wiki/Scientific_theory-1.md
category: reference
title: Scientific theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_theory
date_saved: 2026-05-05T06:29:04.644741+00:00
instance: kb-cron
parent: scientific-theory-20260505-0
summary:
It can be subjected to minor adaptations to account for new data that do not fit it perfectly, as they are discovered, thus increasing its predictive capability over time. It is among the most parsimonious explanations, economical in the use of proposed entities or explanatory steps as per Occam's razor. This is because for each accepted explanation of a phenomenon, there may be an extremely large, perhaps even incomprehensible, number of possible and more complex alternatives, because one can always burden failing explanations with ad hoc hypotheses to prevent them from being falsified; therefore, simpler theories are preferable to more complex ones because they are more testable. === Definitions from scientific organizations === The United States National Academy of Sciences defines scientific theories as follows: The formal scientific definition of theory is quite different from the everyday meaning of the word. It refers to a comprehensive explanation of some aspect of nature that is supported by a vast body of evidence. Many scientific theories are so well established that no new evidence is likely to alter them substantially. For example, no new evidence will demonstrate that the Earth does not orbit around the Sun (heliocentric theory), or that living things


id: scientific-theory-20260505-2
file: data/en.wikipedia.org/wiki/Scientific_theory-2.md
category: reference
title: Scientific theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_theory
date_saved: 2026-05-05T06:29:04.644741+00:00
instance: kb-cron
parent: scientific-theory-20260505-0
summary:
== Modification and improvement == If experimental results contrary to a theory's predictions are observed, scientists first evaluate whether the experimental design was sound, and if so they confirm the results by independent replication. A search for potential improvements to the theory then begins. Solutions may require minor or major changes to the theory, or none at all if a satisfactory explanation is found within the theory's existing framework. Over time, as successive modifications build on top of each other, theories consistently improve and greater predictive accuracy is achieved. Since each new version of a theory (or a completely new theory) must have more predictive and explanatory power than the last, scientific knowledge consistently becomes more accurate over time. If modifications to the theory or other explanations seem to be insufficient to account for the new results, then a new theory may be required. Since scientific knowledge is usually durable, this occurs much less commonly than modification. Furthermore, until such a theory is proposed and accepted, the previous theory will be retained. This is because it is still the best available explanation for many other phenomena, as verified by its predictive power in other contexts. For example, it has been


id: scientific-theory-20260505-3
file: data/en.wikipedia.org/wiki/Scientific_theory-3.md
category: reference
title: Scientific theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_theory
date_saved: 2026-05-05T06:29:04.644741+00:00
instance: kb-cron
parent: scientific-theory-20260505-0
summary:
=== Theories and facts === A scientific theory does not turn into a fact. Scientific facts are observations that theories organize and explain. As new facts appear, a theory may be revised or new theories may emerge that encompass these additional facts. While American vernacular speech uses "theory" as similar to a "guess" in opposition to a "fact", in science the word "theory" means a model that is expected to explain a wide range of facts. == About theories == === Theories as axioms === The logical positivists thought of scientific theories as statements in a formal language. First-order logic is an example of a formal language. The logical positivists envisaged a similar scientific language. In addition to scientific theories, the language also included observation sentences ("the sun rises in the east"), definitions, and mathematical statements. The phenomena explained by the theories, if they could not be directly observed by the senses (for example, atoms and radio waves), were treated as theoretical concepts. In this view, theories function as axioms: predicted observations are derived from the theories much like theorems are derived in Euclidean geometry. However, the predictions are then tested against reality to verify the predictions, and the "axioms"


id: scientific-theory-20260505-4
file: data/en.wikipedia.org/wiki/Scientific_theory-4.md
category: reference
title: Scientific theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_theory
date_saved: 2026-05-05T06:29:04.644741+00:00
instance: kb-cron
parent: scientific-theory-20260505-0
summary:
...it is incorrect to speak of an assumption as either true or false, since there is no way of proving it to be either (If there were, it would no longer be an assumption). It is better to consider assumptions as either useful or useless, depending on whether deductions made from them correspond to reality...Since we must start somewhere, we must have assumptions, but at least let us have as few assumptions as possible. Certain assumptions are necessary for all empirical claims (e.g. the assumption that reality exists). However, theories do not generally make assumptions in the conventional sense (statements accepted without evidence). While assumptions are often incorporated during the formation of new theories, these are either supported by evidence (such as from previously existing theories) or the evidence is produced in the course of validating the theory. This may be as simple as observing that the theory makes accurate predictions, which is evidence that any assumptions made at the outset are correct or approximately correct under the conditions tested. Conventional assumptions, without evidence, may be used if the theory is only intended to apply when the assumption is valid (or approximately valid). For example, the special theory of relativity


id: scientific-theory-20260505-5
file: data/en.wikipedia.org/wiki/Scientific_theory-5.md
category: reference
title: Scientific theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_theory
date_saved: 2026-05-05T06:29:04.644741+00:00
instance: kb-cron
parent: scientific-theory-20260505-0
summary:
It is easy to obtain confirmations, or verifications, for nearly every theory—if we look for confirmations. Confirmations should count only if they are the result of risky predictions; that is to say, if, unenlightened by the theory in question, we should have expected an event which was incompatible with the theory—an event which would have refuted the theory. Every "good" scientific theory is a prohibition: it forbids certain things from happening. The more a theory forbids, the better it is. A theory which is not refutable by any conceivable event is non-scientific. Irrefutability is not a virtue of a theory (as people often think) but a vice. Every genuine test of a theory is an attempt to falsify it or to refute it. Testability is falsifiability; but there are degrees of testability: some theories are more testable, more exposed to refutation, than others; they take, as it were, greater risks. Confirming evidence should not count except when it is the result of a genuine test of the theory, and this means that it can be presented as a serious but unsuccessful attempt to falsify the theory. (I now speak in such cases of "corroborating evidence".) Some genuinely testable theories, when


id: scientific-theory-20260505-6
file: data/en.wikipedia.org/wiki/Scientific_theory-6.md
category: reference
title: Scientific theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_theory
date_saved: 2026-05-05T06:29:04.644741+00:00
instance: kb-cron
parent: scientific-theory-20260505-0
summary:
A theory is something other than myself. It may be set out on paper as a system of rules, and it is the more truly a theory the more completely it can be put down in such terms. Mathematical theory reaches the highest perfection in this respect. But even a geographical map fully embodies in itself a set of strict rules for finding one's way through a region of otherwise uncharted experience. Indeed, all theory may be regarded as a kind of map extended over space and time. A scientific theory can also be thought of as a book that captures the fundamental information about the world, a book that must be researched, written, and shared. In 1623, Galileo Galilei wrote: Philosophy [i.e. physics] is written in this grand book—I mean the universe—which stands continually open to our gaze, but it cannot be understood unless one first learns to comprehend the language and interpret the characters in which it is written. It is written in the language of mathematics, and its characters are triangles, circles, and other geometrical figures, without which it is humanly impossible to understand a single word of it; without these, one is wandering around in a


id: selection-bias-20260505-0
file: data/en.wikipedia.org/wiki/Selection_bias-0.md
category: reference
title: Selection bias
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Selection_bias
date_saved: 2026-05-05T06:29:08.366229+00:00
instance: kb-cron
parent: selection-bias-20260505-0
summary:
Selection bias is the bias introduced by the selection of individuals, groups, or data for analysis in such a way that the association between exposure and outcome among those selected for analysis differs from the association among those eligible. It typically occurs when researchers condition on a factor that is influenced both by the exposure and the outcome (or their causes), creating a false association between them. Selection bias encompasses several forms of bias, including differential loss-to-follow-up, incidence–prevalence bias, volunteer bias, healthy-worker bias, and nonresponse bias. == Types of bias == === Sampling bias === Sampling bias is systematic error due to a non-random sample of a population, causing some members of the population to be less likely to be included than others, resulting in a biased sample, defined as a statistical sample of a population (or non-human factors) in which all participants are not equally balanced or objectively represented. It is mostly classified as a subtype of selection bias, sometimes specifically termed sample selection bias, but some classify it as a separate type of bias. A distinction of sampling bias (albeit not a universally accepted one) is that it undermines the external validity of a test (the ability of


id: selection-bias-20260505-1
file: data/en.wikipedia.org/wiki/Selection_bias-1.md
category: reference
title: Selection bias
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Selection_bias
date_saved: 2026-05-05T06:29:08.366229+00:00
instance: kb-cron
parent: selection-bias-20260505-0
summary:
=== Observer selection === Philosopher Nick Bostrom has argued that data are filtered not only by study design and measurement, but by the necessary precondition that there has to be someone doing a study. In situations where the existence of the observer or the study is correlated with the data, observation selection effects occur, and anthropic reasoning is required. An example is the past impact event record of Earth: if large impacts cause mass extinctions and ecological disruptions precluding the evolution of intelligent observers for long periods, no one will observe any evidence of large impacts in the recent past (since they would have prevented intelligent observers from evolving). Hence there is a potential bias in the impact record of Earth. Astronomical existential risks might similarly be underestimated due to selection bias, and an anthropic correction has to be introduced. === Volunteer bias === Self-selection bias or a volunteer bias in studies offer further threats to the validity of a study as these participants may have intrinsically different characteristics from the target population of the study. Studies have shown that volunteers tend to come from a higher social standing than from a lower socio-economic background. Furthermore, another study shows that


id: self-experimentation-20260505-0
file: data/en.wikipedia.org/wiki/Self-experimentation-0.md
category: reference
title: Self-experimentation
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Self-experimentation
date_saved: 2026-05-05T06:29:09.699220+00:00
instance: kb-cron
parent: self-experimentation-20260505-0
summary:
Self-experimentation refers to single-subject research in which the experimenter conducts the experiment on themself. Usually, this means that a single person is the designer, operator, subject, analyst, and user or reporter of the experiment. Self-experimentation is an example of citizen science since it can be done by patients or people interested in their own health and well-being, as both research subjects and self-experimenters. It is also referred to as personal science or N-of-1 research. == Biology and medicine == Human scientific self-experimentation principally (though not necessarily) falls into the fields of medicine and psychology. Self-experimentation has a long and well-documented history in medicine which continues to the present day. For example, after failed attempts to infect piglets in 1984, Barry Marshall drank a petri dish of Helicobacter pylori from a patient, and soon developed gastritis, achlorhydria, stomach discomfort, nausea, vomiting, and halitosis. The results were published in 1985 in the Medical Journal of Australia, and is among the most cited articles from the journal. He was awarded the Nobel Prize in Physiology or Medicine in 2005. Evaluations have been presented in the context of clinical trials and program evaluations. == Psychology == In psychology, the best-known self-experiments are the memory


id: self-experimentation-in-medicine-20260505-0
file: data/en.wikipedia.org/wiki/Self-experimentation_in_medicine-0.md
category: reference
title: Self-experimentation in medicine
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Self-experimentation_in_medicine
date_saved: 2026-05-05T06:29:11.041214+00:00
instance: kb-cron
parent: self-experimentation-in-medicine-20260505-0
summary:
Self-experimentation refers to scientific experimentation in which the experimenter conducts the experiment on themself. Often this means that the designer, operator, subject, analyst, and user or reporter of the experiment are all the same. Self-experimentation has a long and well-documented history in medicine which continues to the present. Some of these experiments have been very valuable and shed new and often unexpected insights into different areas of medicine. There are many motivations for self-experiment. These include the wish to get results quickly and avoid the need for a formal organisational structure, to take the ethical stance of taking the same risk as volunteers, or just a desire to do good for humanity. Other ethical issues include whether a researcher should self-experiment because another volunteer would not get the same benefit as the researcher will get, and the question of whether informed consent of a volunteer can truly be given by those outside a research program. A number of distinguished scientists have undertaken self-experimentation, including at least five Nobel laureates; in several cases, the prize was awarded for findings the self-experimentation made possible. Many experiments were dangerous; various people exposed themselves to pathogenic, toxic or radioactive materials. Some self-experimenters, like Jesse


id: self-experimentation-in-medicine-20260505-1
file: data/en.wikipedia.org/wiki/Self-experimentation_in_medicine-1.md
category: reference
title: Self-experimentation in medicine
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Self-experimentation_in_medicine
date_saved: 2026-05-05T06:29:11.041214+00:00
instance: kb-cron
parent: self-experimentation-in-medicine-20260505-0
summary:
== Ethics == As already mentioned, it is an ethical principle that the researcher should not inflict on volunteers anything that the researcher would not be willing to do to themself, but the researcher is not always a suitable, or even possible, subject for the experiment. For instance, the researcher may be the wrong sex if the research is into hormone treatment for women, or may be too old, or too young. The ethical question for the researchers is would they agree to the experiment if they were in the same position as the volunteers? Another issue that can lead researchers not to take part is whether the researcher would stand to gain any benefit from taking part in the experiment. It is an ethical principle that volunteers must stand to gain some benefit from the research, even if that is only a remote future possibility of treatment being found for a disease that they only have a small chance of contracting. Tests on experimental drugs are sometimes conducted on sufferers of an untreatable condition. If the researcher does not have that condition then there can be no possible benefit to them personally. For instance, Ronald C. Desrosiers in responding


id: self-experimentation-in-medicine-20260505-2
file: data/en.wikipedia.org/wiki/Self-experimentation_in_medicine-2.md
category: reference
title: Self-experimentation in medicine
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Self-experimentation_in_medicine
date_saved: 2026-05-05T06:29:11.041214+00:00
instance: kb-cron
parent: self-experimentation-in-medicine-20260505-0
summary:
=== Anaesthesia === Dentist Horace Wells made multiple experiments with nitrous oxide, diethyl ether, and chloroform while trying to determine their uses as anaesthetics. The first, conducted in 1844, consisted of having his assistant John Riggs dose him with nitrous oxide and then extract one of his teeth. His later self-experimentation of ether and chloroform took place in 1848, and he eventually became addicted to chloroform due to excessive use. He inhaled chloroform as an anaesthetic shortly before committing suicide on January 24, 1848. Lidocaine, the first amino amide–type local anaesthetic, was first synthesized under the name xylocaine by Swedish chemist Nils Löfgren in 1943. His colleague Bengt Lundqvist performed the first injection anaesthesia experiments on himself. === Asthma === Roger Altounyan developed the use of sodium cromoglycate as a remedy for asthma, based on khella, a traditional Middle Eastern remedy, with experiments on himself. === Blood === ==== ABO blood group system ==== Dr. Karl Landsteiner's discovery of the ABO blood group system in 1900 was based on an analysis of blood samples from six members of his laboratory staff, including himself. ==== Thrombocytopenia ==== In the Harrington–Hollingsworth experiment in 1950, William J. Harrington performed an exchange blood transfusion


id: self-experimentation-in-medicine-20260505-3
file: data/en.wikipedia.org/wiki/Self-experimentation_in_medicine-3.md
category: reference
title: Self-experimentation in medicine
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Self-experimentation_in_medicine
date_saved: 2026-05-05T06:29:11.041214+00:00
instance: kb-cron
parent: self-experimentation-in-medicine-20260505-0
summary:
===== Helicobacter pylori ===== In 1984 a Western Australian scientist, Dr Barry Marshall, discovered the link between Helicobacter pylori (at that time known as Campylobacter pylori) and gastritis. This was based on a series of self-experiments that involved gastroscopy and biopsy, ingestion of H. pylori, regastroscopy and biopsy and subsequent treatment with tinidazole. His only option was self-experimentation: ethical measures forbade him from administering H. pylori to any other person. In 2005, Marshall and his long-time collaborator Robin Warren were awarded Nobel Prize in Physiology or Medicine, "for their discovery of the bacterium Helicobacter pylori and its role in gastritis and peptic ulcer disease". Marshall's experiment debunked the long-held belief of the medical profession that stress was the cause of gastritis. This cleared the way for the development of antibiotic treatments for gastritis and peptic ulcers and a new line of research into the likely role of H. pylori in stomach cancer. ===== Campylobacter jejuni ===== Marshall's investigation was preceded by David A. Robinson who, in 1980, ingested Campylobacter jejuni, a bacterium found in cow's milk, to investigate whether gastritis could be caused by drinking milk infected with C. jejuni. Robinson became sick as a result. Robinson needed to do


id: self-experimentation-in-medicine-20260505-4
file: data/en.wikipedia.org/wiki/Self-experimentation_in_medicine-4.md
category: reference
title: Self-experimentation in medicine
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Self-experimentation_in_medicine
date_saved: 2026-05-05T06:29:11.041214+00:00
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parent: self-experimentation-in-medicine-20260505-0
summary:
==== Disulfiram ==== In 1945, during the German occupation of Denmark, Erik Jacobsen and Jens Hald at the Danish drug company Medicinalco (which had a group of enthusiastic self-experimenters that called itself the "Death Battalion") were exploring the possible use of disulfiram to treat intestinal parasites, and in the course of testing it on themselves, accidentally discovered its effects (Disulfiram-alcohol reaction) when alcohol is ingested, which led several years later to the drug called Antabuse. ==== Furan ==== Chauncey D. Leake, in 1930, took furan as a possible substitute for aspirin but it just gave him a splitting headache and painful urination that lasted three days. ==== Grapefruit juice ==== David G. Bailey, in 1989, was researching the effects of drinking alcohol while taking the then experimental drug felodipine. It was usual in this kind of research to mix the alcohol with orange juice but Bailey did not like the taste of this drink so used grapefruit juice instead. Bailey found that there was three times more felodipine in his, and fellow researchers', blood than had been reported by other scientists using orange juice. It was later found that grapefruit juice suppresses an enzyme responsible for breaking down a large


id: self-experimentation-in-medicine-20260505-5
file: data/en.wikipedia.org/wiki/Self-experimentation_in_medicine-5.md
category: reference
title: Self-experimentation in medicine
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Self-experimentation_in_medicine
date_saved: 2026-05-05T06:29:11.041214+00:00
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parent: self-experimentation-in-medicine-20260505-0
summary:
==== Hanging ==== In the early 1900s Nicolae Minovici, a professor of forensic science in Bucharest, undertook a series of experiments into hanging. At first he put the noose around his neck while lying down and had an assistant put tension on the rope. He then moved on to full suspension by the neck. Finally, he attempted suspension with a slipping hangman's knot, but the pain was too great for him to continue. He could not swallow for a month. Minovici was determined to surpass a record set by Dr. Fleichmann of Erlangen, who in 1832, self-asphyxiated for two minutes. However, Minovici could not get close to this and disbelieved Fleichmann. Minovici and Fleichmann are not the only ones to self-experiment with strangulation. Graeme Hammond, a doctor in New York, tried it in 1882. Francis Bacon described an even earlier occasion in 1623 when the self-experimenter stepped off a stool with a rope around his neck, but was unable to regain his footing on the stool without assistance. ==== Rapid acceleration ==== John Paul Stapp, in 1954, sat in a rocket sled fired along rails in a series of steadily more violent tests. Speeds reached 631 mph, almost the speed


id: self-experimentation-in-medicine-20260505-6
file: data/en.wikipedia.org/wiki/Self-experimentation_in_medicine-6.md
category: reference
title: Self-experimentation in medicine
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Self-experimentation_in_medicine
date_saved: 2026-05-05T06:29:11.041214+00:00
instance: kb-cron
parent: self-experimentation-in-medicine-20260505-0
summary:
There have been several cases of surgeons operating on themselves, but most often it has been in the nature of an emergency rather than experiment. Such a case was Leonid Rogozov who was obliged to remove his own appendix in 1961 while stranded in Antarctica in winter. However, the first surgeon to carry out this self-operation, Evan O'Neill Kane in 1921, did so with an element of experiment. Although Kane's operation was necessary, it was not necessary to do it himself, so that in itself was experimental. More than that, Kane wished to experience the operation under local anaesthetic before trying the procedure on his patients. Kane advocated a reduction in the use of general anaesthetic by surgeons. In 2023, Michael Raduga, a Russian lucid dreaming researcher, performed self-neurosurgery that included trepanation, electrode implantation, and electrical stimulation of the motor cortex. ==== Sensory deprivation ==== John C. Lilly developed the first sensory deprivation tanks and self-experimented them with the intention to study the origin of consciousness and its relation to the brain by creating an environment which isolates an individual from external stimulation. ==== Temperature and pressure ==== Joseph Barcroft, in 1920, spent six days in a sealed glass chamber


id: serendipity-20260505-0
file: data/en.wikipedia.org/wiki/Serendipity-0.md
category: reference
title: Serendipity
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Serendipity
date_saved: 2026-05-05T06:29:12.267758+00:00
instance: kb-cron
parent: serendipity-20260505-0
summary:
Serendipity is an unplanned fortunate discovery. The term was coined by Horace Walpole in 1754. The concept is often associated with scientific and technological breakthroughs, where accidental discoveries led to new insights or inventions. Many significant discoveries in history were serendipitous, including penicillin, Post-it notes, Popsicles, and the microwave oven, arising from unforeseen circumstances that were then recognized and capitalized upon. == Definition == Christian Busch views serendipity as "active luck", where chance encounters and human action come together. A missed flight or a casual walk in the park can lead to new friendships, interests, or even career opportunities. While serendipity in popular usage is often understood as a matter of pure chance, scientific discussions emphasize the crucial role of human agency—recognizing, interpreting, and acting upon unexpected opportunities. This interaction between chance and conscious action has been a key theme in areas such as creativity, leadership, innovation, and entrepreneurship. == Etymology == The first noted use of "serendipity" was by Horace Walpole on 28 January 1754. In a letter he wrote to his friend Horace Mann, Walpole explained an unexpected discovery he had made about a painting of Bianca Cappello, which he recently received from Mann as a gift. The


id: serendipity-20260505-1
file: data/en.wikipedia.org/wiki/Serendipity-1.md
category: reference
title: Serendipity
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Serendipity
date_saved: 2026-05-05T06:29:12.267758+00:00
instance: kb-cron
parent: serendipity-20260505-0
summary:
Carbonated water was invented by Joseph Priestley, independently and by accident, in 1767 when he discovered a method of infusing water with carbon dioxide after having suspended a bowl of water above a beer vat at a brewery in Leeds, Yorkshire. He wrote of the "peculiar satisfaction" he found in drinking it, and in 1772 he published a paper entitled Impregnating Water with Fixed Air. Vulcanization was discovered by Charles Goodyear in 1839 when he accidentally dropped rubber mixed with sulfur on a hot frying pan. He noticed that the resulting rubber was stronger and heat-resistant. Corn flakes were invented in 1894 when John Harvey Kellogg unintentionally left a batch of wheat-berry dough out over night. The next day, he decided to figure out what could be done to salvage it, rather than throwing it out. John, Will, and Ella Kellogg then discerned what happened and realized that this process could be reliably recreated through a process known as tempering. Safety glass first originated when French chemist Édouard Bénédictus accidentally dropped a glass flask in 1903 and noticed that it did not shatter like traditional glass. He then sought to refine the material to create a safer form of glass.


id: serendipity-20260505-2
file: data/en.wikipedia.org/wiki/Serendipity-2.md
category: reference
title: Serendipity
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Serendipity
date_saved: 2026-05-05T06:29:12.267758+00:00
instance: kb-cron
parent: serendipity-20260505-0
summary:
== Related terms == Several uncommonly used terms have been derived from the concept and name of serendipity. William Boyd coined the term zemblanity in the late twentieth century to mean somewhat the opposite of serendipity: "making unhappy, unlucky and expected discoveries occurring by design". The derivation is speculative, but believed to be from Nova Zembla, a barren archipelago once the site of Russian nuclear testing. Bahramdipity is derived directly from Bahram Gur as characterized in The Three Princes of Serendip. It describes the suppression of serendipitous discoveries or research results by powerful individuals. In addition, Solomon & Bronstein (2018) further distinguish between perceptual and realised pseudo-serendipity and nemorinity. == See also == Browse Coincidence Felix culpa Insight Lateral thinking Multiple discovery Role of chance in scientific discoveries Serendipaceratops Serendipity Sapphire Side effect Synchronicity == References == == Further reading == Merton, Robert K.; Barber, Elinor (2004). The Travels and Adventures of Serendipity: A Study in Sociological Semantics and the Sociology of Science. Princeton University Press. ISBN 978-0691117546. (Manuscript written 1958). Hannan, Patrick J. (2006). Serendipity, Luck and Wisdom in Research. iUniverse. ISBN 978-0595365517. Roberts, Royston M. (1989). Serendipity: Accidental Discoveries in Science. Wiley. ISBN 978-0471602033. Isabelle Rivoal and Noel


id: statistical-alchemy-20260505-0
file: data/en.wikipedia.org/wiki/Statistical_alchemy-0.md
category: reference
title: Statistical alchemy
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Statistical_alchemy
date_saved: 2026-05-05T06:29:16.221389+00:00
instance: kb-cron
parent: statistical-alchemy-20260505-0
summary:
Statistical alchemy was a term originated by John Maynard Keynes to describe econometrics in 1939. The phrase has subsequently been used by Alvan Feinstein to describe meta-analysis. It is generally regarded as a deprecatory term which undermines attempts to present such activities as meeting the rigorous standards of science. == Econometrics == Keynes (1939) wrote a review of Jan Tinbergen's Statistical Testing of Business-Cycle Theories. Although he praised Tinbergen for his objectivity, he however depicted his methodology as "black magic" which he regarded as essentially untrustworthy. He was unpersuaded that "this brand of statistical alchemy is ripe to become a branch of science" (emphasis in the original). Often this metaphor is seen as a way of suggesting that econometricians were following a foolhardy pursuit comparable to the alchemical quest of turning base metal into gold. However G. M. P. Swann points out that Keynes was well aware that such eminent early scientists as Isaac Newton. He rather proposes a more nuanced interpretation of the metaphor as referring to the Alkahest, a universal solvent, which, it was claimed could turn stone into water. He claimed that by restricting econometrics to theory, mathematics and statistics, econometricians had discarded other important applied techniques.


id: statistical-inference-20260505-0
file: data/en.wikipedia.org/wiki/Statistical_inference-0.md
category: reference
title: Statistical inference
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Statistical_inference
date_saved: 2026-05-05T06:29:17.584057+00:00
instance: kb-cron
parent: statistical-inference-20260505-0
summary:
Statistical inference is the process of using data analysis to infer properties of an underlying probability distribution. Inferential statistical analysis infers properties of a population, for example by testing hypotheses and deriving estimates. It is assumed that the observed data set is sampled from a larger population. Inferential statistics can be contrasted with descriptive statistics. Descriptive statistics is solely concerned with properties of the observed data, and it does not rest on the assumption that the data come from a larger population. In machine learning, the term inference is sometimes used instead to mean "make a prediction, by evaluating an already trained model"; in this context inferring properties of the model is referred to as training or learning (rather than inference), and using a model for prediction is referred to as inference (instead of prediction); see also predictive inference. == Introduction == Statistical inference makes propositions about a population, using data drawn from the population with some form of sampling. Given a hypothesis about a population, for which we wish to draw inferences, statistical inference consists of (first) selecting a statistical model of the process that generates the data and (second) deducing propositions from the model. Konishi and Kitagawa state


id: statistical-inference-20260505-1
file: data/en.wikipedia.org/wiki/Statistical_inference-1.md
category: reference
title: Statistical inference
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Statistical_inference
date_saved: 2026-05-05T06:29:17.584057+00:00
instance: kb-cron
parent: statistical-inference-20260505-0
summary:
Given the difficulty in specifying exact distributions of sample statistics, many methods have been developed for approximating these. With finite samples, approximation results measure how close a limiting distribution approaches the statistic's sample distribution: For example, with 10,000 independent samples the normal distribution approximates (to two digits of accuracy) the distribution of the sample mean for many population distributions, by the Berry–Esseen theorem. Yet for many practical purposes, the normal approximation provides a good approximation to the sample-mean's distribution when there are 10 (or more) independent samples, according to simulation studies and statisticians' experience. Following Kolmogorov's work in the 1950s, advanced statistics uses approximation theory and functional analysis to quantify the error of approximation. In this approach, the metric geometry of probability distributions is studied; this approach quantifies approximation error with, for example, the Kullback–Leibler divergence, Bregman divergence, and the Hellinger distance. With indefinitely large samples, limiting results like the central limit theorem describe the sample statistic's limiting distribution if one exists. Limiting results are not statements about finite samples, and indeed are irrelevant to finite samples. However, the asymptotic theory of limiting distributions is often invoked for work with finite samples. For example, limiting results are often invoked to


id: statistical-inference-20260505-2
file: data/en.wikipedia.org/wiki/Statistical_inference-2.md
category: reference
title: Statistical inference
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Statistical_inference
date_saved: 2026-05-05T06:29:17.584057+00:00
instance: kb-cron
parent: statistical-inference-20260505-0
summary:
This paradigm calibrates the plausibility of propositions by considering (notional) repeated sampling of a population distribution to produce datasets similar to the one at hand. By considering the dataset's characteristics under repeated sampling, the frequentist properties of a statistical proposition can be quantified—although in practice this quantification may be challenging. ==== Examples of frequentist inference ==== p-value Confidence interval Null hypothesis significance testing ==== Frequentist inference, objectivity, and decision theory ==== One interpretation of frequentist inference (or classical inference) is that it is applicable only in terms of frequency probability; that is, in terms of repeated sampling from a population. However, the approach of Neyman develops these procedures in terms of pre-experiment probabilities. That is, before undertaking an experiment, one decides on a rule for coming to a conclusion such that the probability of being correct is controlled in a suitable way: such a probability need not have a frequentist or repeated sampling interpretation. In contrast, Bayesian inference works in terms of conditional probabilities (i.e. probabilities conditional on the observed data), compared to the marginal (but conditioned on unknown parameters) probabilities used in the frequentist approach. The frequentist procedures of significance testing and confidence intervals can be constructed without regard


id: statistical-inference-20260505-3
file: data/en.wikipedia.org/wiki/Statistical_inference-3.md
category: reference
title: Statistical inference
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Statistical_inference
date_saved: 2026-05-05T06:29:17.584057+00:00
instance: kb-cron
parent: statistical-inference-20260505-0
summary:
Formulating the statistical model: A statistical model is defined based on the problem at hand, specifying the distributional assumptions and the relationship between the observed data and the unknown parameters. The model can be simple, such as a normal distribution with known variance, or complex, such as a hierarchical model with multiple levels of random effects. Constructing the likelihood function: Given the statistical model, the likelihood function is constructed by evaluating the joint probability density or mass function of the observed data as a function of the unknown parameters. This function represents the probability of observing the data for different values of the parameters. Maximizing the likelihood function: The next step is to find the set of parameter values that maximizes the likelihood function. This can be achieved using optimization techniques such as numerical optimization algorithms. The estimated parameter values, often denoted as y ¯ {\displaystyle {\bar {y}}} , are the maximum likelihood estimates (MLEs). Assessing uncertainty: Once the MLEs are obtained, it is crucial to quantify the uncertainty associated with the parameter estimates. This can be done by calculating standard errors, confidence intervals, or conducting hypothesis tests based on asymptotic theory or simulation techniques such as bootstrapping. Model checking:


id: stigler-s-law-of-eponymy-20260505-0
file: data/en.wikipedia.org/wiki/Stigler's_law_of_eponymy-0.md
category: reference
title: Stigler's law of eponymy
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Stigler's_law_of_eponymy
date_saved: 2026-05-05T06:29:18.810696+00:00
instance: kb-cron
parent: stigler-s-law-of-eponymy-20260505-0
summary:
Stigler's law of eponymy, proposed by University of Chicago statistics professor Stephen Stigler in 1980, states that no scientific discovery is named after its original discoverer. Examples include Hubble's law, which was derived by Georges Lemaître two years before Edwin Hubble; the Pythagorean theorem, which was known to Babylonian mathematicians and to Indian mathematicians before Pythagoras; and Halley's Comet, which was observed by astronomers since at least 240 BC (although its official designation is due to the first ever mathematical prediction of such astronomical phenomenon in the sky, not to its discovery). Stigler attributed the discovery of Stigler's law to sociologist Robert K. Merton, making "Stigler's law" an example of Stigler's law. In his paper, Stigler wrote: I have chosen as a title for this paper, and for the thesis I wish to present and discuss, "Stigler's law of eponymy". At first glance this may appear to be a flagrant violation of the "Institutional Norm of Humility," and since statisticians are even more aware of the importance of norms than are members of other disciplines, I hasten to add a humble disclaimer. If there is an idea in this paper that is not at least implicit in Merton's The Sociology


id: list-of-examples-of-stigler-s-law-20260505-0
file: data/en.wikipedia.org/wiki/List_of_examples_of_Stigler's_law-0.md
category: reference
title: List of examples of Stigler's law
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/List_of_examples_of_Stigler's_law
date_saved: 2026-05-05T06:29:20.028518+00:00
instance: kb-cron
parent: list-of-examples-of-stigler-s-law-20260505-0
summary:
Stigler's law concerns the supposed tendency of eponymous expressions for scientific discoveries to honor people other than their respective originators. Examples include: == A == Aharonov–Bohm effect. Werner Ehrenberg and Raymond E. Siday first predicted the effect in 1949, and similar effects were later rediscovered by Yakir Aharonov and David Bohm in 1959. Ampère's circuital law was inspired by the experimental results of André-Marie Ampère, and named in his honor. However, it was James Clerk Maxwell who combined those results into a single mathematical law. Arabic numerals, first developed in India around 7th century. Archimedes' screw is known to predate Archimedes by at least three centuries in ancient Babylon. Argand diagram by Caspar Wessel in 1797, predating Jean-Robert Argand by nine years. Arrhenius equation. The equation was first proposed by the Dutch chemist J. H. van 't Hoff in 1884; five years later in 1889, the Swedish chemist Svante Arrhenius provided a physical justification and interpretation for it. Auger effect. First discovered by Lise Meitner in 1922 and then, independently, in 1923 by Pierre Victor Auger. == B == Bailey–Borwein–Plouffe formula was discovered by Simon Plouffe, who has since expressed regret at having to share credit for his discovery. Bell


id: list-of-examples-of-stigler-s-law-20260505-1
file: data/en.wikipedia.org/wiki/List_of_examples_of_Stigler's_law-1.md
category: reference
title: List of examples of Stigler's law
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/List_of_examples_of_Stigler's_law
date_saved: 2026-05-05T06:29:20.028518+00:00
instance: kb-cron
parent: list-of-examples-of-stigler-s-law-20260505-0
summary:
== C == Cantor–Bernstein–Schröder theorem (also known by other variations, such as Schröder-Bernstein theorem) first proved by Richard Dedekind Cantor set, discovered in 1874 by Henry John Stephen Smith and introduced by German mathematician Georg Cantor 1883. Carmichael number: Václav Šimerka listed the first seven Carmichael numbers in 1885; they are named after Robert Daniel Carmichael who subsequently discovered the first one in 1910. Cartan matrices, first investigated by Wilhelm Killing. Cardano's formula, the solution to general cubic equations. Cardano stated that it was discovered by Scipione del Ferro, who passed the knowledge to his student Antonio Maria Fior. Around 1535 Niccolò Fontana Tartaglia learned of this from Fior and re-derived the formula for the cubic, which he later shared with Cardano. Cassegrain reflector, named after a design published in 1672 which has been attributed to Laurent Cassegrain, but was already known to Bonaventura Cavalieri in 1632 and Marin Mersenne in 1636. Cartesian duality: Named for René Descartes, but Teresa of Avila and her contemporaries wrote about similar methods of philosophical exploration eight to ten years before Descartes was born. Cavendish balance for measuring the universal gravitational constant, first devised and constructed by John Michell. The Cayley–Hamilton theorem was proven


id: list-of-examples-of-stigler-s-law-20260505-2
file: data/en.wikipedia.org/wiki/List_of_examples_of_Stigler's_law-2.md
category: reference
title: List of examples of Stigler's law
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/List_of_examples_of_Stigler's_law
date_saved: 2026-05-05T06:29:20.028518+00:00
instance: kb-cron
parent: list-of-examples-of-stigler-s-law-20260505-0
summary:
== G == Galileo's paradox: the property of infinite sets was known to Duns Scotus. Gauss's law: first described by Joseph Louis Lagrange in 1773, over half a century before Gauss. Gauss's theorem: first proved by Ostrogradsky in 1831. Gaussian distribution: the normal distribution was introduced by Abraham de Moivre in 1733, but named after Carl Friedrich Gauss who began using it in 1794. Gaussian elimination: was already in well-known textbooks such as Thomas Simpson's when Gauss in 1809 remarked that he used "common elimination." Gibbs phenomenon: named for Josiah Willard Gibbs who published in 1901. First discovered by Henry Wilbraham in 1851. Goodhart's law, with several earlier variations, like Campbell's law. The Graetz circuit, also known as the diode bridge, was invented and patented in 1896 by Karol Pollak a year before it was published by Leo Graetz. The Graham escapement is often erroneously credited to English clockmaker George Graham but it was actually invented by astronomer Richard Towneley. The Gregorian telescope is named after James Gregory, who published it in 1663, but was already known to Bonaventura Cavalieri in 1632 and Marin Mersenne in 1636. Gresham's law was described by Nicolaus Copernicus in 1519, the year of Thomas


id: list-of-examples-of-stigler-s-law-20260505-3
file: data/en.wikipedia.org/wiki/List_of_examples_of_Stigler's_law-3.md
category: reference
title: List of examples of Stigler's law
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/List_of_examples_of_Stigler's_law
date_saved: 2026-05-05T06:29:20.028518+00:00
instance: kb-cron
parent: list-of-examples-of-stigler-s-law-20260505-0
summary:
== M == Maxwell's equations. The modern form of the equations in their most common formulation is credited to Oliver Heaviside, based on James Clerk Maxwell's original work. Madelung rule, describing the order in which electron orbitals are filled, named after Erwin Madelung but first discovered by Charles Janet. Magellanic Clouds, while observed by Antonio Pigafetta on one of Magellan's voyages there were previous reports made by 16th century Italian authors Peter Martyr d'Anghiera and Andrea Corsali and earlier reports by Arabic astronomers. Matthew effect, named by Robert K. Merton after the writer of the Gospel of Matthew quoting the words of Jesus. Meadow's law, the formulation that one cot death in a family is tragic, two suspicious, and three murder, originally described by D.J. and V.J.M. Di Maio. Metropolis–Hastings algorithm. The algorithm was named after Nicholas Metropolis, who was the director of the Theoretical Division of Los Alamos National Laboratory at the time of writing the paper Equation of State Calculations by Fast Computing Machines. However, Metropolis did not contribute to that study in any way, as confirmed by various sources. The research problem was proposed by Augusta H. Teller and solved by Marshall N. Rosenbluth and Arianna W.


id: list-of-examples-of-stigler-s-law-20260505-4
file: data/en.wikipedia.org/wiki/List_of_examples_of_Stigler's_law-4.md
category: reference
title: List of examples of Stigler's law
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/List_of_examples_of_Stigler's_law
date_saved: 2026-05-05T06:29:20.028518+00:00
instance: kb-cron
parent: list-of-examples-of-stigler-s-law-20260505-0
summary:
== R == The Reynolds number in fluid mechanics was introduced by George Stokes, but is named after Osborne Reynolds, who popularized its use. Richards equation is attributed to Richards in his 1931 publication, but was earlier introduced by Richardson in 1922 in his book "Weather prediction by numerical process." (Cambridge University press. p. 262) as pointed out by John Knight and Peter Raats in "The contributions of Lewis Fry Richardson to drainage theory, soil physics, and the soil-plant-atmosphere continuum" EGU General Assembly 2016. Russell's paradox is a paradox in set theory that Bertrand Russell discovered and published in 1901. However, Ernst Zermelo had independently discovered the paradox in 1899. == S == The Sankey diagram was invented by Charles Joseph Minard The Schottky diode was neither discovered by Schottky nor its operation correctly explained by him. The actual nature of the metal–semiconductor junction was noted by Hans Bethe. The Schröder–Bernstein theorem in set theory was first stated without proof by Georg Cantor and first proved by Richard Dedekind Shuey's equation from 1985, which is an approximation of the Zoeprittz Equation first published in 1919. Simpson's paradox, a term introduced by Colin R. Blyth in 1972; but Edward Simpson did


id: strong-inference-20260505-0
file: data/en.wikipedia.org/wiki/Strong_inference-0.md
category: reference
title: Strong inference
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Strong_inference
date_saved: 2026-05-05T06:29:21.302231+00:00
instance: kb-cron
parent: strong-inference-20260505-0
summary:
In philosophy of science, strong inference is a model of scientific inquiry that emphasizes the need for alternative hypotheses, rather than a single hypothesis to avoid confirmation bias. The term "strong inference" was coined by John R. Platt, a biophysicist at the University of Chicago. Platt notes that some fields, such as molecular biology and high-energy physics, seem to adhere strongly to strong inference, with very beneficial results for the rate of progress in those fields. == The single hypothesis problem == The problem with single hypotheses, confirmation bias, was aptly described by Thomas Chrowder Chamberlin in 1897: The moment one has offered an original explanation for a phenomenon which seems satisfactory, that moment affection for [one’s] intellectual child springs into existence, and as the explanation grows into a definite theory [one’s] parental affections cluster about [the] offspring and it grows more and more dear .... There springs up also unwittingly a pressing of the theory to make it fit the facts and a pressing of the facts to make them fit the theory... The temptation to misinterpret results that contradict the desired hypothesis is probably irresistible. Despite the admonitions of Platt, reviewers of grant-applications often require "A Hypothesis" as


id: structuralism-philosophy-of-science-20260505-0
file: data/en.wikipedia.org/wiki/Structuralism_(philosophy_of_science)-0.md
category: reference
title: Structuralism (philosophy of science)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Structuralism_(philosophy_of_science)
date_saved: 2026-05-05T06:29:22.643026+00:00
instance: kb-cron
parent: structuralism-philosophy-of-science-20260505-0
summary:
In the philosophy of science, structuralism (also known as scientific structuralism or as the structuralistic theory-concept) asserts that all aspects of reality are best understood in terms of empirical scientific constructs of entities and their relations, rather than in terms of concrete entities in themselves. == Overview == Structuralism is an active research program in the philosophy of science, which was first developed in the late 1960s and throughout the 1970s by several analytic philosophers. As an instance of structuralism, the concept of matter should be interpreted not as an absolute property of nature in itself, but instead of how scientifically grounded mathematical relations describe how the concept of matter interacts with other properties, whether that be in a broad sense such as the gravitational fields that mass-produces or more empirically as how matter interacts with sense systems of the body to produce sensations such as weight. Structuralism's aim is to comprise all important aspects of an empirical theory in one formal framework. The proponents of this meta-theoretic theory are Frederick Suppe, Patrick Suppes, Ronald Giere, Joseph D. Sneed, Wolfgang Stegmüller, Carlos Ulises Moulines, Wolfgang Balzer, John Worrall, Elie Georges Zahar, Pablo Lorenzano, Otávio Bueno, Anjan Chakravartty, Tian Yu Cao,


id: structuralism-philosophy-of-science-20260505-1
file: data/en.wikipedia.org/wiki/Structuralism_(philosophy_of_science)-1.md
category: reference
title: Structuralism (philosophy of science)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Structuralism_(philosophy_of_science)
date_saved: 2026-05-05T06:29:22.643026+00:00
instance: kb-cron
parent: structuralism-philosophy-of-science-20260505-0
summary:
=== Ontic structural realism === While ESR claims that only the structure of reality is knowable, ontic structural realism (OSR) goes further to claim that structure is all there is. In this view, reality has no "nature" underlying its observed structure. Rather, reality is fundamentally structural, though variants of OSR disagree on precisely which aspects of structure are primitive. OSR is strongly motivated by modern physics, particularly quantum field theory, which undermines intuitive notions of identifiable objects with intrinsic properties. Some early quantum physicists held this view, including Hermann Weyl (1931), Ernst Cassirer (1936), and Arthur Eddington (1939). Recently, OSR has been called "the most fashionable ontological framework for modern physics". Max Tegmark takes this concept even further with the mathematical universe hypothesis, which proposes that, if our universe is only a particular structure, then it is no more real than any other structure. == Definition of structure == In mathematical logic, a mathematical structure is a standard concept. A mathematical structure is a set of abstract entities with relations between them. The natural numbers under arithmetic constitute a structure, with relations such as "is evenly divisible by" and "is greater than". Here the relation "is greater than" includes the


id: suspension-of-judgment-20260505-0
file: data/en.wikipedia.org/wiki/Suspension_of_judgment-0.md
category: reference
title: Suspension of judgment
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Suspension_of_judgment
date_saved: 2026-05-05T06:29:23.829938+00:00
instance: kb-cron
parent: suspension-of-judgment-20260505-0
summary:
Suspended judgment is a cognitive process and a rational state of mind in which one withholds judgments, particularly on the drawing of moral or ethical conclusions. The opposite of suspension of judgment is premature judgement, usually shortened to prejudice. While prejudgment involves drawing a conclusion or making a judgment before having the information relevant to such a judgment, suspension of judgment involves waiting for all the facts before making a decision. == Law == Suspension of judgment is used in civil law to indicate a court's decision to nullify a civil judgment. Motions to set aside judgments entered in civil cases in the United States district courts are governed by Rule 60 of the Federal Rules of Civil Procedure which opens with the statement, "On motion and just terms, the court may relieve a party or its legal representative from a final judgment, order, or proceeding...". The rule is quite straightforward and court room application is mostly as stated. In the New York Law Journal David Bliven argues that suspended judgement ought to be an alternative disposition in family offenses (a type of civil case), particularly in cases where a family judgement is being used as behavior modification rather than


id: testability-20260505-0
file: data/en.wikipedia.org/wiki/Testability-0.md
category: reference
title: Testability
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Testability
date_saved: 2026-05-05T06:29:25.025771+00:00
instance: kb-cron
parent: testability-20260505-0
summary:
Testability is a primary aspect of science and the scientific method. There are two components to testability: Falsifiability or defeasibility, which means that counterexamples to the hypothesis are logically possible. The practical feasibility of observing a reproducible series of such counterexamples if they do exist. In short, a hypothesis is testable if there is a possibility of deciding whether it is true or false based on experimentation by anyone. This allows anyone to decide whether a theory can be supported or refuted by data. However, the interpretation of experimental data may be also inconclusive or uncertain. Karl Popper introduced the concept that scientific knowledge had the property of falsifiability as published in The Logic of Scientific Discovery. == See also == Confirmability Controllability Observability Scientific method Test method == Further reading == == References ==


id: violation-paradigm-20260505-0
file: data/en.wikipedia.org/wiki/Violation_paradigm-0.md
category: reference
title: Violation paradigm
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Violation_paradigm
date_saved: 2026-05-05T06:29:38.826382+00:00
instance: kb-cron
parent: violation-paradigm-20260505-0
summary:
A violation paradigm is a scientific method where the scientist perturbs an expected factor to look at the subject's following reactions. These reactions are believed to be relevant to the process studied. For example, creating wrong word segmentations in a text will destabilize the reader. This warns the researcher that the respondent's brain considers the characters are united into words, and not just as a succession of given sets of letters. The process was originally developed by Danks, Bohn & Fear (1983), and proved valid (Chen 1999). == See also == Danks, Bohn, Fear (1983), Comprehension processes in oral reading, The process of language understanding, pp. 193–223. Chen, H.-C. (1999), "How do readers of Chinese process words during reading for comprehension ?", Reading Chinese Script: A Cognitive Analysis, Routledge, pp. 257–278, ISBN 9780805824780


id: why-most-published-research-findings-are-false-20260505-0
file: data/en.wikipedia.org/wiki/Why_Most_Published_Research_Findings_Are_False-0.md
category: reference
title: Why Most Published Research Findings Are False
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Why_Most_Published_Research_Findings_Are_False
date_saved: 2026-05-05T06:29:40.049921+00:00
instance: kb-cron
parent: why-most-published-research-findings-are-false-20260505-0
summary:
"Why Most Published Research Findings Are False" is a 2005 essay written by John Ioannidis, a professor at the Stanford School of Medicine, and published in PLOS Medicine. It is considered foundational to the field of metascience. In the paper, Ioannidis argued that a large number, if not the majority, of published medical research papers contain results that cannot be replicated. In simple terms, the essay states that scientists use hypothesis testing to determine whether scientific discoveries are significant. Statistical significance is formalized in terms of probability, with its p-value measure being reported in the scientific literature as a screening mechanism. Ioannidis posited assumptions about the way people perform and report these tests; then he constructed a statistical model which indicates that most published findings are likely false positive results. While the general arguments in the paper recommending reforms in scientific research methodology were well-received, Ionnidis received criticism for the validity of his model and his claim that the majority of scientific findings are false. Responses to the paper suggest lower false positive and false negative rates than what Ioannidis puts forth. == Argument == Suppose that in a given scientific field there is a known baseline probability that a


id: why-most-published-research-findings-are-false-20260505-1
file: data/en.wikipedia.org/wiki/Why_Most_Published_Research_Findings_Are_False-1.md
category: reference
title: Why Most Published Research Findings Are False
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Why_Most_Published_Research_Findings_Are_False
date_saved: 2026-05-05T06:29:40.049921+00:00
instance: kb-cron
parent: why-most-published-research-findings-are-false-20260505-0
summary:
== Reception == Despite skepticism about extreme statements made in the paper, Ioannidis's broader argument and warnings have been accepted by a large number of researchers. The growth of metascience and the recognition of a scientific replication crisis have bolstered the paper's credibility, and led to calls for methodological reforms in scientific research. In commentaries and technical responses, statisticians Goodman and Greenland identified several weaknesses in Ioannidis' model. Ioannidis's use of dramatic and exaggerated language that he "proved" that most research findings' claims are false and that "most research findings are false for most research designs and for most fields" [italics added] was rejected, and yet they agreed with his paper's conclusions and recommendations. Biostatisticians Jager and Leek criticized the model as being based on justifiable but arbitrary assumptions rather than empirical data, and did an investigation of their own which calculated that the false positive rate in biomedical studies was estimated to be around 14%, not over 50% as Ioannidis asserted. Their paper was published in a 2014 special edition of the journal Biostatistics along with extended, supporting critiques from other statisticians. Leek summarized the key points of agreement as: when talking about the science-wise false discovery rate one


id: woozle-effect-20260505-0
file: data/en.wikipedia.org/wiki/Woozle_effect-0.md
category: reference
title: Woozle effect
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Woozle_effect
date_saved: 2026-05-05T06:29:41.522778+00:00
instance: kb-cron
parent: woozle-effect-20260505-0
summary:
The Woozle effect, also known as evidence by citation, occurs when a source is widely cited for a claim that the source does not adequately support, giving said claim undeserved credibility. If results are not replicated and no one notices that a key claim was never well-supported in its original publication, faulty assumptions may affect further research. The Woozle effect is somewhat similar to circular reporting in journalism, where someone makes a questionable claim, and a journalist unthinkingly accepts the claim and republishes it without realizing its dubious and unreliable origins. In turn, other journalists and the public then continue to repeat and duplicate the unsupported claim. == Origin and definition == A Woozle is an imaginary character in the A. A. Milne book Winnie-the-Pooh, published in 1926. In chapter three, "In which Pooh and Piglet Go Hunting and Nearly Catch a Woozle", Winnie-the-Pooh and Piglet start following tracks left in snow believing they are the tracks of an imaginary animal called a woozle. The tracks keep multiplying until Christopher Robin explains to them that they have been following their own tracks in circles around a spinney. Prior to the introduction of the specific term "Woozle effect", the underlying concept


id: woozle-effect-20260505-1
file: data/en.wikipedia.org/wiki/Woozle_effect-1.md
category: reference
title: Woozle effect
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Woozle_effect
date_saved: 2026-05-05T06:29:41.522778+00:00
instance: kb-cron
parent: woozle-effect-20260505-0
summary:
== Examples == In 1980, Gelles illustrated the Woozle effect, showing how work by Gelles (1974) based on a small sample and published in The Violent Home by Straus, who had written the foreword for Gelles's book, was presented as if it applied to a large sample. Both of these were then cited by Langley & Levy in their 1977 book, Wife Beating: The Silent Crisis. In the 1998 book Intimate Violence, Gelles and Straus use the Winnie-the-Pooh woozle to illustrate how poor practice in research and self-referential research causes older research to be taken as fresh evidence causing error and bias. One notable example of the effect can be seen in citations of "Addiction Rare in Patients Treated with Narcotics", a letter to the editor by Jane Porter and Hershel Jick published by the New England Journal of Medicine in 1980. The letter, which was five sentences long and unlikely to have been peer reviewed according to a NEJM spokesperson, reported findings from analysis of medical records regarding the use of pain medication for hospital patients and concluded that "despite widespread use of narcotic drugs in hospitals, the development of addiction is rare in medical patients with no history


id: natural-history-20260505-0
file: data/en.wikipedia.org/wiki/Natural_history-0.md
category: reference
title: Natural history
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Natural_history
date_saved: 2026-05-05T06:29:43.333493+00:00
instance: kb-cron
parent: natural-history-20260505-0
summary:
Natural history is a domain of inquiry involving organisms, including animals, fungi, and plants, in their natural environment, leaning more towards observational than experimental methods of study. A person who studies natural history is called a naturalist or natural historian. Natural history encompasses scientific research but is not limited to it. It involves the systematic study of any category of natural objects or organisms, so while it dates from studies in the ancient Greco-Roman world and the medieval Arab world, through to European Renaissance naturalists working in near isolation, today's natural history is a cross-discipline umbrella of many specialty sciences; e.g., geobiology has a strong multidisciplinary nature. == Definitions == === Before 1900 === The meaning of the English term "natural history" (a calque of the Latin historia naturalis) has narrowed progressively with time, while, by contrast, the meaning of the related term "nature" has widened (see also History below). In antiquity, "natural history" covered essentially anything connected with nature, or used materials drawn from nature, such as Pliny the Elder's encyclopedia of this title, published c. 77 to 79 AD, which covers astronomy, geography, humans and their technology, medicine, and superstition, as well as animals and plants. Medieval European


id: natural-history-20260505-1
file: data/en.wikipedia.org/wiki/Natural_history-1.md
category: reference
title: Natural history
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Natural_history
date_saved: 2026-05-05T06:29:43.333493+00:00
instance: kb-cron
parent: natural-history-20260505-0
summary:
=== Prehistory === Prior to the advent of Western science, humans were engaged and highly competent in indigenous ways of understanding the more-than-human world that are now referred to as traditional ecological knowledge. 21st century definitions of natural history are inclusive of this understanding, such as this by Thomas Fleischner of the Natural History Institute (Prescott, Arizona): Natural history – a practice of intentional focused attentiveness and receptivity to the more-than-human world, guided by honesty and accuracy – is the oldest continuous human endeavor. In the evolutionary past of our species, the practice of natural history was essential for our survival, imparting critical information on habits and chronologies of plants and animals that we could eat or that could eat us. Natural history continues to be critical to human survival and thriving. It contributes to our fundamental understanding of how the world works by providing the empirical foundation of natural sciences, and it contributes directly and indirectly to human emotional and physical health, thereby fostering healthier human communities. It also serves as the basis for all conservation efforts, with natural history both informing the science and inspiring the values that drive these. === Ancient === As a precursor to Western


id: natural-history-20260505-2
file: data/en.wikipedia.org/wiki/Natural_history-2.md
category: reference
title: Natural history
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Natural_history
date_saved: 2026-05-05T06:29:43.333493+00:00
instance: kb-cron
parent: natural-history-20260505-0
summary:
Natural history museums, which evolved from cabinets of curiosities, played an important role in the emergence of professional biological disciplines and research programs. Particularly in the 19th century, scientists began to use their natural history collections as teaching tools for advanced students and the basis for their own morphological research. == Societies == The term "natural history" alone, or sometimes together with archaeology, forms the name of many national, regional, and local natural history societies that maintain records for animals—including birds (ornithology), insects (entomology) and mammals (mammalogy)—fungi (mycology), plants (botany), and other organisms. They may also have geological and microscopical sections. Examples of these societies in Britain include the Natural History Society of Northumbria founded in 1829, London Natural History Society (1858), Birmingham Natural History Society (1859), British Entomological and Natural History Society founded in 1872, Glasgow Natural History Society, Manchester Microscopical and Natural History Society established in 1880, Whitby Naturalists' Club founded in 1913, Scarborough Field Naturalists' Society and the Sorby Natural History Society, Sheffield, founded in 1918. The growth of natural history societies was also spurred due to the growth of British colonies in tropical regions with numerous new species to be discovered. Many civil servants took an


id: list-of-natural-history-dealers-20260505-0
file: data/en.wikipedia.org/wiki/List_of_natural_history_dealers-0.md
category: reference
title: List of natural history dealers
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/List_of_natural_history_dealers
date_saved: 2026-05-05T06:29:44.538558+00:00
instance: kb-cron
parent: list-of-natural-history-dealers-20260505-0
summary:
Natural history specimen dealers had an important role in the development of science in the 18th, 19th and early 20th centuries. They supplied the rapidly growing, both in size and number, museums and educational establishments and private collectors whose collections, either in entirety or parts finally entered museums. Most sold not just zoological, botanical and geological specimens but also equipment and books. Many also sold archaeological and ethnographic items. They purchased specimens from professional and amateur collectors, sometimes collected themselves as well as acting as agents for the sale of collections. Many were based in mercantile centres notably Amsterdam, Hamburg, and London or in major cities. Some were specialists and some were taxonomic authorities who wrote scientific works and manuals, some functioned as trading museums or institutes. This is a list of natural history dealers from the 16th to the 19th century: here are names that are frequently encountered in museum collections. Johan Hans Abegg (fl. 1882–1885) Mineral collector and dealer in Zurich. Augustus Theodore Abel (?1802-1882); German Mineral dealer resident in Ballarat. Anton Franz Abraham Preparator and dealer in educational materials at " "Naturhistorisches Institut" on Beatrixgasse, Vienna, 1896, on Ungargasse, Vienna in 1903–1906.Supplied specimens to Archduke Franz Ferdinand.


id: list-of-natural-history-dealers-20260505-1
file: data/en.wikipedia.org/wiki/List_of_natural_history_dealers-1.md
category: reference
title: List of natural history dealers
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/List_of_natural_history_dealers
date_saved: 2026-05-05T06:29:44.538558+00:00
instance: kb-cron
parent: list-of-natural-history-dealers-20260505-0
summary:
Edmund Reitter "Natural History Institute" 1879 -1880 Vienna, 1881-1891 Mödling, after 1891 Paskau and Munich (extant). Carl Ribbe Heinrich Ribbe (1832–1898) Entomologist and dealer in Berlin Hermann Rolle Berlin William Frederick Henry Rosenberg (1868–1957) 57 Haverstock Hill, London fl. 1920s. Claimed to hold 5,000 bird species as scientific skins (and to be the largest bird skin dealership in the world). Supplier to museums and private collectors. Traveller. Emil Adolf Rossmässler Natural History dealer Karl Rost Fritz Rühl Auguste Sallé L.W. Schaufuß else E. Klocke, Dresden Christian Julius Wilhelm Schiede Wilhelm Schlüter Gustav Schneider (1867–1958) Basel Gustav Schrader Wilhelm Schlüter Abraham Scrivener (fl. 1840s) naturalist and merchant with a shop at Deptford Bridge, Greenwich. Paul Smart Southwick & Jencks’ Natural History Store Providence, Rhode Island Otto Staudinger Stazione Zoologica Anton Dohrn Marine specimens. Wilh. Steeg "Dr. Steeg & Reuter" after 1879. Crystallographic microscope slides. Alexandre Stuer(fl. 1890s-1920?) Paris mineral dealer. Owner of Comptoir Géologique et Minéralogique, 40, rue de Mathurins and at 4, rue de Castellane. John Crace Stevens Covent Gardens auctioneer. Emanuel Sweerts (1552–1612) Dutch merchant and natural history dealer. Rudolf Tancré (1842–1934) Anklam, Pomerania Dealer in Lepidoptera mainly of Central Asia and Siberia. Georg Thorey - Hamburg pharmacist and


id: affinity-taxonomy-20260505-0
file: data/en.wikipedia.org/wiki/Affinity_(taxonomy)-0.md
category: reference
title: Affinity (taxonomy)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Affinity_(taxonomy)
date_saved: 2026-05-05T06:29:45.754091+00:00
instance: kb-cron
parent: affinity-taxonomy-20260505-0
summary:
Affinity (taxonomy) – mainly in life sciences or natural history – refers to resemblance suggesting a common descent, phylogenetic relationship, or type. The term does, however, have broader application, such as in geology (for example, in descriptive and theoretical works), and similarly in astronomy (for example, see "Centaur object" in the context of 2060 Chiron's close affinity with icy comet nuclei.) == Basis == In taxonomy the basis of any particular type of classification is the way in which objects in the domain resemble each other. A resemblance of a type that seems appropriate to the classification that we propose, we may call an affinity, and when we decide how to classify say, a specimen of rock or butterfly, we justify our decision according to the affinities that we observe. Other resemblances we dismiss as being out of context or at least non-cogent; for example, in deciding whether to classify a lizard as having closer affinities to a snake than to a table, biologists rely on affinities such as the scales, blood, physiology, vertebral anatomy, and reproductive system as being more relevant than the possession of four "feet". == Application and obstacles == Analysing and determining the proper classification of


id: alfred-russel-wallace-centenary-20260505-0
file: data/en.wikipedia.org/wiki/Alfred_Russel_Wallace_centenary-0.md
category: reference
title: Alfred Russel Wallace centenary
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Alfred_Russel_Wallace_centenary
date_saved: 2026-05-05T06:29:47.023875+00:00
instance: kb-cron
parent: alfred-russel-wallace-centenary-20260505-0
summary:
The centenary of the death of the naturalist Alfred Russel Wallace on 7 November 1913 was marked in 2013 with events around the world to celebrate his life and work. The commemorations was co-ordinated by the Natural History Museum, London. Events between October 2013 and June 2014 were planned by the Natural History Museum and other organisations including the Zoological Society of London, Cardiff University, the University of Alberta, Dorset County Museum, Swansea Museum, Dorset Wildlife Trust, Ness Botanical Gardens (South Wirral), the Royal Society, the Linnean Society, the Harvard Museum of Natural History, the American Museum of Natural History, Hertford Museum and the National Museum of Wales. == Context == The naturalist, explorer, geographer, anthropologist and biologist Alfred Russel Wallace (born 8 January 1823) died on 7 November 1913. He is principally remembered now for having independently conceived the theory of evolution through natural selection, which prompted Charles Darwin to publish On the Origin of Species. Some of his books such as The Malay Archipelago remain in print; it is considered one of the best accounts of scientific exploration published during the 19th century. Wallace is also remembered for recognizing the presence of a biogeographical boundary, now known as


id: botanical-expedition-20260505-0
file: data/en.wikipedia.org/wiki/Botanical_expedition-0.md
category: reference
title: Botanical expedition
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Botanical_expedition
date_saved: 2026-05-05T06:29:48.362768+00:00
instance: kb-cron
parent: botanical-expedition-20260505-0
summary:
A botanical expedition (sometimes called "Plant hunting") is a scientific voyage designed to explore the flora of a particular region, either as a specific design or part of a larger expedition. A naturalist or botanist would be responsible for identification, description and collection of specimens. In some cases the plants might be collected by the person in the field, but described and named by a government sponsored scientist at a botanical garden or university. For example, species collected on the Lewis and Clark Expedition were described and named by Frederick Traugott Pursh. While accounts of plant collection occur in antiquity, a scientific basis occurred during the Renaissance and was associated with the establishment of botanical gardens and the teaching of botany as a discipline. The practice of botanical expeditions reached a peak in the late 18th and during the 19th century with the systematic organisation of plants into taxonomic classifications. Plant collection has attracted a number of criticisms of exploitation and colonialism leading to the establishment of international regulations and safeguards. == Description == Botanical expeditions have often been referred to as "plant hunting" (or less commonly "botanomania"). They are mainly scientific journeys or voyages designed to explore the flora


id: conservation-and-restoration-of-insect-specimens-20260505-0
file: data/en.wikipedia.org/wiki/Conservation_and_restoration_of_insect_specimens-0.md
category: reference
title: Conservation and restoration of insect specimens
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Conservation_and_restoration_of_insect_specimens
date_saved: 2026-05-05T06:29:49.658173+00:00
instance: kb-cron
parent: conservation-and-restoration-of-insect-specimens-20260505-0
summary:
The conservation and restoration of insect specimens is the process of caring for and preserving insects as a part of a collection. Conservation concerns begin at collection and continue through preparation, storage, examination, documentation, research and treatment when restoration is needed. == Collection == Insect collecting can be done in many different ways depending on the kind of insects being collected and from which habitats. Both hobbyists and professional entomologist have found particular ways to collect with minimal damage to their specimens. Following established techniques helps begin the conservation of insect specimens from the beginning by eliminating as much potential damage as possible. It must be done delicately to ensure that neither the collector nor the live insect itself will cause harm to the distinctive features such as wings, legs and antennae that give purpose to the collection. Special collection nets, traps and techniques must be utilized in consideration of how easily breakage can happen. A kill jar is often used to immediately immobilize the insect before it can damage itself. == Preparation == The way an insect specimen is prepared is often the first step in their conservation. They have to be carefully prepared with the appropriate methods depending


id: conservation-and-restoration-of-insect-specimens-20260505-1
file: data/en.wikipedia.org/wiki/Conservation_and_restoration_of_insect_specimens-1.md
category: reference
title: Conservation and restoration of insect specimens
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Conservation_and_restoration_of_insect_specimens
date_saved: 2026-05-05T06:29:49.658173+00:00
instance: kb-cron
parent: conservation-and-restoration-of-insect-specimens-20260505-0
summary:
==== Safe handling ==== Proper handling for insect specimens prevents the excessive breaking of legs, antennae and other body parts that could then easily be lost. Curved forceps may be used to allow more precision and less chance of the brittle specimen coming in contact with the handler. The handler picks up the specimen by the pin, which is placed with enough space below the specimen for the handler to put in the pinning block and enough space above to grip without touching the specimen. ==== Integrated pest management ==== Integrated pest management (IPM) is a specialized modern pest control used in museums. All IPM systems begin with regular sanitation and monitoring of collections to detect castings from various pests, and checking insect traps laid out to capture and identify which pests are present. Some pests, such as carpet beetles and flour beetles, feed on dried insects. When an infestation is present, treatment may be necessary. Freezing is commonly used to rid insect collections of pests. Alternatively, inert gases may be used for an anoxic fumigation – depriving the pests of oxygen to exterminate, and in extreme cases chemical fumigation proven to be safe for collections and people may be


id: cymdeithas-edward-llwyd-20260505-0
file: data/en.wikipedia.org/wiki/Cymdeithas_Edward_Llwyd-0.md
category: reference
title: Cymdeithas Edward Llwyd
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Cymdeithas_Edward_Llwyd
date_saved: 2026-05-05T06:29:50.979654+00:00
instance: kb-cron
parent: cymdeithas-edward-llwyd-20260505-0
summary:
Cymdeithas Edward Llwyd (Welsh for 'Edward Llwyd Society') is a Welsh natural history organisation whose name commemorates the great Welsh natural historian, geographer and linguist Edward Llwyd (1660–1709). The Cymdeithas Edward Llwyd organises regular country walks throughout Wales in sites of interest of the Welsh environment, including SSSIs and post-industrial landscapes. These are Welsh-language walking groups, although learners are just as welcome. They also organise a variety of nature and environmental activities, including lectures, publications and conservation work. They have worked on collecting and documenting the names of natural species. In 2010, the society held a conference on Welsh toponymy, at which it was agreed to form the Welsh Place-Name Society. == References == == External links == Official site


id: field-guide-20260505-0
file: data/en.wikipedia.org/wiki/Field_guide-0.md
category: reference
title: Field guide
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Field_guide
date_saved: 2026-05-05T06:29:52.197836+00:00
instance: kb-cron
parent: field-guide-20260505-0
summary:
A field guide is a book designed to help the reader identify wildlife (flora or fauna or funga) or other objects of natural occurrence (e.g. rocks and minerals). It is generally designed to be brought into the "field" or local area where such objects exist to help distinguish between similar objects. Field guides are often designed to help users distinguish animals and plants that may be similar in appearance but are not necessarily closely related. It will typically include a description of the objects covered, together with paintings or photographs and an index. More serious and scientific field identification books, including those intended for students, will probably include identification keys to assist with identification, but the publicly accessible field guide is more often a browsable picture guide organized by family, colour, shape, location or other descriptors. == History == Popular interests in identifying things in nature probably were strongest in bird and plant guides. Perhaps the first popular field guide to plants in the United States was the 1893 How to Know the Wildflowers by "Mrs. William Starr Dana" (Frances Theodora Parsons). In 1890, Florence Merriam published Birds Through an Opera-Glass, describing 70 common species. Focused on living birds observed


id: flying-and-gliding-animals-20260505-0
file: data/en.wikipedia.org/wiki/Flying_and_gliding_animals-0.md
category: reference
title: Flying and gliding animals
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Flying_and_gliding_animals
date_saved: 2026-05-05T06:29:53.459168+00:00
instance: kb-cron
parent: flying-and-gliding-animals-20260505-0
summary:
A number of animals are capable of aerial locomotion, either by active flight, by passive gliding or, in rare occasions, by kiting/ballooning. Such animals typically have appendages that interact with air to generate lift in order to overcome the weight of their own body and any payload they are carrying (e.g. food/prey, nesting materials). Majority of flying and glide animals are terrestrial, while species from one extant taxon, i.e. the flying fish, are aquatic. The ability to fly or glide has appeared via convergent evolution many times throughout the history of life, and has evolved prominently in at least four terrestrial clades: insects, pterosaurs, birds and bats. Gliding and kiting, which are essentially controlled, prolonged free falls, have evolved on many more occasions, especially among arboreal species. Usually the aerial trait is to aid animals leaping directly across extended distances from one tree canopy to another without having to descend to the ground, which will otherwise expose them to risks from ground predators, although there are other reasons why aerial locomotions have developed. Gliding, in particular, has evolved among forest animals, especially in the rainforests in Asia (most especially Borneo) where the trees are tall (making it costly to descend


id: flying-and-gliding-animals-20260505-1
file: data/en.wikipedia.org/wiki/Flying_and_gliding_animals-1.md
category: reference
title: Flying and gliding animals
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Flying_and_gliding_animals
date_saved: 2026-05-05T06:29:53.459168+00:00
instance: kb-cron
parent: flying-and-gliding-animals-20260505-0
summary:
=== Gliding and parachuting === While gliding occurs independently from powered flight, it has some ecological advantages of its own as it is the simplest form of flight. Gliding is a very energy-efficient way of travelling from tree to tree. Although moving through the canopy running along the branches may be less energetically demanding, the faster transition between trees allows for greater foraging rates in a particular patch. Glide ratios can be dependent on size and current behavior. Higher foraging rates are supported by low glide ratios as smaller foraging patches require less gliding time over shorter distances and greater amounts of food can be acquired in a shorter time period. Low ratios are not as energy efficient as the higher ratios, but an argument made is that many gliding animals eat low energy foods such as leaves and are restricted to gliding because of this, whereas flying animals eat more high energy foods such as fruits, nectar, and insects. Mammals tend to rely on lower glide ratios to increase the amount of time foraging for lower energy food. An equilibrium glide, achieving a constant airspeed and glide angle, is harder to obtain as animal size increases. Larger animals need


id: flying-and-gliding-animals-20260505-2
file: data/en.wikipedia.org/wiki/Flying_and_gliding_animals-2.md
category: reference
title: Flying and gliding animals
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Flying_and_gliding_animals
date_saved: 2026-05-05T06:29:53.459168+00:00
instance: kb-cron
parent: flying-and-gliding-animals-20260505-0
summary:
Powered flight has evolved unambiguously only four times—birds, bats, pterosaurs, and insects (though see above for possible independent acquisitions within bird and bat groups). In contrast to gliding, which has evolved more frequently but typically gives rise to only a handful of species, all three extant groups of powered flyers have a huge number of species, suggesting that flight is a very successful strategy once evolved. Bats, after rodents, have the most species of any mammalian order, about 20% of all mammalian species. Birds have the most species of any class of terrestrial vertebrates. Finally, insects (most of which fly at some point in their life cycle) have more species than all other animal groups combined. The evolution of flight is one of the most striking and demanding in animal evolution, and has attracted the attention of many prominent scientists and generated many theories. Additionally, because flying animals tend to be small and have a low mass (both of which increase the surface-area-to-mass ratio), they tend to fossilize infrequently and poorly compared to the larger, heavier-boned terrestrial species they share habitat with. Fossils of flying animals tend to be confined to exceptional fossil deposits formed under highly specific circumstances, resulting


id: flying-and-gliding-animals-20260505-3
file: data/en.wikipedia.org/wiki/Flying_and_gliding_animals-3.md
category: reference
title: Flying and gliding animals
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Flying_and_gliding_animals
date_saved: 2026-05-05T06:29:53.459168+00:00
instance: kb-cron
parent: flying-and-gliding-animals-20260505-0
summary:
Unlike most air vehicles, in which the objects that generate lift (wings) and thrust (engine or propeller) are separate and the wings remain fixed, flying animals use their wings to generate both lift and thrust by moving them relative to the body. This has made the flight of organisms considerably harder to understand than that of vehicles, as it involves varying speeds, angles, orientations, areas, and flow patterns over the wings. A bird or bat flying through the air at a constant speed moves its wings up and down (usually with some fore-aft movement as well). Because the animal is in motion, there is some airflow relative to its body which, combined with the velocity of its wings, generates a faster airflow moving over the wing. This will generate lift force vector pointing forwards and upwards, and a drag force vector pointing rearwards and upwards. The upwards components of these counteract gravity, keeping the body in the air, while the forward component provides thrust to counteract both the drag from the wing and from the body as a whole. Pterosaur flight likely worked in a similar manner, though no living pterosaurs remain for study. Insect flight is considerably different, due


id: flying-and-gliding-animals-20260505-4
file: data/en.wikipedia.org/wiki/Flying_and_gliding_animals-4.md
category: reference
title: Flying and gliding animals
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Flying_and_gliding_animals
date_saved: 2026-05-05T06:29:53.459168+00:00
instance: kb-cron
parent: flying-and-gliding-animals-20260505-0
summary:
==== Most maneuverable glider ==== Many gliding animals have some ability to turn, but which is the most maneuverable is difficult to assess. Even paradise tree snakes, Chinese gliding frogs, and gliding ants have been observed as having considerable capacity to turn in the air. == Flying animals == === Extant === ==== Insects ==== Pterygota: The first of all animals to evolve flight, they are also the only invertebrates that have evolved flight. As they comprise almost all insects, the species are too numerous to list here. Insect flight is an active research field. ==== Birds ==== Birds (flying, soaring) – Most of the approximately 10,000 living species can fly (flightless birds are the exception). Bird flight is one of the most studied forms of aerial locomotion in animals. See List of soaring birds for birds that can soar as well as fly. ==== Mammals ==== Bats. There are approximately 1,240 bat species, representing about 20% of all classified mammal species. Most bats are nocturnal and many feed on insects while flying at night, using echolocation to home in on their prey. === Extinct === ==== Reptile: Pterosaurs ==== Pterosaurs were the first flying vertebrates, and are generally agreed


