# kb

id: royal-commission-on-animal-magnetism-20260505-14
file: data/en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism-14.md
category: reference
title: Royal Commission on Animal Magnetism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism
date_saved: 2026-05-05T03:45:20.529918+00:00
instance: kb-cron
parent: royal-commission-on-animal-magnetism-20260505-0
summary:
=== Response of the Paris Faculty of Medicine === Immediately following the release of the reports of the two Commissions, the Paris Faculty of Medicine "pressure[d] its own members to renounce animal magnetism" (Crabtree, 1993, p. 32). The Faculty identified some thirty of its docteurs-régent, including François Louis Thomas d'Onglée and Charles-Louis Varnier, who "openly favored animal magnetism or were suspected of so doing". According to the contemporary account of Thomas d'Onglée (1785, passim), the thirty "magnetic physicians" were subjected to "abuse" and were presented with a declaration, of which it was demanded that they sign. Both Thomas d'Onglée and Varnier, among others, refused to sign the declaration (and were, thereby, immediately expelled). The declaration in question read: No Doctor may declare himself a partisan of animal magnetism, through writings or through practice, under penalty of being removed from the role of docteurs-régents. === D'Eslon's response === D'Eslon immediately published an attack on the commission's reports, in which he criticized their failure to investigate the longer-term effects of his treatments, and their refusal to accept his (alleged) "cures" as proof of the existence of "animal magnetism", as well as noting that, "the commissioners' recommendation that the practice of magnetism should


id: royal-commission-on-animal-magnetism-20260505-15
file: data/en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism-15.md
category: reference
title: Royal Commission on Animal Magnetism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism
date_saved: 2026-05-05T03:45:20.529918+00:00
instance: kb-cron
parent: royal-commission-on-animal-magnetism-20260505-0
summary:
The "classic" structure of the investigations undertaken by the "Franklin" Commission inspired – among many others over the ensuing years – the (1799) investigations of Chester physician John Haygarth (1740–1827) into the efficacy of Perkins' "tractors". In the process of discussing the experiments he had conducted (with medical colleagues as witnesses) with (dummy) "wooden tractors" on 7 January 1799, and with Perkins' "true metallick tractors" on 8 January 1799, Haygarth emphasized his considerable debt to the (earlier) "Franklin Commission" enterprise: It need not be remarked, how completely the trial illustrates the nature of this popular illusion, which has so wonderfully prevailed, and spread so rapidly; it resembles, in a striking manner, that of Animal Magnetism, which merited the attention of Dr. Franklin, when ambassador from America, and of other philosophers at Paris. If any person would repeat these experiments, they should be performed with due solemnity. During the process, the wonderful cures which this remedy is said to have performed ought to be particularly related. Without these indispensable aids, other trials will not prove as successful as those which are above reported. The whole effect undoubtedly depends upon the impression which can be made upon the patient's Imagination. This method


id: royal-commission-on-animal-magnetism-20260505-16
file: data/en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism-16.md
category: reference
title: Royal Commission on Animal Magnetism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism
date_saved: 2026-05-05T03:45:20.529918+00:00
instance: kb-cron
parent: royal-commission-on-animal-magnetism-20260505-0
summary:
The noted neurologist and psychoanalyst Sigmund Freud not only studied and wrote about "hypnosis" (e.g., Freud, 1891, and 1966), but he also actively used "hypnosis" in his clinical practice for some time. However the (à la Salpêtrière) "hypnosis" that Freud employed – quite unlike the conventional "hypnotism" of James Braid (that was induced by Braid's standard "upwards and inwards squint") – relied upon an induction process that often involved rubbing the top of a patient's head. This requirement, of course, demanded that Freud sat at the end of the therapeutic couch – in order to gain easy access to his subject's head – a practice that Freud continued to follow for his entire (post-"hypnosis") professional career. Another vestige of phreno-magnetism that demanded that Freud position himself at the patient's head was Freud's application of the "head pressure" technique that he had, in person, observed Hippolyte Bernheim use, on one of his visits to Bernheim's clinic at Nancy in 1899. Freud had discontinued this "head pressure" practice by, at least, 1904 – and, possibly, by 1900. === Mistaken identification of Esdaile's Jhar-Phoonk with d’Eslon's magnetization-by-contact === Due, to a large extent, to the (mistaken) enthusiastic promotion of Esdaile's (otherwise) valuable work


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: 2026-05-05T03:45:25.903358+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: 2026-05-05T03:45:25.903358+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: 2026-05-05T03:45:25.903358+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: 2026-05-05T03:45:25.903358+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: 2026-05-05T03:45:25.903358+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: 2026-05-05T03:45:25.903358+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: 2026-05-05T03:45:25.903358+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: 2026-05-05T03:45:25.903358+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: 2026-05-05T03:45:27.166660+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: science-on-the-verge-20260505-0
file: data/en.wikipedia.org/wiki/Science_on_the_Verge-0.md
category: reference
title: Science on the Verge
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Science_on_the_Verge
date_saved: 2026-05-05T03:45:28.491279+00:00
instance: kb-cron
parent: science-on-the-verge-20260505-0
summary:
Science on the verge is a book written in 2016 by group of eight scholars working in the tradition of Post-normal science. The book analyzes the main features and possible causes of the present science's crisis. == Content == Science on the verge, written by Alice Benessia, Silvio Funtowicz, Mario Giampietro, Ângela Guimarães Pereira, Jerome Ravetz, Andrea Saltelli, Roger Strand, and Jeroen P. van der Sluijs, with a preface by Dan Sarewitz, follows different threads of the present science's crisis (lost reproducibility, collapsing peer review, perverse metrics, evidence based policy's dysfunctions, techno-science and its hubris, science as metaphysics ...) and presents a first systematic analysis of the main causes of the present predicaments. Chapter 1. The chapter entitled 'Who will solve the crisis in science?' tackles the following questions: Is there a crisis? What is being done 'from within'? Is this sufficient? What are the diagnoses for the crisis' root causes, and what are the solutions 'from without'? Chapter 2. This is chapter entitled 'The fallacy of evidence based policy' tackles: Quantification as hypocognition; Socially constructed ignorance & uncomfortable knowledge; Ancien régime syndrome; Quantitative story telling. Chapter 3. This is chapter entitled 'Never late, never lost, never unprepared' treats: Trajectories


id: scientific-collection-20260505-0
file: data/en.wikipedia.org/wiki/Scientific_collection-0.md
category: reference
title: Scientific collection
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_collection
date_saved: 2026-05-05T03:45:29.768232+00:00
instance: kb-cron
parent: scientific-collection-20260505-0
summary:
A scientific collection is a collection of items that are preserved, catalogued, and managed for the purpose of scientific study. Scientific collections dealing specifically with organisms plants, fungi, animals, insects and their remains, may also be called natural history collections or biological collections. The latter may contain either living stocks or preserved repositories of biodiversity specimens and materials. Scientific collections hold a tangible portion of the cumulative evidence base in such fields as biology (especially taxonomy and evolutionary biology), geology, and archaeology. They may be stored and managed by governments, educational institutions (e.g. colleges and universities), private organizations (including museums), or individuals. Prominent uses of scientific collections include the systematic description and identification of biological species, the study and prediction of long-term historical trends (including impacts of climate change), the dating and analysis of historical objects (e.g. via wood samples and ice cores with annual rings), and the maintenance of teaching resources. == Indexing == The indexing of the collections was historically made by directories, catalogs, index cards, today supplemented by or replaced by databases with information such as e.g. scientific description, including picture, name, location, find circumstances, fund age, scientific analysis, phylogenetic relationships, DNA and isotope analysis results, analysis


id: scientific-collection-20260505-1
file: data/en.wikipedia.org/wiki/Scientific_collection-1.md
category: reference
title: Scientific collection
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_collection
date_saved: 2026-05-05T03:45:29.768232+00:00
instance: kb-cron
parent: scientific-collection-20260505-0
summary:
in France: Muséum National d'Histoire Naturelle, Paris contains 9,500,000 specimens; Université Montpellier contains 4,000,000 specimens; Université Claude Bernard Lyon 1 contains 4,000,000 specimens in Russia: Komarov Botanical Institute in St. Petersburg contains 7,160,000 specimens, in Great Britain: Royal Botanic Gardens Kew contains 7,000,000 specimens; British Museum of Natural History contains 80,000,000 specimens; Royal Botanic Garden Edinburgh contains 2,000,000 specimens; University of Cambridge contains 1,000,000 specimens; University of Manchester contains 1,000,000 specimens; in Switzerland: Conservatoire et Jardin botaniques de la Ville de Genève contains 6,000,000 specimens; Eidgenössische Technische Hochschule Zürich contains 1,500,000 specimens, in Austria: Naturhistorisches Museum Wien contains 5,000,000 specimens; Universität Wien contains 1,400,000 specimens, in Sweden: Swedish Museum of Natural History (Naturhistoriska riksmuseet) contains 4,400,000 specimens; Uppsala University contains 3,000,000 specimens, Botanical Museum, Lund contains 2,500,000 specimens, University of Gothenburg contains 1,600,000 specimens, in Netherlands: National Herbarium of the Netherlands (Nationaal Herbarium Nederland) contains 4,000,000 specimens in Italy: Museo di Storia Naturale dell'Università, Florence contains 3,650,000 specimens; Università degli Studi di Roma La Sapienza, Rome contains 1,120,000 specimens; Università degli Studi di Torino contains 1,000,000 specimens, in Belgium: National Botanic Garden of Belgium contains 3,500,000 specimens, in Germany: Botanischer Garten und Botanisches Museum Berlin-Dahlem, Zentraleinrichtung der Freien Universität Berlin


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: 2026-05-05T03:45:31.111708+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: 2026-05-05T03:45:31.111708+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: 2026-05-05T03:45:31.111708+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: 2026-05-05T03:45:31.111708+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: 2026-05-05T03:45:32.446115+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-enterprise-20260505-0
file: data/en.wikipedia.org/wiki/Scientific_enterprise-0.md
category: reference
title: Scientific enterprise
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_enterprise
date_saved: 2026-05-05T03:45:33.771589+00:00
instance: kb-cron
parent: scientific-enterprise-20260505-0
summary:
A scientific enterprise is a science-based project developed by, or in cooperation with, a private entrepreneur. For example, in the Age of Exploration, leaders like Henry the Navigator founded schools of navigation, from which stemmed voyages of exploration. == Examples of enterprising scientific organizations == Each organization listed below has the ability to conduct scientific research on an extended basis, involving multiple researchers over an extended time. Generally, the research is funded not only for the science itself, but for some application which shows promise for the enterprise. But the researchers, if left to their own choices, will tend to follow their research interest, which is essential for the long-term health of their chosen field. Note that a successful scientific enterprise is not equivalent to a successful high-tech enterprise or to a successful business enterprise, but that they form an ecology, a food chain. The Max Planck Institute, which supports fundamental research in the natural, life and social sciences, the arts and humanities The RAND Corporation, founded as a research corporation. Did groundbreaking work in the field of artificial intelligence The Jet Propulsion Laboratory, founded by Theodore von Kármán Bell Laboratories, renowned for the quality of its scientific work and


id: scientific-evidence-20260505-0
file: data/en.wikipedia.org/wiki/Scientific_evidence-0.md
category: reference
title: Scientific evidence
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_evidence
date_saved: 2026-05-05T03:45:34.945725+00:00
instance: kb-cron
parent: scientific-evidence-20260505-0
summary:
Scientific evidence is evidence that serves to either support or counter a scientific theory or hypothesis, although scientists also use evidence in other ways, such as when applying theories to practical problems. Such evidence is expected to be empirical evidence and interpretable in accordance with the scientific method. Standards for scientific evidence vary according to the field of inquiry, but the strength of scientific evidence is generally based on the results of statistical analysis and the strength of scientific controls. == Principles of inference == A person's assumptions or beliefs about the relationship between observations and a hypothesis will affect whether that person takes the observations as evidence. These assumptions or beliefs will also affect how a person utilizes the observations as evidence. For example, the Earth's apparent lack of motion may be taken as evidence for a geocentric cosmology. However, after sufficient evidence is presented for heliocentric cosmology and the apparent lack of motion is explained, the initial observation is strongly discounted as evidence. When rational observers have different background beliefs, they may draw different conclusions from the same scientific evidence. For example, Priestley, working with phlogiston theory, explained his observations about the decomposition of mercuric oxide using phlogiston.


id: scientific-evidence-20260505-1
file: data/en.wikipedia.org/wiki/Scientific_evidence-1.md
category: reference
title: Scientific evidence
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_evidence
date_saved: 2026-05-05T03:45:34.945725+00:00
instance: kb-cron
parent: scientific-evidence-20260505-0
summary:
== Philosophical versus scientific views == In the 20th century, many philosophers investigated the logical relationship between evidence statements and hypotheses, whereas scientists tended to focus on how the data used for statistical inference are generated. But according to philosopher Deborah Mayo, by the end of the 20th century philosophers had come to understand that "there are key features of scientific practice that are overlooked or misdescribed by all such logical accounts of evidence, whether hypothetico-deductive, Bayesian, or instantiationist". There were a variety of 20th-century philosophical approaches to decide whether an observation may be considered evidence; many of these focused on the relationship between the evidence and the hypothesis. In the 1950s, Rudolf Carnap recommended distinguishing such approaches into three categories: classificatory (whether the evidence confirms the hypothesis), comparative (whether the evidence supports a first hypothesis more than an alternative hypothesis) or quantitative (the degree to which the evidence supports a hypothesis). A 1983 anthology edited by Peter Achinstein provided a concise presentation by prominent philosophers on scientific evidence, including Carl Hempel (on the logic of confirmation), R. B. Braithwaite (on the structure of a scientific system), Norwood Russell Hanson (on the logic of discovery), Nelson Goodman (of grue fame,


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: 2026-05-05T03:45:36.172732+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-imperialism-20260505-0
file: data/en.wikipedia.org/wiki/Scientific_imperialism-0.md
category: reference
title: Scientific imperialism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_imperialism
date_saved: 2026-05-05T03:45:37.416247+00:00
instance: kb-cron
parent: scientific-imperialism-20260505-0
summary:
Scientific imperialism is a term that appears to have been coined by Ellis T. Powell when addressing the Commonwealth Club of Canada on 8 September 1920. He defined imperialism as "the sense of arbitrary and capricious domination over the bodies and souls of men," he used the term "scientific imperialism" to mean "the subjection of all the developed and undeveloped powers of the earth to the mind of man." In modern usage, however, scientific imperialism refers to situations in which critics perceive science to act imperiously. Philosopher of science John Dupré described it (in his 2001 book Human Nature and the Limits of Science, p. 74) as "the tendency to push a good scientific idea far beyond the domain in which it was originally introduced, and often far beyond the domain in which it can provide much illumination." He wrote that "devotees of these approaches are inclined to claim that they are in possession not just of one useful perspective on human behavior, but of the key that will open doors to the understanding of ever wider areas of human behavior." Scientific imperialism has also been charged against "those who believe that the study of politics can and should be


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: 2026-05-05T03:45:42.549163+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: 2026-05-05T03:45:42.549163+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: 2026-05-05T03:45:42.549163+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: 2026-05-05T03:45:42.549163+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: 2026-05-05T03:45:42.549163+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: 2026-05-05T03:45:42.549163+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: 2026-05-05T03:45:42.549163+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: 2026-05-05T03:45:43.771670+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: 2026-05-05T03:45:43.771670+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: 2026-05-05T03:45:43.771670+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: 2026-05-05T03:45:43.771670+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: 2026-05-05T03:45:43.771670+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: 2026-05-05T03:45:43.771670+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: 2026-05-05T03:45:44.912301+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-protocol-20260505-0
file: data/en.wikipedia.org/wiki/Scientific_protocol-0.md
category: reference
title: Scientific protocol
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_protocol
date_saved: 2026-05-05T03:45:46.143301+00:00
instance: kb-cron
parent: scientific-protocol-20260505-0
summary:
In natural and social science research, a protocol is most commonly a predefined procedural method in the design and implementation of an experiment. Protocols are written whenever it is desirable to standardize a laboratory method to ensure successful replication of results by others in the same laboratory or by other laboratories. Additionally, and by extension, protocols have the advantage of facilitating the assessment of experimental results through peer review. In addition to detailed procedures, equipment, and instruments, protocols will also contain study objectives, reasoning for experimental design, reasoning for chosen sample sizes, safety precautions, and how results were calculated and reported, including statistical analysis and any rules for predefining and documenting excluded data to avoid bias. Similarly, a protocol may refer to the procedural methods of health organizations, commercial laboratories, manufacturing plants, etc. to ensure their activities (e.g., blood testing at a hospital, testing of certified reference materials at a calibration laboratory, and manufacturing of transmission gears at a facility) are consistent to a specific standard, encouraging safe use and accurate results. Finally, in the field of social science, a protocol may also refer to a "descriptive record" of observed events or a "sequence of behavior" of one or more


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: 2026-05-05T03:45:47.413689+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: 2026-05-05T03:45:48.591816+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: 2026-05-05T03:45:49.802572+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: 2026-05-05T03:45:49.802572+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: 2026-05-05T03:45:49.802572+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: 2026-05-05T03:45:49.802572+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: 2026-05-05T03:45:49.802572+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: 2026-05-05T03:45:49.802572+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: 2026-05-05T03:45:49.802572+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: scientism-20260505-0
file: data/en.wikipedia.org/wiki/Scientism-0.md
category: reference
title: Scientism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientism
date_saved: 2026-05-05T03:45:51.099471+00:00
instance: kb-cron
parent: scientism-20260505-0
summary:
Scientism is the belief that science and the scientific method are the best or only way to render truth about the world and reality. While the term was defined originally to mean "methods and attitudes typical of or attributed to natural scientists", some scholars, as well as political and religious leaders, have also adopted it as a pejorative term with the meaning "an exaggerated trust in the efficacy of the methods of natural science applied to all areas of investigation (as in philosophy, the social sciences, and the humanities)". == Overview == Francis Bacon has been viewed by some scholars as an early proponent of scientism, but this is a modern assertion as Bacon was a devout Anglican, writing in his Essays, "a little philosophy inclineth man's mind to atheism, but depth in philosophy bringeth men's minds about to religion." With respect to the philosophy of science, the term scientism frequently implies a critique of the more extreme expressions of logical positivism and has been used by social scientists such as Friedrich Hayek, philosophers of science such as Karl Popper, and philosophers such as Mary Midgley, the later Hilary Putnam, and Tzvetan Todorov to describe (for example) the dogmatic endorsement


id: scientism-20260505-1
file: data/en.wikipedia.org/wiki/Scientism-1.md
category: reference
title: Scientism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientism
date_saved: 2026-05-05T03:45:51.099471+00:00
instance: kb-cron
parent: scientism-20260505-0
summary:
It is used to criticize a totalizing opinion of science as if it were capable of describing all reality and knowledge, or as if it were the only true method to acquire knowledge about reality and the nature of things; It is used, often pejoratively, to denote violations by which the theories and methods of one (scientific) discipline are applied inappropriately to another (scientific or non-scientific) discipline and its domain. An example of this second usage is to term as scientism any attempt to claim science as the only or primary source of human values (a traditional domain of ethics) or as the source of meaning and purpose (a traditional domain of religion and related worldviews). The term scientism was popularized by F. A. Hayek, who defined it in 1942 as the "slavish imitation of the method and language of Science". Mathematician Alexander Grothendieck, in his 1971 essay "The New Universal Church", characterized scientism as a religion-like ideology that advocates scientific reductionism, scientific authoritarianism, political technocracy and technological salvation, while denying the epistemological validity of feelings and experiences such as love, emotion, beauty and fulfillment. He predicted that "in coming years, the chief political dividing line will fall less and


id: scientism-20260505-2
file: data/en.wikipedia.org/wiki/Scientism-2.md
category: reference
title: Scientism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientism
date_saved: 2026-05-05T03:45:51.099471+00:00
instance: kb-cron
parent: scientism-20260505-0
summary:
== Relevance to debates about science and religion == Both religious and non-religious scholars have applied the term scientism to individuals associated with New Atheism. Theologian John Haught argued that philosopher Daniel Dennett and other New Atheists subscribe to a belief system of scientific naturalism, which includes the dogma that "only nature, including humans and our creations, is real: that God does not exist; and that science alone can give us complete and reliable knowledge of reality." Haught argued that this belief system is self-refuting since it requires its adherents to assent to beliefs that violate its own stated requirements for knowledge. Christian philosopher Peter Williams argued in 2013 that it is only by conflating science with scientism that New Atheists feel qualified to "pontificate on metaphysical issues". Daniel Dennett responded to religious criticism of his 2006 book Breaking the Spell: Religion as a Natural Phenomenon by saying that accusations of scientism "[are] an all-purpose, wild-card smear ... When someone puts forward a scientific theory that [religious critics] really don't like, they just try to discredit it as 'scientism'. But when it comes to facts, and explanations of facts, science is the only game in town". Non-religious scholars have also


id: scientism-20260505-3
file: data/en.wikipedia.org/wiki/Scientism-3.md
category: reference
title: Scientism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientism
date_saved: 2026-05-05T03:45:51.099471+00:00
instance: kb-cron
parent: scientism-20260505-0
summary:
Thomas M. Lessl argued that religious themes persist in what he terms scientism, the public rhetoric of science. There are two methods of describing this idea of scientism: the epistemological method (the assumption that the scientific method trumps other ways of knowing) and the ontological method (that the rational mind represents the world and both operate in knowable ways). According to Lessl, the ontological method is an attempt to "resolve the conflict between rationalism and skepticism". Lessl also argued that without scientism, there would not be a scientific culture. == Rationalization and modernity == In the introduction to his collected works on the sociology of religion, Max Weber asked why "the scientific, the artistic, the political, or the economic development [elsewhere] ... did not enter upon that path of rationalization which is peculiar to the Occident?" According to the German social theorist Jürgen Habermas, "For Weber, the intrinsic (that is, not merely contingent) relationship between modernity and what he called 'Occidental rationalism' was still self-evident." Weber described a process of rationalisation, disenchantment and the "disintegration of religious world views" that resulted in modern secular societies and capitalism. "Modernization" was introduced as a technical term only in the 1950s. It is


id: secondary-research-20260505-0
file: data/en.wikipedia.org/wiki/Secondary_research-0.md
category: reference
title: Secondary research
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Secondary_research
date_saved: 2026-05-05T03:45:52.254825+00:00
instance: kb-cron
parent: secondary-research-20260505-0
summary:
Secondary research involves the summary, collation and/or synthesis of existing research. Secondary research is contrasted with primary research in that primary research involves the generation of data, whereas secondary research uses primary research sources as a source of data for analysis. A notable marker of primary research is the inclusion of a "methods" section, where the authors describe how the data was generated. Common examples of secondary research include textbooks, encyclopedias, news articles, review articles, and meta analyses. When conducting secondary research, authors may draw data from published academic papers, government documents, statistical databases, and historical records. == Fields == The term is widely used in fields such as history, legal research, market research, and Wikipedia editing. The principal methodology in health secondary research is the systematic review, commonly using meta-analytic statistical techniques. Other methods of synthesis, like realist reviews and meta-narrative reviews, have been developed in the 21st century. Secondary market research includes the reuse by a second party of any data collected from a first party such as telephone interviews or surveys. Secondary market research can be broken up into two categories: information from internal sources such as an agency or company, and information from external sources held


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: 2026-05-05T03:45:53.377239+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: 2026-05-05T03:45:53.377239+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: 2026-05-05T03:45:58.269443+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: 2026-05-05T03:45:59.502626+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: 2026-05-05T03:45:59.502626+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: 2026-05-05T03:45:59.502626+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: 2026-05-05T03:45:59.502626+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: 2026-05-05T03:45:59.502626+00:00
instance: kb-cron
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: 2026-05-05T03:45:59.502626+00:00
instance: kb-cron
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: 2026-05-05T03:45:59.502626+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: 2026-05-05T03:46:00.971376+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: 2026-05-05T03:46:00.971376+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: 2026-05-05T03:46:00.971376+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: solomon-four-group-design-20260505-0
file: data/en.wikipedia.org/wiki/Solomon_four-group_design-0.md
category: reference
title: Solomon four-group design
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Solomon_four-group_design
date_saved: 2026-05-05T03:46:02.115206+00:00
instance: kb-cron
parent: solomon-four-group-design-20260505-0
summary:
The Solomon four-group design is a research method developed by Richard Solomon in 1949. It is sometimes used in social science, psychology and medicine. It can be used if there are concerns that the treatment might be sensitized by the pre-test. In addition of the usual two groups (treatment and control), it has a second pair of groups who do not receive a pre-intervention evaluation. == Structure == The structure of the trial is shown in the table : The first two groups receive the evaluation test before and after the study, as in a normal two-group trial. The second groups receive the evaluation only after the study. The effectiveness of the treatment can be evaluated by comparisons between groups 1 and 3 and between groups 2 and 4.. In addition, the effect of the pre-treatment evaluation can be calculated by comparing the control group who received the pre-treatment evaluation with those who did not (groups 2 and 4). Various statistical treatments for the Solomon four-group design have been put forward, including Stouffer's Z and Monte Carlo. == References ==


id: source-criticism-20260505-0
file: data/en.wikipedia.org/wiki/Source_criticism-0.md
category: reference
title: Source criticism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Source_criticism
date_saved: 2026-05-05T03:46:03.309305+00:00
instance: kb-cron
parent: source-criticism-20260505-0
summary:
Source criticism (or information evaluation) is the process of evaluating an information source, i.e.: a document, a person, a speech, a fingerprint, a photo, an observation, or anything used in order to obtain knowledge. In relation to a given purpose, a given information source may be more or less valid, reliable or relevant. Broadly, "source criticism" is the interdisciplinary study of how information sources are evaluated for given tasks. == Meaning == Problems in translation: The Danish word kildekritik, like the Norwegian word kildekritikk and the Swedish word källkritik, derived from the German Quellenkritik and is closely associated with the German historian Leopold von Ranke (1795–1886). Historian Wolfgang Hardtwig wrote: His [Ranke's] first work Geschichte der romanischen und germanischen Völker von 1494–1514 (History of the Latin and Teutonic Nations from 1494 to 1514) (1824) was a great success. It already showed some of the basic characteristics of his conception of Europe, and was of historiographical importance particularly because Ranke made an exemplary critical analysis of his sources in a separate volume, Zur Kritik neuerer Geschichtsschreiber (On the Critical Methods of Recent Historians). In this work he raised the method of textual criticism used in the late eighteenth century, particularly in


id: source-criticism-20260505-1
file: data/en.wikipedia.org/wiki/Source_criticism-1.md
category: reference
title: Source criticism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Source_criticism
date_saved: 2026-05-05T03:46:03.309305+00:00
instance: kb-cron
parent: source-criticism-20260505-0
summary:
Empiricism evaluates sources by considering the observations (or sensations) on which they are based. Sources without basis in experience are not seen as valid. Rationalism provides low priority to sources based on observations. In order to be meaningful, observations must be explained by clear ideas or concepts. It is the logical structure and the well definedness that is in focus in evaluating information sources from the rationalist point of view. Historicism evaluates information sources on the basis of their reflection of their sociocultural context and their theoretical development. Pragmatism evaluate sources on the basis of how their values and usefulness to accomplish certain outcomes. Pragmatism is skeptical about claimed neutral information sources. The evaluation of knowledge or information sources cannot be more certain than is the construction of knowledge. If one accepts the principle of fallibilism then one also has to accept that source criticism can never 100% verify knowledge claims. As discussed in the next section, source criticism is intimately linked to scientific methods. The presence of fallacies of argument in sources is another kind of philosophical criterion for evaluating sources. Fallacies are presented by Walton (1998). Among the fallacies are the ad hominem fallacy (the use of personal


id: source-criticism-20260505-2
file: data/en.wikipedia.org/wiki/Source_criticism-2.md
category: reference
title: Source criticism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Source_criticism
date_saved: 2026-05-05T03:46:03.309305+00:00
instance: kb-cron
parent: source-criticism-20260505-0
summary:
Photos are often manipulated during wars and for political purposes. One well known example is Joseph Stalin's manipulation of a photograph from May 5, 1920, on which Stalin's predecessor Lenin held a speech for Soviet troops that Leon Trotsky attended. Stalin had later Trotsky retouched out of this photograph. (cf. King, 1997). A recent example is reported by Healy (2008) about North Korean leader Kim Jong Il. === Internet sources === Much interest in evaluating Internet sources (such as Wikipedia) is reflected in the scholarly literature of library and information science and in other fields. Mintz (2002) is an edited volume about this issue. Examples of literature examining Internet sources include Chesney (2006), Fritch & Cromwell (2001), Leth & Thurén (2000) and Wilkinson, Bennett, & Oliver (1997). === Archaeology and history === "In history, the term historical method was first introduced in a systematic way in the sixteenth century by Jean Bodin in his treatise of source criticism, Methodus ad facilem historiarium cognitionem (1566). Characteristically, Bodin's treatise intended to establish the ways by which reliable knowledge of the past could be established by checking sources against one another and by so assessing the reliability of the information conveyed by them,


id: source-criticism-20260505-3
file: data/en.wikipedia.org/wiki/Source_criticism-3.md
category: reference
title: Source criticism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Source_criticism
date_saved: 2026-05-05T03:46:03.309305+00:00
instance: kb-cron
parent: source-criticism-20260505-0
summary:
The most important legal sources are created by parliaments, governments, courts, and legal researchers. They may be written or informal and based on established practices. Views concerning the quality of sources differ among legal philosophies: Legal positivism is the view that the text of the law should be considered in isolation, while legal realism, interpretivism (legal), critical legal studies and feminist legal criticism interprets the law on a broader cultural basis. == See also == == Notes == == References == American Library Association (1994) Evaluating Information: A Basic Checklist. Brochure. American Library Association Anderson, Richard C. (1978). Schema-directed processes in language comprehension. IN: NATO International Conference on Cognitive Psychology and Instruction, 1977, Amsterdam: Cognitive Psychology and Instruction. Ed. by A. M. Lesgold, J. W. Pellegrino, S. D. Fokkema & R. Glaser. New York: Plenum Press (pp. 67–82). Anderson, Richard C. & Pichert, J. W. (1977). Recall of previously unrecallable information following a shift of perspective. Urbana, Il: University of Illinois, Center for the Study of Reading, April. 1977. (Technical Report 41). Available in full-text from: http://eric.ed.gov/ERICDocs/data/ericdocs2sql/content_storage_01/0000019b/80/31/83/58.pdf Bazerman, Charles (1995). The Informed Writer: Using Sources in the Disciplines. 5th ed. Houghton Mifflin. Bee, Ronald E. (1983). Statistics and Source Criticism.


id: source-criticism-20260505-4
file: data/en.wikipedia.org/wiki/Source_criticism-4.md
category: reference
title: Source criticism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Source_criticism
date_saved: 2026-05-05T03:46:03.309305+00:00
instance: kb-cron
parent: source-criticism-20260505-0
summary:
== External links == The Source Compass: Source Criticism The History Sourcebook: The Need for Source Criticism


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: 2026-05-05T03:46:04.534427+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: 2026-05-05T03:46:05.679193+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: 2026-05-05T03:46:05.679193+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: 2026-05-05T03:46:05.679193+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: 2026-05-05T03:46:05.679193+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: 2026-05-05T03:46:06.846901+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: 2026-05-05T03:46:08.081898+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: 2026-05-05T03:46:08.081898+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: 2026-05-05T03:46:08.081898+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: 2026-05-05T03:46:08.081898+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: 2026-05-05T03:46:08.081898+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: 2026-05-05T03:46:09.345306+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: 2026-05-05T03:46:14.259101+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: 2026-05-05T03:46:14.259101+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: 2026-05-05T03:46:15.441953+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: 2026-05-05T03:46:16.674581+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: twyman-s-law-20260505-0
file: data/en.wikipedia.org/wiki/Twyman's_law-0.md
category: reference
title: Twyman's law
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Twyman's_law
date_saved: 2026-05-05T03:46:17.935817+00:00
instance: kb-cron
parent: twyman-s-law-20260505-0
summary:
Twyman's law states that "Any figure that looks interesting or different is usually wrong", following the principle that "the more unusual or interesting the data, the more likely they are to have been the result of an error of one kind or another". It is named after the media and market researcher Tony Twyman and has been described as one of the most important laws of data analysis. The law is based on the fact that errors in data measurement and analysis can lead to observed quantities that are wildly different from typical values. These errors are usually more common than real changes of similar magnitude in the underlying process being measured. For example, if an analyst at a software company notices that the number of users has doubled overnight, the most likely explanation is a bug in logging, rather than a true increase in users. The law can also be extended to situations where the underlying data is influenced by unexpected factors that differ from what was intended to be measured. For example, when schools show unusually large improvements in test scores, subsequent investigation often reveals that those scores were driven by fraud. == See also == Extraordinary claims


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: 2026-05-05T03:46:19.051542+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: 2026-05-05T03:46:20.229041+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: 2026-05-05T03:46:20.229041+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: 2026-05-05T03:46:21.411609+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: 2026-05-05T03:46:21.411609+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: 2026-05-05T03:46:23.045729+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: 2026-05-05T03:46:23.045729+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: 2026-05-05T03:46:23.045729+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: 2026-05-05T03:46:24.303507+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: 2026-05-05T03:46:24.303507+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: 2026-05-05T03:46:25.545596+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


