# kb

id: cross-impact-analysis-20260505-0
file: data/en.wikipedia.org/wiki/Cross_impact_analysis-0.md
category: reference
title: Cross impact analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Cross_impact_analysis
date_saved: 2026-05-05T03:43:30.297149+00:00
instance: kb-cron
parent: cross-impact-analysis-20260505-0
summary:
Cross-impact analysis is a methodology developed by Theodore J. Gordon and Olaf Helmer in 1966 to help determine how relationships between events would impact resulting events and reduce uncertainty in the future. The Central Intelligence Agency (CIA) became interested in the methodology in the late 1960s and early 1970s as an analytic technique for predicting how different factors and variables would impact future decisions. In the mid-1970s, futurists began to use the methodology in larger numbers as a means to predict the probability of specific events and determine how related events impacted one another. By 2006, cross-impact analysis matured into a number of related methodologies with uses for businesses and communities as well as futurists and intelligence analysts. == Development == The basic principles of cross-impact analysis date back to the late 1960s, but the original processes were relatively simple and were based on a game design. Eventually, advanced techniques, methodologies, and programs were developed to apply the principles of cross-impact analysis, and the basic method is now applied in futures think tanks, business settings, and the intelligence community. Theodore J. Gordon writes that cross-impact analysis was the result of a question: "can forecasting be based on perceptions about how


id: cross-impact-analysis-20260505-1
file: data/en.wikipedia.org/wiki/Cross_impact_analysis-1.md
category: reference
title: Cross impact analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Cross_impact_analysis
date_saved: 2026-05-05T03:43:30.297149+00:00
instance: kb-cron
parent: cross-impact-analysis-20260505-0
summary:
==== Relationship to Delphi technique ==== The accuracy of the math and specific events requires special expertise in the events or topic of discussion. In order to get the insight needed to get events and calculations, analysts typically contact a large number of experts and ask their opinions on events or probabilities in person as groups or through surveys. These groupings often resemble the Delphi Technique, which is an analytic technique that gathers a group of experts on a subject together and asks their opinion on a scenario or prediction. Usually, analysts consider the average prediction or scenario as the most likely to occur. The two are so closely related, that analysts often use the two techniques in combination or as part of a larger methodology. ==== Strengths ==== The futurist forecasting style of cross-impact analysis carries a few key strengths. Its use of groups of experts ensures a number of opinions worth considering when calculating probabilities of events. The level of mathematics in calculating probabilities ensures that the results are as accurate as a researcher can make them. In addition, when used on consort with other analytic techniques, this type of cross-impact analysis can give greater quantitative results to


id: data-analysis-20260505-0
file: data/en.wikipedia.org/wiki/Data_analysis-0.md
category: reference
title: Data analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Data_analysis
date_saved: 2026-05-05T03:43:31.459045+00:00
instance: kb-cron
parent: data-analysis-20260505-0
summary:
Data analysis is the process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today's business world, data analysis plays an important role in making decisions more scientific and helping businesses operate more effectively. It is widely used in fields such as business analytics, healthcare, and artificial intelligence to extract meaningful insights from data. Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. In statistical applications, data analysis can be divided into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA). EDA focuses on discovering new features in the data, while CDA focuses on confirming or falsifying existing hypotheses. Predictive analytics focuses on the application of statistical models for predictive forecasting or classification, while text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a


id: data-analysis-20260505-1
file: data/en.wikipedia.org/wiki/Data_analysis-1.md
category: reference
title: Data analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Data_analysis
date_saved: 2026-05-05T03:43:31.459045+00:00
instance: kb-cron
parent: data-analysis-20260505-0
summary:
Mathematical formulas or mathematical models (supported by algorithms) may be applied to the data in order to identify relationships among the variables; for example, checking for correlation and by determining whether or not there is the presence of causality. In general terms, models may be developed to evaluate a specific variable based on other variable(s) contained within the dataset, with some residual error depending on the implemented model's accuracy (e.g., Data = Model + Error). Inferential statistics utilizes techniques that measure the relationships between particular variables. For example, regression analysis may be used to model whether a change in advertising (independent variable X), provides an explanation for the variation in sales (dependent variable Y), i.e. is Y a function of X? This can be described as (Y = aX + b + error), where the model is designed such that (a) and (b) minimize the error when the model predicts Y for a given range of values of X. === Data product === A data product is a computer application that takes data inputs and generates outputs, feeding them back into the environment. It may be based on a model or algorithm. For instance, an application that analyzes data about


id: data-analysis-20260505-2
file: data/en.wikipedia.org/wiki/Data_analysis-2.md
category: reference
title: Data analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Data_analysis
date_saved: 2026-05-05T03:43:31.459045+00:00
instance: kb-cron
parent: data-analysis-20260505-0
summary:
McKinsey and Company named a technique for breaking down a quantitative problem into its component parts called the MECE principle. MECE means "Mutually Exclusive and Collectively Exhaustive". Each layer can be broken down into its components; each of the sub-components must be mutually exclusive of each other and collectively add up to the layer above them. For example, profit by definition can be broken down into total revenue and total cost. Analysts may use robust statistical measurements to solve certain analytical problems. Hypothesis testing is used when a particular hypothesis about the true state of affairs is made by the analyst and data is gathered to determine whether that hypothesis is true or false. For example, the hypothesis might be that "Unemployment has no effect on inflation", which relates to an economics concept called the Phillips Curve. Hypothesis testing involves considering the likelihood of Type I and type II errors, which relate to whether the data supports accepting or rejecting the hypothesis. Regression analysis may be used when the analyst is trying to determine the extent to which independent variable X affects dependent variable Y (e.g., "To what extent do changes in the unemployment rate (X) affect the inflation rate


id: data-analysis-20260505-3
file: data/en.wikipedia.org/wiki/Data_analysis-3.md
category: reference
title: Data analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Data_analysis
date_saved: 2026-05-05T03:43:31.459045+00:00
instance: kb-cron
parent: data-analysis-20260505-0
summary:
=== Initial data analysis === The most important distinction between the initial data analysis phase and the main analysis phase is that during initial data analysis one refrains from any analysis that is aimed at answering the original research question. The initial data analysis phase is guided by the following four questions: ==== Quality of data ==== The quality of the data should be checked as early as possible. Data quality can be assessed in several ways, using different types of analysis: frequency counts, descriptive statistics (mean, standard deviation, median), normality (skewness, kurtosis, frequency histograms), normal imputation is needed. Analysis of extreme observations: outlying observations in the data are analyzed to see if they seem to disturb the distribution. Comparison and correction of differences in coding schemes: variables are compared with coding schemes of variables external to the data set, and possibly corrected if coding schemes are not comparable. Test for common-method variance. The choice of analyses to assess the data quality during the initial data analysis phase depends on the analyses that will be conducted in the main analysis phase. ==== Quality of measurements ==== The quality of the measurement instruments should only be checked during the initial data


id: data-analysis-20260505-4
file: data/en.wikipedia.org/wiki/Data_analysis-4.md
category: reference
title: Data analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Data_analysis
date_saved: 2026-05-05T03:43:31.459045+00:00
instance: kb-cron
parent: data-analysis-20260505-0
summary:
==== Exploratory and confirmatory approaches ==== In the main analysis phase, either an exploratory or confirmatory approach can be adopted. Usually the approach is decided before data is collected. In an exploratory analysis no clear hypothesis is stated before analysing the data, and the data is searched for models that describe the data well. In a confirmatory analysis, clear hypotheses about the data are tested. Exploratory data analysis should be interpreted carefully. When testing multiple models at once there is a high chance on finding at least one of them to be significant, but this can be due to a type 1 error. It is important to always adjust the significance level when testing multiple models with, for example, a Bonferroni correction. Also, one should not follow up an exploratory analysis with a confirmatory analysis in the same dataset. An exploratory analysis is used to find ideas for a theory, but not to test that theory as well. When a model is found exploratory in a dataset, then following up that analysis with a confirmatory analysis in the same dataset could simply mean that the results of the confirmatory analysis are due to the same type 1 error that resulted


id: deductive-nomological-model-20260505-0
file: data/en.wikipedia.org/wiki/Deductive-nomological_model-0.md
category: reference
title: Deductive-nomological model
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Deductive-nomological_model
date_saved: 2026-05-05T03:43:33.815064+00:00
instance: kb-cron
parent: deductive-nomological-model-20260505-0
summary:
The deductive-nomological model (DN model) of scientific explanation, also known as Hempel's model, the Hempel–Oppenheim model, the Popper–Hempel model, or the covering law model, is a formal view of scientifically answering questions asking, "Why...?". The DN model poses scientific explanation as a deductive structure, one where truth of its premises entails truth of its conclusion, hinged on accurate prediction or postdiction of the phenomenon to be explained. Because of problems concerning humans' ability to define, discover, and know causality, this was omitted in initial formulations of the DN model. Causality was thought to be incidentally approximated by realistic selection of premises that derive the phenomenon of interest from observed starting conditions plus general laws. Still, the DN model formally permitted causally irrelevant factors. Also, derivability from observations and laws sometimes yielded absurd answers. When logical empiricism fell out of favor in the 1960s, the DN model was widely seen as a flawed or greatly incomplete model of scientific explanation. Nonetheless, it remained an idealized version of scientific explanation, and one that was rather accurate when applied to modern physics. In the early 1980s, a revision to the DN model emphasized maximal specificity for relevance of the conditions and axioms stated.


id: deductive-nomological-model-20260505-1
file: data/en.wikipedia.org/wiki/Deductive-nomological_model-1.md
category: reference
title: Deductive-nomological model
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Deductive-nomological_model
date_saved: 2026-05-05T03:43:33.815064+00:00
instance: kb-cron
parent: deductive-nomological-model-20260505-0
summary:
== Growth == Whereas Comtean positivism posed science as description, logical positivism emerged in the late 1920s and posed science as explanation, perhaps to better unify empirical sciences by covering not only fundamental science—that is, fundamental physics—but special sciences, too, such as biology, psychology, economics, and anthropology. After defeat of National Socialism with World War II's close in 1945, logical positivism shifted to a milder variant, logical empiricism. All variants of the movement, which lasted until 1965, are neopositivism, sharing the quest of verificationism. Neopositivists led emergence of the philosophy subdiscipline philosophy of science, researching such questions and aspects of scientific theory and knowledge. Scientific realism takes scientific theory's statements at face value, thus accorded either falsity or truth—probable or approximate or actual. Neopositivists held scientific antirealism as instrumentalism, holding scientific theory as simply a device to predict observations and their course, while statements on nature's unobservable aspects are elliptical at or metaphorical of its observable aspects, rather. DN model received its most detailed, influential statement by Carl G Hempel, first in his 1942 article "The function of general laws in history", and more explicitly with Paul Oppenheim in their 1948 article "Studies in the logic of explanation". Leading logical


id: deductive-nomological-model-20260505-2
file: data/en.wikipedia.org/wiki/Deductive-nomological_model-2.md
category: reference
title: Deductive-nomological model
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Deductive-nomological_model
date_saved: 2026-05-05T03:43:33.815064+00:00
instance: kb-cron
parent: deductive-nomological-model-20260505-0
summary:
== Weaknesses == By DN model, if one asks, "Why is that shadow 20 feet long?", another can answer, "Because that flagpole is 15 feet tall, the Sun is at x angle, and laws of electromagnetism". Yet by problem of symmetry, if one instead asked, "Why is that flagpole 15 feet tall?", another could answer, "Because that shadow is 20 feet long, the Sun is at x angle, and laws of electromagnetism", likewise a deduction from observed conditions and scientific laws, but an answer clearly incorrect. By the problem of irrelevance, if one asks, "Why did that man not get pregnant?", one could in part answer, among the explanans, "Because he took birth control pills"—if he factually took them, and the law of their preventing pregnancy—as covering law model poses no restriction to bar that observation from the explanans. Many philosophers have concluded that causality is integral to scientific explanation. DN model offers a necessary condition of a causal explanation—successful prediction—but not sufficient conditions of causal explanation, as a universal regularity can include spurious relations or simple correlations, for instance Z always following Y, but not Z because of Y, instead Y and then Z as an effect of X.


id: deductive-nomological-model-20260505-3
file: data/en.wikipedia.org/wiki/Deductive-nomological_model-3.md
category: reference
title: Deductive-nomological model
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Deductive-nomological_model
date_saved: 2026-05-05T03:43:33.815064+00:00
instance: kb-cron
parent: deductive-nomological-model-20260505-0
summary:
== Covering action == Through lawlike explanation, fundamental physics—often perceived as fundamental science—has proceeded through intertheory relation and theory reduction, thereby resolving experimental paradoxes to great historical success, resembling covering law model. In early 20th century, Ernst Mach as well as Wilhelm Ostwald had resisted Ludwig Boltzmann's reduction of thermodynamics—and thereby Boyle's law—to statistical mechanics partly because it rested on kinetic theory of gas, hinging on atomic/molecular theory of matter. Mach as well as Ostwald viewed matter as a variant of energy, and molecules as mathematical illusions, as even Boltzmann thought possible. In 1905, via statistical mechanics, Albert Einstein predicted the phenomenon Brownian motion—unexplained since reported in 1827 by botanist Robert Brown. Soon, most physicists accepted that atoms and molecules were unobservable yet real. Also in 1905, Einstein explained the electromagnetic field's energy as distributed in particles, doubted until this helped resolve atomic theory in the 1910s and 1920s. Meanwhile, all known physical phenomena were gravitational or electromagnetic, whose two theories misaligned. Yet belief in aether as the source of all physical phenomena was virtually unanimous. At experimental paradoxes, physicists modified the aether's hypothetical properties. Finding the luminiferous aether a useless hypothesis, Einstein in 1905 a priori unified all inertial


id: deductive-nomological-model-20260505-4
file: data/en.wikipedia.org/wiki/Deductive-nomological_model-4.md
category: reference
title: Deductive-nomological model
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Deductive-nomological_model
date_saved: 2026-05-05T03:43:33.815064+00:00
instance: kb-cron
parent: deductive-nomological-model-20260505-0
summary:
== Sources == Avent, Ryan, "The Q&A: Brian Greene—life after the Higgs", The Economist blog: Babbage, 19 Jul 2012. Ayala, Francisco J & Theodosius G Dobzhansky, eds, Studies in the Philosophy of Biology: Reduction and Related Problems (Berkeley & Los Angeles: University of California Press, 1974). Bechtel, William, Discovering Cell Mechanisms: The Creation of Modern Cell Biology (New York: Cambridge University Press, 2006). Bechtel, William, Philosophy of Science: An Overview for Cognitive Science (Hillsdale, NJ: Lawrence Erlbaum Associates, 1988). Bem, Sacha & Huib L de Jong, Theoretical Issues in Psychology: An Introduction, 2nd edn (London: Sage Publications, 2006). Ben-Menahem, Yemima, Conventionalism: From Poincaré to Quine (Cambridge: Cambridge University Press, 2006). Blackmore, J T & R Itagaki, S Tanaka, eds, Ernst Mach's Vienna 1895–1930: Or Phenomenalism as Philosophy of Science (Dordrecht: Kluwer Academic Publishers, 2001). Boffetta, Paolo, "Causation in the presence of weak associations", Critical Reviews in Food Science and Nutrition, 2010 Dec; 50(s1):13–16. Bolotin, David, An Approach to Aristotle's Physics: With Particular Attention to the Role of His Manner of Writing (Albany: State University of New York Press, 1998). Bourdeau, Michel, "Auguste Comte", in Edward N Zalta, ed, The Stanford Encyclopedia of Philosophy, Winter 2013 edn. Braibant, Sylvie & Giorgio


id: deductive-nomological-model-20260505-5
file: data/en.wikipedia.org/wiki/Deductive-nomological_model-5.md
category: reference
title: Deductive-nomological model
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Deductive-nomological_model
date_saved: 2026-05-05T03:43:33.815064+00:00
instance: kb-cron
parent: deductive-nomological-model-20260505-0
summary:
Rowlands, Peter, Oliver Lodge and the Liverpool Physical Society (Liverpool: Liverpool University Press, 1990). Sarkar, Sahotra & Jessica Pfeifer, eds, The Philosophy of Science: An Encyclopedia, Volume 1: A–M (New York: Routledge, 2006). Schwarz, John H (1998). "Recent developments in superstring theory". Proceedings of the National Academy of Sciences of the United States of America. 95 (6): 2750–7. Bibcode:1998PNAS...95.2750S. doi:10.1073/pnas.95.6.2750. PMC 19640. PMID 9501161. Schweber, Silvan S, QED and the Men who Made it: Dyson, Feynman, Schwinger, and Tomonaga (Princeton: Princeton University Press, 1994). Schliesser, Eric, "Hume's Newtonianism and anti-Newtonianism", in Edward N Zalta, ed, The Stanford Encyclopedia of Philosophy, Winter 2008 edn. Spohn, Wolfgang, The Laws of Belief: Ranking Theory and Its Philosophical Applications (Oxford: Oxford University Press, 2012). Suppe, Frederick, ed, The Structure of Scientific Theories, 2nd edn (Urbana, Illinois: University of Illinois Press, 1977). Tavel, Morton, Contemporary Physics and the Limits of Knowledge (Piscataway, NJ: Rutgers University Press, 2002). Torretti, Roberto, The Philosophy of Physics (New York: Cambridge University Press, 1999). Vongehr, Sascha, "Higgs discovery rehabilitating despised Einstein Ether", Science 2.0: Alpha Meme website, 13 Dec 2011. Vongehr, Sascha, "Supporting abstract relational space-time as fundamental without doctrinism against emergence, arXiv (History and Philosophy of Physics):0912.3069, 2 Oct


id: discovery-science-20260505-0
file: data/en.wikipedia.org/wiki/Discovery_science-0.md
category: reference
title: Discovery science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Discovery_science
date_saved: 2026-05-05T03:43:35.183336+00:00
instance: kb-cron
parent: discovery-science-20260505-0
summary:
Discovery science (also known as discovery-based science) is a scientific methodology which aims to find new patterns, correlations, and form hypotheses through the analysis of large-scale experimental data. The term “discovery science” encompasses various fields of study, including basic, translational, and computational science and research. Discovery-based methodologies are commonly contrasted with traditional scientific practice, the latter involving hypothesis formation before experimental data is closely examined. Discovery science involves the process of inductive reasoning or using observations to make generalisations, and can be applied to a range of science-related fields, e.g., medicine, proteomics, hydrology, psychology, and psychiatry. == Overview == === Purpose === Discovery science places an emphasis on 'basic' discovery, which can fundamentally change the status quo. For example, in the early years of water resources research, the use of discovery science was demonstrated by seeking to elucidate phenomena that were, until that point, unexplained. It did not matter how unusual these ideas may have been perceived to be. In this sense, discovery science is based on the attitude that "we must not allow our concepts of the earth, in so far as they transcend the reach of observation, to root themselves so deeply and so firmly in our minds


id: discovery-science-20260505-1
file: data/en.wikipedia.org/wiki/Discovery_science-1.md
category: reference
title: Discovery science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Discovery_science
date_saved: 2026-05-05T03:43:35.183336+00:00
instance: kb-cron
parent: discovery-science-20260505-0
summary:
=== Proteomics === Another example of discovery science is proteomics, a technology-driven and technology limited discovery science. Technologies for proteomic analysis provide information that is useful in discovery science. Proteome analysis as a discovery science is applicable in biotechnology, e.g., it assists in 1) the discovery of biochemical pathways which can identify targets for therapies, 2) developing new processes for manufacturing biological materials, 3) monitoring manufacturing processes for the purpose of quality control, and 4) developing diagnostic tests and efficacious treatment strategies for clinical diseases. In the context of proteomics, current life-science research remains technology-limited, however, recent available tools have assisted in evolving such research from being hypothesis-driven to discovery-driven. === Hydrology === Field hydrology has experienced a decline in progress due to a change from discovery-based field work to the gathering of data for modal parameterisation. In field hydrology, models are not any more useful than an understanding of how systems work, and discovery science allows for this understanding. Several important examples of field-based inquiry and discovery have taken place in field hydrology. These include: identifying spatial patterns of soil moisture and how they relate to topography; interrogating such data through the use of geostatistics; and discovering the importance


id: discovery-science-20260505-2
file: data/en.wikipedia.org/wiki/Discovery_science-2.md
category: reference
title: Discovery science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Discovery_science
date_saved: 2026-05-05T03:43:35.183336+00:00
instance: kb-cron
parent: discovery-science-20260505-0
summary:
== Methodology == Discovery-based methodologies are often viewed in contrast to traditional scientific practice, where hypotheses are formed before close examination of experimental data. However, from a philosophical perspective where all or most of the observable "low-hanging fruit" has already been plucked, examining the phenomenological world more closely than the senses alone (even augmented senses, e.g. via microscopes, telescopes, bifocals etc.) opens a new source of knowledge for hypothesis formation. This process is also known as inductive reasoning or the use of specific observations to make generalisations. Discovery science is usually a complex process, and consequently does not follow a simple linear cause and effect pattern. This means that outcomes are uncertain, and it is expected to have disappointing results as a fundamental part of discovery science. In particular, this may apply to medicine for the critically ill, where disease syndromes may be complex and multi-factorial. In psychiatry, studying complex relationships between brain and behaviour requires a large-scale science. This calls for a need to conceptually switch from hypothesis-driven studies to hypothesis-generating research which is discovery-based. Normally, discovery-based approaches for research are initially hypothesis-free, however, hypothesis testing can be elevated to a new level that effectively supports traditional hypothesis-driven studies.


id: evidence-based-practice-20260505-0
file: data/en.wikipedia.org/wiki/Evidence-based_practice-0.md
category: reference
title: Evidence-based practice
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Evidence-based_practice
date_saved: 2026-05-05T03:43:36.513768+00:00
instance: kb-cron
parent: evidence-based-practice-20260505-0
summary:
Evidence-based practice (EBP) is the idea that occupational practices ought to be based on scientific evidence. The movement towards evidence-based practices attempts to encourage and, in some instances, require professionals and other decision-makers to pay more attention to evidence to inform their decision-making. The goal of evidence-based practice is to eliminate unsound or outdated practices in favor of more-effective ones by shifting the basis for decision making from tradition, intuition, and unsystematic experience to firmly grounded scientific research. The proposal has been controversial, with some arguing that results may not specialize to individuals as well as traditional practices. Evidence-based practices have been gaining ground since the introduction of evidence-based medicine and have spread to the allied health professions, education, management, law, public policy, architecture, and other fields. In light of studies showing problems in scientific research (such as the replication crisis), there is also a movement to apply evidence-based practices in scientific research itself. Research into the evidence-based practice of science is called metascience. An individual or organisation is justified in claiming that a specific practice is evidence-based if, and only if, three conditions are met. First, the individual or organisation possesses comparative evidence about the effects of the specific


id: evidence-based-practice-20260505-1
file: data/en.wikipedia.org/wiki/Evidence-based_practice-1.md
category: reference
title: Evidence-based practice
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Evidence-based_practice
date_saved: 2026-05-05T03:43:36.513768+00:00
instance: kb-cron
parent: evidence-based-practice-20260505-0
summary:
Evaluating scientific research is extremely complex. The process can be greatly simplified with the use of a heuristic that ranks the relative strengths of results obtained from scientific research, which is called a hierarchy of evidence. The design of the study and the endpoints measured (such as survival or quality of life) affect the strength of the evidence. Typically, systematic reviews and meta-analysis rank at the top of the hierarchy while randomized controlled trials rank above observational studies, and expert opinion and case reports rank at the bottom. There is broad agreement on the relative strength of the different types of studies, but there is no single, universally-accepted hierarchy of evidence. More than 80 different hierarchies have been proposed for assessing medical evidence. == Applications == === Medicine === Evidence-based medicine is an approach to medical practice intended to optimize decision-making by emphasizing the use of evidence from well-designed and well-conducted research. Although all medicine based on science has some degree of empirical support, evidence-based medicine goes further, classifying evidence by its epistemologic strength and requiring that only the strongest types (coming from meta-analyses, systematic reviews, and randomized controlled trials) can yield strong recommendations; weaker types (such as from case-control


id: evidence-based-practice-20260505-2
file: data/en.wikipedia.org/wiki/Evidence-based_practice-2.md
category: reference
title: Evidence-based practice
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Evidence-based_practice
date_saved: 2026-05-05T03:43:36.513768+00:00
instance: kb-cron
parent: evidence-based-practice-20260505-0
summary:
==== Reviews of existing research on education ==== The following websites offer free analysis and information on education research: The Best Evidence Encyclopedia is a free website created by the Johns Hopkins University School of Education's Center for Data-Driven Reform in Education (established in 2004) and is funded by the Institute of Education Sciences, U.S. Department of Education. It gives educators and researchers reviews about the strength of the evidence supporting a variety of English programs available for students in grades K–12. The reviews cover programs in areas such as Mathematics, Reading, Writing, Science, Comprehensive school reform, and Early childhood Education; and include such topics as the effectiveness of technology and struggling readers. The Education Endowment Foundation was established in 2011 by The Sutton Trust, as a lead charity in partnership with Impetus Trust, together being the government-designated What Works Centre for UK Education. Evidence for the Every Student Succeeds Act began in 2017 and is produced by the Center for Research and Reform in Education at Johns Hopkins University School of Education. It offers free up-to-date information on current PK-12 programs in reading, writing, math, science, and others that meet the standards of the Every Student Succeeds Act (the


id: experimenter-s-regress-20260505-0
file: data/en.wikipedia.org/wiki/Experimenter's_regress-0.md
category: reference
title: Experimenter's regress
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Experimenter's_regress
date_saved: 2026-05-05T03:43:41.898956+00:00
instance: kb-cron
parent: experimenter-s-regress-20260505-0
summary:
In science, experimenter's regress refers to a loop of dependence between theory and evidence. In order to judge whether a new piece of evidence is correct we rely on theory-based predictions, and to judge the value of competing theories we rely on existing evidence. Cognitive bias affects experiments, and experiments determine which theory is valid. This issue is particularly important in new fields of science where there is no consensus regarding the values of various competing theories, and where the extent of experimental errors is not well known. If experimenter's regress acts a positive feedback system, it can be a source of pathological science. An experimenter's strong belief in a new theory produces confirmation bias, and any biased evidence they obtain then strengthens their belief in that particular theory. Neither individual researchers nor entire scientific communities are immune to this effect: see N-rays and polywater. Experimenter's regress is a typical relativistic phenomenon in the Empirical Programme of Relativism (EPOR). EPOR is very much concerned with a focus on social interactions, by looking at particular (local) cases and controversial issues in the context in which they happen. In EPOR, all scientific knowledge is perceived to be socially constructed and is thus


id: experimentum-crucis-20260505-0
file: data/en.wikipedia.org/wiki/Experimentum_crucis-0.md
category: reference
title: Experimentum crucis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Experimentum_crucis
date_saved: 2026-05-05T03:43:43.118696+00:00
instance: kb-cron
parent: experimentum-crucis-20260505-0
summary:
In science, an experimentum crucis (English: crucial experiment or critical experiment) is an experiment capable of decisively determining whether or not a particular hypothesis or theory is superior to all others whose acceptance is currently widespread in the scientific community. In particular, such an experiment—if true—must typically be able to produce a result that rules out all other hypotheses or theories, thereby demonstrating that under the conditions of the experiment (i.e., under the same external circumstances and for the same "input variables" within the experiment), those hypotheses and theories are proven false but the experimenter's hypothesis is not ruled out. An opposite view, rejecting the decisive value of the experimentum crucis in choosing one hypothesis or theory over its rivals, is the Duhem–Quine thesis. == History == Francis Bacon in his Novum Organum first described the concept of a situation in which one theory but not others would hold true, using the name instantia crucis ("crucial instance"). The phrase experimentum crucis, denoting the deliberate creation of such a situation for the purpose of testing the rival theories, was later coined by Robert Hooke and then famously used by Isaac Newton and Robert Boyle. The production of such an experiment is


id: expert-elicitation-20260505-0
file: data/en.wikipedia.org/wiki/Expert_elicitation-0.md
category: reference
title: Expert elicitation
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Expert_elicitation
date_saved: 2026-05-05T03:43:44.433509+00:00
instance: kb-cron
parent: expert-elicitation-20260505-0
summary:
In science, engineering, and research, expert elicitation is the synthesis of opinions of authorities of a subject where there is uncertainty due to insufficient data or when such data is unattainable because of physical constraints or lack of resources. Expert elicitation is essentially a scientific consensus methodology. It is often used in the study of rare events. Expert elicitation allows for parametrization, an "educated guess", for the respective topic under study. Expert elicitation generally helps quantify uncertainty. Expert elicitation tends to be multidisciplinary as well as interdisciplinary, with practically universal applicability, and is used in a broad range of fields. Prominent recent expert elicitation applications include climate change, modeling seismic hazard and damage, association of tornado damage to wind speed in developing the Enhanced Fujita scale, risk analysis for nuclear waste storage. In performing expert elicitation certain factors need to be taken into consideration. The topic must be one for which there are people who have predictive expertise. Furthermore, the objective should be to obtain an experts' carefully considered judgment based on a systematic consideration of all relevant evidence. For this reason one should take care to adopt strategies designed to help the expert being interviewed to avoid overlooking relevant


id: extended-peer-community-20260505-0
file: data/en.wikipedia.org/wiki/Extended_peer_community-0.md
category: reference
title: Extended peer community
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Extended_peer_community
date_saved: 2026-05-05T03:43:45.627385+00:00
instance: kb-cron
parent: extended-peer-community-20260505-0
summary:
The concept of Extended peer community belongs to the field of Sociology of science, and in particular the use of science in the solution of social, political or ecological problems. It was first introduced by in the 1990s by Silvio Funtowicz and Jerome R. Ravetz. in the context of what would become Post-normal science. An Extended peer community is intended by these authors as a space where both credentialed experts from different disciplines and lay stakeholders can discuss and deliberate. == Content == An Extended peer community is intended by its creators as an arrangement at the science policy interface that helps to expand and assess both the knowledge-base and the value-base of policy-making'. Post-normal science's extended peer community argues for two kind of extensions: first, more than one discipline is assumed to have a potential bearing on the issue being debated, thereby providing different lenses to consider the problem. Second the community is extended to lay actors, taken to be all those with stakes, or an interest, in the given issue. The lay members of the community thus constituted may also take upon themselves active 'research' tasks; this has happened e.g. in the so-called 'popular epidemiology', when the official


id: free-parameter-20260505-0
file: data/en.wikipedia.org/wiki/Free_parameter-0.md
category: reference
title: Free parameter
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Free_parameter
date_saved: 2026-05-05T03:43:46.862708+00:00
instance: kb-cron
parent: free-parameter-20260505-0
summary:
A free parameter is a variable in a mathematical model which cannot be predicted precisely or constrained by the model and must be estimated experimentally or theoretically. A mathematical model, theory, or conjecture is more likely to be right and less likely to be the product of wishful thinking if it relies on few free parameters and is consistent with large amounts of data. == See also == Decision variables Exogenous variables Overfitting Random variables State variables == References ==


id: fringe-science-20260505-0
file: data/en.wikipedia.org/wiki/Fringe_science-0.md
category: reference
title: Fringe science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Fringe_science
date_saved: 2026-05-05T03:43:48.153140+00:00
instance: kb-cron
parent: fringe-science-20260505-0
summary:
Fringe science refers to ideas whose attributes include being highly speculative or relying on premises already refuted. The chance of ideas rejected by editors and published outside the mainstream being correct is remote. When the general public does not distinguish between science and imitators, it risks exploitation, and in some cases, a "yearning to believe or a generalized suspicion of experts is a very potent incentive to accepting some pseudoscientific claims". The term "fringe science" covers everything from novel hypotheses, which can be tested utilizing the scientific method, to wild ad hoc hypotheses and mumbo jumbo. This has resulted in a tendency to dismiss all fringe science as the domain of pseudoscientists, hobbyists, and quacks. A concept that was once accepted by the mainstream scientific community may become fringe science because of a later evaluation of previous research. For example, focal infection theory, which held that focal infections of the tonsils or teeth are a primary cause of systemic disease, was once considered to be medical fact. It has since been dismissed because of a lack of evidence. == Description == The boundary between fringe science and pseudoscience is disputed. Friedlander writes that there is no widespread understanding of what


id: fringe-science-20260505-1
file: data/en.wikipedia.org/wiki/Fringe_science-1.md
category: reference
title: Fringe science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Fringe_science
date_saved: 2026-05-05T03:43:48.153140+00:00
instance: kb-cron
parent: fringe-science-20260505-0
summary:
== Responding to fringe science == Michael W. Friedlander has suggested some guidelines for responding to fringe science, which, he argues, is a more difficult problem than scientific misconduct. His suggested methods include impeccable accuracy, checking cited sources, not overstating orthodox science, thorough understanding of the Wegener continental drift example, examples of orthodox science investigating radical proposals, and prepared examples of errors from fringe scientists. Friedlander suggests that fringe science is necessary so mainstream science will not atrophy. Scientists must evaluate the plausibility of each new fringe claim, and certain fringe discoveries "will later graduate into the ranks of accepted" — while others "will never receive confirmation". Margaret Wertheim profiled many "outsider scientists" in her book Physics on the Fringe, who receive little or no attention from professional scientists. She describes all of them as trying to make sense of the world using the scientific method but in the face of being unable to understand modern science's complex theories. She also finds it fair that credentialed scientists do not bother spending a lot of time learning about and explaining problems with the fringe theories of uncredentialed scientists since the authors of those theories have not taken the time to understand


id: gold-effect-20260505-0
file: data/en.wikipedia.org/wiki/Gold_effect-0.md
category: reference
title: Gold effect
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Gold_effect
date_saved: 2026-05-05T03:43:49.268222+00:00
instance: kb-cron
parent: gold-effect-20260505-0
summary:
The Gold effect is the phenomenon in which a scientific idea, particularly in medicine, is developed to the status of an accepted position within a professional body or association by the social process itself of scientific conferences, committees, and consensus building, despite not being supported by conclusive evidence. The Gold effect is used to analyze errors in public health policy and practice, such as the widespread use of cholesterol screening in the prevention of cardiovascular disease. The effect was described by Thomas Gold in 1979. The effect was reviewed by Petr Skrabanek and James McCormick in their book Follies and Fallacies in Medicine. In their book, Skrabanek and McCormick describe the Gold effect as: "At the beginning a few people arrive at a state of near belief in some idea. A meeting is held to discuss the pros and cons of the idea. More people favouring the idea than those disinterested will be present. A representative committee will be nominated to prepare a collective volume to propagate and foster interest in the idea. The totality of resulting articles based on the idea will appear to show an increasing consensus. A specialized journal will be launched. Only orthodox or near orthodox


id: graphism-thesis-20260505-0
file: data/en.wikipedia.org/wiki/Graphism_thesis-0.md
category: reference
title: Graphism thesis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Graphism_thesis
date_saved: 2026-05-05T03:43:50.464721+00:00
instance: kb-cron
parent: graphism-thesis-20260505-0
summary:
In the sociology of science and science and technology studies (STS), the graphism thesis is a proposition advanced by Bruno Latour that visual inscriptions—especially graphs, diagrams, and other visual representations—are central to the practice and authority of science. First articulated in Latour's 1990 essay "Visualization and Cognition: Drawing Things Together", the thesis holds that the power of scientific disciplines is closely linked to their capacity to produce, circulate, and combine visual representations of data. The thesis has been empirically tested by a number of researchers, who have found strong correlations between the level of graph use in scientific publications and the perceived "hardness" of scientific disciplines. == Background == The graphism thesis emerged from Latour's broader theory of inscriptions in scientific practice, developed across several works including Laboratory Life (1979, with Steve Woolgar) and Science in Action (1987). In these works, Latour defined an inscription device as any apparatus or configuration that transforms a material substance into a visual display usable within a scientific text, such as a diagram, chart, photograph, map, equation, or table. Central to this framework is the concept of immutable mobiles (a key term in actor–network theory)—objects that can be transported across distances while retaining their


id: heterophenomenology-20260505-0
file: data/en.wikipedia.org/wiki/Heterophenomenology-0.md
category: reference
title: Heterophenomenology
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Heterophenomenology
date_saved: 2026-05-05T03:43:53.007645+00:00
instance: kb-cron
parent: heterophenomenology-20260505-0
summary:
In the thought of the philosopher Daniel Dennett, heterophenomenology ("phenomenology of another, not oneself") is an explicitly third-person, scientific approach to the study of consciousness and other mental phenomena. It consists of applying the scientific method with an anthropological bent, combining the subject's self-reports with all other available evidence to determine their mental state. The goal is to discover how subjects see the world themselves, without taking the accuracy of the subject's view for granted. == Overview == Heterophenomenology is put forth as the alternative to traditional Cartesian phenomenology, which Dennett calls "lone-wolf autophenomenology" to emphasize the fact that traditional phenomenology accepts the subject's self-reports as being authoritative. In contrast, heterophenomenology considers the subjects authoritative only about how things seem to them. It does not dismiss the Cartesian first-person perspective, but rather brackets it so that it can be intersubjectively verified by empirical means, allowing it to be submitted as scientific evidence. The method requires a researcher to listen to the subjects and take what they say seriously, but to also look at everything else available to them, including the subject's bodily responses and environment, evidence provided by relevant neurological or psychological studies, the researcher's memories of their own experiences,


id: hypothesis-20260505-0
file: data/en.wikipedia.org/wiki/Hypothesis-0.md
category: reference
title: Hypothesis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Hypothesis
date_saved: 2026-05-05T03:43:58.163409+00:00
instance: kb-cron
parent: hypothesis-20260505-0
summary:
A hypothesis (pl.: hypotheses) is a proposed explanation for a phenomenon. A scientific hypothesis must be based on observations and make a testable and reproducible prediction about reality, in a process beginning with an educated guess. If a hypothesis is repeatedly independently demonstrated by experiment to be true, it becomes a scientific theory. In colloquial usage, the words hypothesis and theory are often used interchangeably, but this is incorrect in the context of science. A working hypothesis is a provisionally-accepted hypothesis used for the purpose of pursuing further progress in research. Working hypotheses are frequently discarded, and often proposed with knowledge (and warning) that they are incomplete and thus false, with the intent of moving research in at least somewhat the right direction, especially when scientists are stuck on an issue and brainstorming ideas. In formal logic, a hypothesis is the antecedent in a proposition. For example, in the proposition "If P, then Q", statement P denotes the hypothesis (or antecedent) of the consequent Q. Hypothesis P is the assumption in a (possibly counterfactual) "what if" question. The adjective "hypothetical" (having the nature of a hypothesis or being assumed to exist as an immediate consequence of a hypothesis), can refer


id: hypothesis-20260505-1
file: data/en.wikipedia.org/wiki/Hypothesis-1.md
category: reference
title: Hypothesis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Hypothesis
date_saved: 2026-05-05T03:43:58.163409+00:00
instance: kb-cron
parent: hypothesis-20260505-0
summary:
A working hypothesis is a hypothesis that is provisionally accepted as a basis for further research in the hope that a tenable theory will be produced, even if the hypothesis ultimately fails. Like all hypotheses, a working hypothesis is constructed as a statement of expectations, which can be linked to the exploratory research purpose in empirical investigation. Working hypotheses are often used as a conceptual framework in qualitative research. The provisional nature of working hypotheses makes them useful as an organizing device in applied research. Here they act like a useful guide to address problems that are still in a formative phase. In recent years, philosophers of science have tried to integrate the various approaches to evaluating hypotheses, and the scientific method in general, to form a more complete system that integrates the individual concerns of each approach. Notably, Imre Lakatos and Paul Feyerabend, Karl Popper's colleague and student, respectively, have produced novel attempts at such a synthesis. == Hypotheses, concepts and measurement == Concepts in Hempel's deductive-nomological model play a key role in the development and testing of hypotheses. Most formal hypotheses connect concepts by specifying the expected relationships between propositions. When a set of hypotheses are grouped together,


id: hypothetico-deductive-model-20260505-0
file: data/en.wikipedia.org/wiki/Hypothetico-deductive_model-0.md
category: reference
title: Hypothetico-deductive model
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Hypothetico-deductive_model
date_saved: 2026-05-05T03:44:00.724162+00:00
instance: kb-cron
parent: hypothetico-deductive-model-20260505-0
summary:
The hypothetico-deductive model or method is a proposed description of the scientific method. According to it, scientific inquiry proceeds by formulating a hypothesis in a form that can be falsifiable, using a test on observable data where the outcome is not yet known. A test outcome that could have and does run contrary to predictions of the hypothesis is taken as a falsification of the hypothesis. A test outcome that could have, but does not run contrary to the hypothesis corroborates the theory. It is then proposed to compare the explanatory value of competing hypotheses by testing how stringently they are corroborated by their predictions. == Example == One example of an algorithmic statement of the hypothetico-deductive method is as follows: One possible sequence in this model would be 1, 2, 3, 4. If the outcome of 4 holds, and 3 is not yet disproven, you may continue with 3, 4, 1, and so forth; but if the outcome of 4 shows 3 to be false, you will have to go back to 2 and try to invent a new 2, deduce a new 3, look for 4, and so forth. Note that this method can never absolutely verify (prove


id: interdisciplinary-peer-review-20260505-0
file: data/en.wikipedia.org/wiki/Interdisciplinary_peer_review-0.md
category: reference
title: Interdisciplinary peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Interdisciplinary_peer_review
date_saved: 2026-05-05T03:44:02.046934+00:00
instance: kb-cron
parent: interdisciplinary-peer-review-20260505-0
summary:
Interdisciplinary Peer Review (IPR) is a peer review process with an additional focus outside of the area of the author's subject of expertise. Disciplines such as telecommunications, political science, engineering, and medicine require specific subject matter expertise, however, they still cross multiple disciplines and may require review from many alternate functional areas to achieve maximum perspective to prevent duplication or improper publication. Reviews of this nature may also cross cultures, race, and other demographics to gain perspective. == Reduplication and Interdisciplinarity == In Peer Commentary on Peer Review: A case study in scientific quality control, Stevan R. Harnard (p. 15) begins to touch on the concept by addressing how well the review process works and what factors prevent re-duplication. Julie Klein, a professor of interdisciplinary studies at Wayne State University, defined interdisciplinarity as "new divisions of intellectual labor, collaborative research, team teaching, hybrid fields, comparative studies, increased borrowing across disciplines, and a variety of unified, holistic perspectives that have created pressures upon traditional divisions of knowledge". Klein has also described how interdisciplinarity is used to "find answers to complex questions, address broad issues, explore disciplinary and professional relations, to solve problems beyond the scope of any one discipline, and to


id: interrogating-ethnography-20260505-0
file: data/en.wikipedia.org/wiki/Interrogating_Ethnography-0.md
category: reference
title: Interrogating Ethnography
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Interrogating_Ethnography
date_saved: 2026-05-05T03:44:03.389592+00:00
instance: kb-cron
parent: interrogating-ethnography-20260505-0
summary:
Interrogating Ethnography: Why Evidence Matters is a 2017 book by Steven Lubet of Northwestern University Law School, critiquing methods used in the discipline of ethnography. Writing in Contexts, Syid Ali of Long Island University called it "an essential critique of the most public-facing product sociology has to offer." Interrogating Ethnography, which criticizes ethnographers for the practice of changing the name of the area they study, is part of the Replication crisis. But it is largely a critique of ethnography methodology when it relies on the narratives of interviewees with no attempt to verify assertions of fact. Lubet began the project of writing this book after reading and publishing a notable critique of the use of evidence Alice Goffman's controversial 2014 book On the Run: Fugitive Life in an American City, That critique led him to read and attempt to verify "more than 50 ethnographic monographs and an equivalent number of articles. Focusing on sociologists’ studies of American cities... (and checking) facts that could be documented — or not... by consulting experts and pulling public records." According to the Chronicle of Higher Education, the book "has touched off a debate about what ethnographers might learn from legal scholars, and vice versa.


id: inverse-square-law-20260505-0
file: data/en.wikipedia.org/wiki/Inverse-square_law-0.md
category: reference
title: Inverse-square law
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Inverse-square_law
date_saved: 2026-05-05T03:44:04.680534+00:00
instance: kb-cron
parent: inverse-square-law-20260505-0
summary:
In physical science, an inverse-square law is any scientific law stating that the observed "intensity" of a specified physical quantity (being nothing more than the value of the physical quantity) is inversely proportional to the square of the distance from the source of that physical quantity. The fundamental cause for this can be understood as geometric dilution corresponding to point-source radiation into three-dimensional space. Radar energy expands during both the signal transmission and the reflected return, so the inverse square for both paths means that the radar will receive energy according to the inverse fourth power of the range. To prevent dilution of energy while propagating a signal, certain methods can be used such as a waveguide, which acts like a canal does for water, or how a gun barrel restricts hot gas expansion to one dimension in order to prevent loss of energy transfer to a bullet. == Formula == In mathematical notation the inverse square law can be expressed as an intensity (I) varying as a function of distance (d) from some centre. The intensity is proportional (see ∝) to the reciprocal of the square of the distance thus: intensity ∝ 1 distance 2 {\displaystyle {\text{intensity}}\ \propto \


id: inverse-square-law-20260505-1
file: data/en.wikipedia.org/wiki/Inverse-square_law-1.md
category: reference
title: Inverse-square law
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Inverse-square_law
date_saved: 2026-05-05T03:44:04.680534+00:00
instance: kb-cron
parent: inverse-square-law-20260505-0
summary:
=== Light and other electromagnetic radiation === The intensity (or illuminance or irradiance) of light or other linear waves radiating from a point source (energy per unit of area perpendicular to the source) is inversely proportional to the square of the distance from the source, so an object (of the same size) twice as far away receives only one-quarter the energy (in the same time period). More generally, the irradiance, i.e., the intensity (or power per unit area in the direction of propagation), of a spherical wavefront varies inversely with the square of the distance from the source (assuming there are no losses caused by absorption or scattering). For example, the intensity of radiation from the Sun is 9126 watts per square meter at the distance of Mercury (0.387 AU) but only 1367 watts per square meter at the distance of Earth (1 AU)—an approximate threefold increase in distance results in an approximate ninefold decrease in intensity of radiation. For non-isotropic radiators such as parabolic antennas, headlights, and lasers, the effective origin is located far behind the beam aperture. If you are close to the origin, you don't have to go far to double the radius, so the signal drops


id: inverse-square-law-20260505-2
file: data/en.wikipedia.org/wiki/Inverse-square_law-2.md
category: reference
title: Inverse-square law
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Inverse-square_law
date_saved: 2026-05-05T03:44:04.680534+00:00
instance: kb-cron
parent: inverse-square-law-20260505-0
summary:
== Non-Euclidean implications == The inverse-square law, fundamental in Euclidean spaces, also applies to non-Euclidean geometries, including hyperbolic space. The curvature present in these spaces alters physical laws, influencing a variety of fields such as cosmology, general relativity, and string theory. John D. Barrow, in his 2020 paper "Non-Euclidean Newtonian Cosmology," expands on the behavior of force (F) and potential (Φ) within hyperbolic 3-space (H3). He explains that F and Φ obey the relationships F ∝ 1 / R² sinh²(r/R) and Φ ∝ coth(r/R), where R represents the curvature radius and r represents the distance from the focal point. The concept of spatial dimensionality, first proposed by Immanuel Kant, remains a topic of debate concerning the inverse-square law. Dimitria Electra Gatzia and Rex D. Ramsier, in their 2021 paper, contend that the inverse-square law is more closely related to force distribution symmetry than to the dimensionality of space. In the context of non-Euclidean geometries and general relativity, deviations from the inverse-square law do not arise from the law itself but rather from the assumption that the force between two bodies is instantaneous, which contradicts special relativity. General relativity reinterprets gravity as the curvature of spacetime, leading particles to move along


id: isotope-dilution-20260505-0
file: data/en.wikipedia.org/wiki/Isotope_dilution-0.md
category: reference
title: Isotope dilution
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Isotope_dilution
date_saved: 2026-05-05T03:44:05.958864+00:00
instance: kb-cron
parent: isotope-dilution-20260505-0
summary:
Isotope dilution analysis is a method of determining the quantity of chemical substances. In its most simple conception, the method of isotope dilution comprises the addition of known amounts of isotopically enriched substance to the analyzed sample. Mixing of the isotopic standard with the sample effectively "dilutes" the isotopic enrichment of the standard and this forms the basis for the isotope dilution method. Isotope dilution is classified as a method of internal standardisation, because the standard (isotopically enriched form of analyte) is added directly to the sample. In addition, unlike traditional analytical methods which rely on signal intensity, isotope dilution employs signal ratios. Owing to both of these advantages, the method of isotope dilution is regarded among chemistry measurement methods of the highest metrological standing. Isotopes are variants of a particular chemical element which differ in neutron number. All isotopes of a given element have the same number of protons in each atom. The term isotope is formed from the Greek roots isos (ἴσος "equal") and topos (τόπος "place"), meaning "the same place"; thus, the meaning behind the name is that different isotopes of a single element occupy the same position on the periodic table. == Early history == Analytical


id: isotope-dilution-20260505-1
file: data/en.wikipedia.org/wiki/Isotope_dilution-1.md
category: reference
title: Isotope dilution
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Isotope_dilution
date_saved: 2026-05-05T03:44:05.958864+00:00
instance: kb-cron
parent: isotope-dilution-20260505-0
summary:
n A = n B R B − R A B R A B − R A × 1 + R A 1 + R B {\displaystyle n_{\mathrm {A} }=n_{\mathrm {B} }{\frac {R_{\mathrm {B} }-R_{\mathrm {AB} }}{R_{\mathrm {AB} }-R_{\mathrm {A} }}}\times {\frac {1+R_{\mathrm {A} }}{1+R_{\mathrm {B} }}}} In a typical gas chromatography analysis, isotopic dilution can decrease the uncertainty of the measurement results from 5% to 1%. It can also be used in mass spectrometry (commonly referred to as isotopic dilution mass spectrometry or IDMS), in which the isotopic ratio can be determined with precision typically better than 0.25%. === Optimum composition of the blend === In a simplified manner, the uncertainty of the measurement results is largely determined from the measurement of RAB: u ( n A ) 2 ∝ ( ∂ n A ∂ R A B ) 2 u ( R A B ) 2 = n A 2 ( R A − R B ) 2 ( R A − R A B ) 2 ( R A B − R B ) 2 u ( R A B ) 2 {\displaystyle u(n_{\mathrm {A} })^{2}\propto \left({\frac {\partial {n_{\mathrm {A} }}}{\partial R_{\mathrm {AB} }}}\right)^{2}u(R_{\mathrm {AB} })^{2}=n_{\mathrm {A}


id: jadad-scale-20260505-0
file: data/en.wikipedia.org/wiki/Jadad_scale-0.md
category: reference
title: Jadad scale
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Jadad_scale
date_saved: 2026-05-05T03:44:07.109883+00:00
instance: kb-cron
parent: jadad-scale-20260505-0
summary:
The Jadad scale, sometimes known as Jadad scoring or the Oxford quality scoring system, is a procedure to assess the methodological quality of a clinical trial by objective criteria. It is named after Canadian-Colombian physician Alex Jadad who in 1996 described a system for allocating such trials a score of between zero (very poor) and five (rigorous). It is the most widely used such assessment in the world, and as of May 2024, its seminal paper has been cited in over 25,000 scientific works. == Description == The Jadad scale independently assesses the methodological quality of a clinical trial judging the effectiveness of blinding. Alejandro "Alex" Jadad Bechara, a Colombian physician who worked as a Research Fellow at the Oxford Pain Relief Unit, Nuffield Department of Anaesthetics at the University of Oxford described the allocating trials a score of between zero (very poor) and five (rigorous) in an appendix to a 1996 paper. In a 2007 book, Jadad described the randomised controlled trial as "one of the simplest, most powerful and revolutionary forms of research". == Background == Clinical trials are conducted for the purpose of collecting data on the efficacy of medical treatments. The treatment might be, for example,


id: leiden-manifesto-20260505-0
file: data/en.wikipedia.org/wiki/Leiden_Manifesto-0.md
category: reference
title: Leiden Manifesto
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Leiden_Manifesto
date_saved: 2026-05-05T03:44:08.418093+00:00
instance: kb-cron
parent: leiden-manifesto-20260505-0
summary:
The Leiden Manifesto for research metrics (LM) is a list of "ten principles to guide research evaluation", published as a comment in Nature on 22 April 2015. It was formulated by public policy professor Diana Hicks, scientometrics professor Paul Wouters, and their colleagues at the 19th International Conference on Science and Technology Indicators, held between 3–5 September 2014 in Leiden, The Netherlands. The Leiden Manifesto was proposed as a guide to combat misuse of bibliometrics when evaluating scientific research literature. Examples of commonly used bibliometrics for science, or scientometrics, are the h-index, impact factor, other indicators such as Altmetrics. According to the Manifesto's authors, these metrics often pervasively misguide evaluations of scientific material. == Motivation == Motivations for codification of the Leiden Manifesto arose from a growing worry that "impact-factor obsession" was leading to inadequate judgement of scientific material that should be worthy of fair evaluation. Lead author Diana Hicks hoped that publishing the list in Nature would spread its ideas, already commonplace in the scientometrics sphere, to the broader scientific community. === DORA and other predecessors === The scientific community has long been interested in reforming assessment of the impact of scientific and academic research. The 2013 San Francisco


id: leiden-manifesto-20260505-1
file: data/en.wikipedia.org/wiki/Leiden_Manifesto-1.md
category: reference
title: Leiden Manifesto
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Leiden_Manifesto
date_saved: 2026-05-05T03:44:08.418093+00:00
instance: kb-cron
parent: leiden-manifesto-20260505-0
summary:
=== Public endorsement === LIBER, a collaboration of European research libraries, issued a substantial review of the Leiden Manifesto in 2017, concluding that it was a "solid foundation" on which academic libraries could base their assessment of metrics. Elsevier, a global leader in research publishing and information analytics, announced on 14 July 2020 that it would use the Leiden Manifesto to guide its development of improved research evaluation. Elsevier stated that the principles of the manifesto were already close in nature to their 2019 CiteScore metrics, which was in summary "improved calculation methodology" for "a more robust, fair and faster indicator of research impact". Loughborough University's LIS-Bibliometrics committee chose to base their own principles on the Leiden Manifesto, instead of the DORA, because the manifesto takes a "broader approach to the responsible use of all bibliometrics across a range of disciplines and settings", according to their policy manager Elizabeth Gadd. Stephen Curry, chair of the DORA steering committee, commented on this statement by emphasizing that DORA was aiming to extend its "disciplinary and geographical reach". === Further applications === David Moher and his co-authors referenced the Leiden Manifesto in a perspective for Issues in Science and Technology, writing that academic


id: level-of-measurement-20260505-0
file: data/en.wikipedia.org/wiki/Level_of_measurement-0.md
category: reference
title: Level of measurement
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Level_of_measurement
date_saved: 2026-05-05T03:44:10.167435+00:00
instance: kb-cron
parent: level-of-measurement-20260505-0
summary:
Level of measurement or scale of measure is a classification that describes the nature of information within the values assigned to variables. Psychologist Stanley Smith Stevens developed the best-known classification with four levels, or scales, of measurement: nominal, ordinal, interval, and ratio. This framework of distinguishing levels of measurement originated in psychology and has since had a complex history, being adopted and extended in some disciplines and by some scholars, and criticized or rejected by others. Other classifications include those by Mosteller and Tukey, and by Chrisman. Stevens proposed his typology in a 1946 Science article titled "On the theory of scales of measurement". In that article, Stevens claimed that all measurement in science was conducted using four different types of scales that he called "nominal", "ordinal", "interval", and "ratio", unifying both "qualitative" (which are described by his "nominal" type) and "quantitative" (to a different degree, all the rest of his scales). The concept of scale types later received the mathematical rigour that it lacked at its inception with the work of mathematical psychologists Theodore Alper (1985, 1987), Louis Narens (1981a, b), and R. Duncan Luce (1986, 1987, 2001). As Luce (1997, p. 395) wrote: S. S. Stevens (1946, 1951,


id: level-of-measurement-20260505-1
file: data/en.wikipedia.org/wiki/Level_of_measurement-1.md
category: reference
title: Level of measurement
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Level_of_measurement
date_saved: 2026-05-05T03:44:10.167435+00:00
instance: kb-cron
parent: level-of-measurement-20260505-0
summary:
==== Central tendency and dispersion ==== According to Stevens, for ordinal data, the appropriate measure of central tendency is the median (the mode is also allowed, but not the mean), and the appropriate measure of dispersion is percentile or quartile (the standard deviation is not allowed). Those restrictions would imply that correlations can only be evaluated using rank order methods, and statistical significance can only be evaluated using non-parametric methods (R. M. Kothari, 2004). But the restrictions have not been generally endorsed by statisticians. In 1946, Stevens observed that psychological measurement, such as measurement of opinions, usually operates on ordinal scales; thus means and standard deviations have no validity according to his rules, but they can be used to get ideas for how to improve operationalization of variables used in questionnaires. Indeed, most psychological data collected by psychometric instruments and tests, measuring cognitive and other abilities, are ordinal (Cliff, 1996; Cliff & Keats, 2003; Michell, 2008). In particular, IQ scores reflect an ordinal scale, in which all scores are meaningful for comparison only. There is no zero point that represents an absence of intelligence, and a 10-point difference may carry different meanings at different points of the scale. === Interval


id: level-of-measurement-20260505-2
file: data/en.wikipedia.org/wiki/Level_of_measurement-2.md
category: reference
title: Level of measurement
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Level_of_measurement
date_saved: 2026-05-05T03:44:10.167435+00:00
instance: kb-cron
parent: level-of-measurement-20260505-0
summary:
== Debate over Stevens's typology == While Stevens's typology is widely adopted, it is still being challenged by other theoreticians, particularly in the cases of the nominal and ordinal types (Michell, 1986). Duncan (1986), for example, objected to the use of the word measurement in relation to the nominal type and Luce (1997) disagreed with Stevens's definition of measurement. On the other hand, Stevens (1975) said of his own definition of measurement that "the assignment can be any consistent rule. The only rule not allowed would be random assignment, for randomness amounts in effect to a nonrule". Hand says, "Basic psychology texts often begin with Stevens's framework and the ideas are ubiquitous. Indeed, the essential soundness of his hierarchy has been established for representational measurement by mathematicians, determining the invariance properties of mappings from empirical systems to real number continua. Certainly the ideas have been revised, extended, and elaborated, but the remarkable thing is his insight given the relatively limited formal apparatus available to him and how many decades have passed since he coined them." The use of the mean as a measure of the central tendency for the ordinal type is still debatable among those who accept Stevens's typology.


id: level-of-measurement-20260505-3
file: data/en.wikipedia.org/wiki/Level_of_measurement-3.md
category: reference
title: Level of measurement
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Level_of_measurement
date_saved: 2026-05-05T03:44:10.167435+00:00
instance: kb-cron
parent: level-of-measurement-20260505-0
summary:
…any law purporting to express a quantitative relation between sensation intensity and stimulus intensity is not merely false but is in fact meaningless unless and until a meaning can be given to the concept of addition as applied to sensation. That is, if Stevens's sone scale genuinely measured the intensity of auditory sensations, then evidence for such sensations as being quantitative attributes needed to be produced. The evidence needed was the presence of additive structure—a concept comprehensively treated by the German mathematician Otto Hölder (Hölder, 1901). Given that the physicist and measurement theorist Norman Robert Campbell dominated the Ferguson committee's deliberations, the committee concluded that measurement in the social sciences was impossible due to the lack of concatenation operations. This conclusion was later rendered false by the discovery of the theory of conjoint measurement by Debreu (1960) and independently by Luce & Tukey (1964). However, Stevens's reaction was not to conduct experiments to test for the presence of additive structure in sensations, but instead to render the conclusions of the Ferguson committee null and void by proposing a new theory of measurement: Paraphrasing N. R. Campbell (Final Report, p. 340), we may say that measurement, in the broadest sense, is


id: list-of-environmental-sampling-techniques-20260505-0
file: data/en.wikipedia.org/wiki/List_of_environmental_sampling_techniques-0.md
category: reference
title: List of environmental sampling techniques
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/List_of_environmental_sampling_techniques
date_saved: 2026-05-05T03:44:11.358834+00:00
instance: kb-cron
parent: list-of-environmental-sampling-techniques-20260505-0
summary:
Environmental sampling techniques are used in biology, ecology and conservation as part of scientific studies to learn about the flora and fauna of a particular area and establish a habitat's biodiversity, the abundance of species and the conditions in which these species live amongst other information. Where species are caught, researchers often then take the trapped organisms for further study in a lab or are documented by a researcher in the field before the animal is released. This information can then be used to better understand the environment, its ecology, the behaviour of species and how organisms interact with one another and their environment. Here is a list of some sampling techniques and equipment used in environmental sampling: Quadrats - used for plants and slow moving animals == Techniques for birds and/or flying invertebrates and/or bats == Malaise Trap Flight Interception Trap Harp Trap Robinson Trap Butterfly Net Mist Net == Techniques for Terrestrial Animals == Transect Tullgren Funnel - used for soil-living arthropods Pitfall Trap - used for small terrestrial animals like insects and amphibians Netting techniques for terrestrial animals Beating Net - used for insects dwelling in trees and shrubs Sweep Netting - used for insects in grasses


id: list-of-mathematics-based-methods-20260505-0
file: data/en.wikipedia.org/wiki/List_of_mathematics-based_methods-0.md
category: reference
title: List of mathematics-based methods
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/List_of_mathematics-based_methods
date_saved: 2026-05-05T03:44:16.378190+00:00
instance: kb-cron
parent: list-of-mathematics-based-methods-20260505-0
summary:
This is a list of mathematics-based methods. Adams' method (differential equations) Akra–Bazzi method (asymptotic analysis) Bisection method (root finding) Brent's method (root finding) Condorcet method (voting systems) Coombs' method (voting systems) Copeland's method (voting systems) Crank–Nicolson method (numerical analysis) D'Hondt method (voting systems) D21 – Janeček method (voting system) Discrete element method (numerical analysis) Domain decomposition method (numerical analysis) Epidemiological methods Euler's forward method Explicit and implicit methods (numerical analysis) Finite difference method (numerical analysis) Finite element method (numerical analysis) Finite volume method (numerical analysis) Highest averages method (voting systems) Method of exhaustion Method of infinite descent (number theory) Information bottleneck method Inverse chain rule method (calculus) Inverse transform sampling method (probability) Iterative method (numerical analysis) Jacobi method (linear algebra) Largest remainder method (voting systems) Level-set method Linear combination of atomic orbitals molecular orbital method (molecular orbitals) Method of characteristics Least squares method (optimization, statistics) Maximum likelihood method (statistics) Method of complements (arithmetic) Method of moving frames (differential geometry) Method of successive substitution (number theory) Monte Carlo method (computational physics, simulation) Newton's method (numerical analysis) Pemdas method (order of operation) Perturbation methods (functional analysis, quantum theory) Probabilistic method (combinatorics) Romberg's method (numerical analysis) Runge–Kutta method (numerical analysis) Sainte-Laguë method (voting


id: mature-technology-20260505-0
file: data/en.wikipedia.org/wiki/Mature_technology-0.md
category: reference
title: Mature technology
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Mature_technology
date_saved: 2026-05-05T03:44:17.608761+00:00
instance: kb-cron
parent: mature-technology-20260505-0
summary:
A mature technology is a technology that has been in use for long enough that most of its initial faults and inherent problems have been removed or reduced by further development. In some contexts, it may also refer to technology that has not seen widespread use, but whose scientific background is well understood. Its performance characteristics are also expected to be well understood with well-established design specifications. One of the key indicators of a mature technology is the ease of use for both non-experts and professionals. Another indicator is a reduction in the rate of new breakthrough advances related to it—whereas inventions related to a (popular) immature technology are usually rapid and diverse, and may change the whole use paradigm—advances to a mature technology are usually incremental improvements only. == Examples == The QWERTY keyboard design is an example of mature technology because its performance characteristics such as typing speeds and error rates have been established in various describable situations. Additionally, the basic key organization of this technology has remained the same over the last century. Another example is the barcode, a technology that also satisfies all the previously cited indicators. It is widely used since when it was first


id: mertonian-norms-20260505-0
file: data/en.wikipedia.org/wiki/Mertonian_norms-0.md
category: reference
title: Mertonian norms
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Mertonian_norms
date_saved: 2026-05-05T03:44:18.741729+00:00
instance: kb-cron
parent: mertonian-norms-20260505-0
summary:
In 1942, Robert K. Merton described four aspects of science that later came to be called Mertonian norms: "four sets of institutional imperatives taken to comprise the ethos of modern science... communism, universalism, disinterestedness, and organized skepticism". The subsequent portion of his book, The Sociology of Science, elaborated on these principles at "the heart of the Mertonian paradigm—the powerful juxtaposition of the normative structure of science with its institutionally distinctive reward system". == Description and motivation == Merton defines this 'ethos' with reference to Albert Bayet's 1931 work La Morale de la Science, which "abandons description and analysis for homily" as "that affectively toned complex of values and norms which is held to be binding on the man of science". He attempted to clarify it, given that previously it had not been 'codified'; Merton uses Bayet's remark that 'this scientific ethos [morale] does not have its theoreticians, but it has its artisans. It does not express its ideals, but serves them: it is implicated in the very existence of science'. The norms are expressed in the form of prescriptions, proscriptions, preferences, and permissions. They are legitimatized in terms of institutional values. These imperatives, transmitted by precept and example and reenforced


id: mertonian-norms-20260505-1
file: data/en.wikipedia.org/wiki/Mertonian_norms-1.md
category: reference
title: Mertonian norms
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Mertonian_norms
date_saved: 2026-05-05T03:44:18.741729+00:00
instance: kb-cron
parent: mertonian-norms-20260505-0
summary:
Secondly, to restrict scientific careers for any reason other than incompetence was to "prejudice the furtherance of knowledge". Merton again noted how the ethos of science may be inconsistent with that of society, but insists that "however inadequately it may be put into practice, the ethos of democracy includes universalism as a dominant guiding principle". He predicted that this inadequacy of laissez-faire democratic processes would lead ultimately to false differential accumulation and increasing regulation of science under political authority, which must be counteracted through "new technical forms of organization" towards equality of opportunity. === Disinterestedness === Distinct from altruism, scientists should act for the benefit of a common scientific enterprise rather than for specific outcomes. Merton reasoned that an individual's scientific motivation may be easily influenced and without institutional enforcement of disinterestedness, and the "seeming virtual absence of fraud" could not be explained by unusually high moral integrity of individuals alone. Merton observed a low rate of fraud in science ("virtual absence … which appears exceptional"), which he believed stemmed from the intrinsic need for "verifiability" in science and expert scrutiny by peers ("rigorous policing, to a degree perhaps unparalleled in any other field of activity") as well as the


id: meta-research-center-at-tilburg-university-20260505-0
file: data/en.wikipedia.org/wiki/Meta-Research_Center_at_Tilburg_University-0.md
category: reference
title: Meta-Research Center at Tilburg University
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Meta-Research_Center_at_Tilburg_University
date_saved: 2026-05-05T03:44:19.886629+00:00
instance: kb-cron
parent: meta-research-center-at-tilburg-university-20260505-0
summary:
The Meta-Research Center at Tilburg University is a metascience research center within the School of Social and Behavioral Sciences at the Dutch Tilburg University. They were profiled in a September 2018 article in Science. == Research == Meta-research aims to improve reproducibility by studying how science is practiced and published and developing better ways for the scientific community to operate. The research institute has published a large statistical meta-analysis of studies on the effect of Stereotype threat on girls' mathematics performance. They also use methods for estimating publication bias. The research institute has developed an R based software tool called Statcheck that can detect incorrect statistical methods used in research articles. A large amount of statistical errors were detected in a sample of 50 000 psychology research articles. The use of it was perceived negatively by some of the researchers. The data mining practices of the research center have been in conflict with the policies of scientific publisher Elsevier. A scientific misconduct case in the field of social psychology at Tilburg University has been a contributing factor in establishing the research center. == Advocacy == The research center makes recommendations for other researchers about how to avoid publication bias and


id: metascience-20260505-0
file: data/en.wikipedia.org/wiki/Metascience-0.md
category: reference
title: Metascience
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Metascience
date_saved: 2026-05-05T03:44:21.146005+00:00
instance: kb-cron
parent: metascience-20260505-0
summary:
Metascience (also known as meta-research) is the use of scientific methodology to study science itself. Metascience seeks to increase the quality of scientific research and enhance its efficiency. It is also known as "research on research" and "the science of science", as it uses research methods to study how research is done and find where improvements can be made. Metascience concerns itself with all fields of research and has been described as "a bird's eye view of science". In the words of John Ioannidis, "Science is the best thing that has happened to human beings ... but we can do it better." In 1966, an early meta-research paper examined the statistical methods of 295 papers published in ten high-profile medical journals. It found that "in almost 73% of the reports read ... conclusions were drawn when the justification for these conclusions was invalid." Meta-research in the following decades found many methodological flaws, inefficiencies, and poor practices in research across numerous scientific fields. Many scientific studies could not be reproduced, particularly in medicine and the soft sciences. The term "replication crisis" was coined in the early 2010s as part of a growing awareness of the problem. Measures have been implemented to


id: metascience-20260505-1
file: data/en.wikipedia.org/wiki/Metascience-1.md
category: reference
title: Metascience
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Metascience
date_saved: 2026-05-05T03:44:21.146005+00:00
instance: kb-cron
parent: metascience-20260505-0
summary:
=== Reporting === Meta-research has identified poor practices in reporting, explaining, disseminating and popularizing research, particularly within the social and health sciences. Poor reporting makes it difficult to accurately interpret the results of scientific studies, to replicate studies, and to identify biases and conflicts of interest in the authors. Solutions include the implementation of reporting standards, and greater transparency in scientific studies (including better requirements for disclosure of conflicts of interest). There is an attempt to standardize reporting of data and methodology through the creation of guidelines by reporting agencies such as CONSORT and the larger EQUATOR Network. === Reproducibility === The replication crisis is an ongoing methodological crisis in which it has been found that many scientific studies are difficult or impossible to replicate. While the crisis has its roots in the meta-research of the mid- to late 20th century, the phrase "replication crisis" was not coined until the early 2010s as part of a growing awareness of the problem. The replication crisis has been closely studied in psychology (especially social psychology) and medicine, including cancer research. Replication is an essential part of the scientific process, and the widespread failure of replication puts into question the reliability of affected


id: metascience-20260505-2
file: data/en.wikipedia.org/wiki/Metascience-2.md
category: reference
title: Metascience
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Metascience
date_saved: 2026-05-05T03:44:21.146005+00:00
instance: kb-cron
parent: metascience-20260505-0
summary:
===== Science funding and awards ===== Scientific awards are one category of science incentives. Metascience can explore existing and hypothetical systems of science awards. For instance, it found that work honored by Nobel Prizes clustered in only a few scientific fields with only 36/71 having received at least one Nobel Prize. Of the 114/849 domains science could be divided into their DC2 and DC3 classification systems, five were shown to comprise over half of the Nobel Prizes awarded between 1995 and 2017 (particle physics [14%], cell biology [12.1%], atomic physics [10.9%], neuroscience [10.1%], molecular chemistry [5.3%]). A study found that delegation of responsibility by policy-makers – a centralized authority-based top-down approach – for knowledge production and appropriate funding to science with science subsequently somehow delivering "reliable and useful knowledge to society" is too simple. Measurements show that allocation of bio-medical resources can be more strongly correlated to previous allocations and research than to burden of diseases. A study suggests that "[i]f peer review is maintained as the primary mechanism of arbitration in the competitive selection of research reports and funding, then the scientific community needs to make sure it is not arbitrary". Studies indicate there to is a need to


id: metascience-20260505-3
file: data/en.wikipedia.org/wiki/Metascience-3.md
category: reference
title: Metascience
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Metascience
date_saved: 2026-05-05T03:44:21.146005+00:00
instance: kb-cron
parent: metascience-20260505-0
summary:
Metascience can investigate how scientific processes evolve over time. A study found that teams are growing in size, "increasing by an average of 17% per decade". (see labor advantage below) It was found that prevalent forms of non-open access publication and prices charged for many conventional journals – even for publicly funded papers – are unwarranted, unnecessary – or suboptimal – and detrimental barriers to scientific progress. Open access can save considerable amounts of financial resources, which could be used otherwise, and level the playing field for researchers in developing countries. There are substantial expenses for subscriptions, gaining access to specific studies, and for article processing charges. Paywall: The Business of Scholarship is a documentary on such issues. Another topic are the established styles of scientific communication (e.g. long text-form studies and reviews) and the scientific publishing practices – there are concerns about a "glacial pace" of conventional publishing. The use of preprint-servers to publish study-drafts early is increasing and open peer review, new tools to screen studies, and improved matching of submitted manuscripts to reviewers are among the proposals to speed up publication. ==== Science overall and intrafield developments ==== Metadata from research publications can be extracted, enriched, and


id: metascience-20260505-4
file: data/en.wikipedia.org/wiki/Metascience-4.md
category: reference
title: Metascience
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Metascience
date_saved: 2026-05-05T03:44:21.146005+00:00
instance: kb-cron
parent: metascience-20260505-0
summary:
Some research is investigating scientific controversy or controversies, and may identify currently ongoing major debates (e.g. open questions), and disagreement between scientists or studies. One study suggests the level of disagreement was highest in the social sciences and humanities (0.61%), followed by biomedical and health sciences (0.41%), life and earth sciences (0.29%); physical sciences and engineering (0.15%), and mathematics and computer science (0.06%). Such research may also show, where the disagreements are, especially if they cluster, including visually such as with cluster diagrams. === Challenges of interpretation of pooled results === Studies about a specific research question or research topic are often reviewed in the form of higher-level overviews in which results from various studies are integrated, compared, critically analyzed and interpreted. Examples of such works are scientific reviews and meta-analyses. These and related practices face various challenges and are a subject of metascience. Various issues with available studies such as, for example, heterogeneity of methods used may lead to faulty conclusions of the meta-analysis. === Knowledge integration and living documents === Various problems require swift integration of new and existing science-based knowledge. Especially setting where there are a large number of loosely related projects and initiatives benefit from a


id: metascience-20260505-5
file: data/en.wikipedia.org/wiki/Metascience-5.md
category: reference
title: Metascience
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Metascience
date_saved: 2026-05-05T03:44:21.146005+00:00
instance: kb-cron
parent: metascience-20260505-0
summary:
==== As and for acceleration of progress ==== A study suggests that improving the way science is done could accelerate the rate of scientific discovery and its applications which could be useful for finding urgent solutions to humanity's problems, improve humanity's conditions, and enhance understanding of nature. Metascientific studies can seek to identify aspects of science that need improvement, and develop ways to improve them. If science is accepted as the fundamental engine of economic growth and social progress, this could raise "the question of what we – as a society – can do to accelerate science, and to direct science toward solving society's most important problems." One author clarified that funding science requires diverse approaches rather than a single model. DARPA models, curiosity-driven research, peer-champion mechanisms, and other approaches each have merit and application in different contexts. Nevertheless, evaluation of them can help build knowledge of what works or works best. == Reforms == Meta-research identifying flaws in scientific practice has inspired reforms in science. These reforms seek to address and fix problems in scientific practice which lead to low-quality or inefficient research. A 2015 study lists "fragmented" efforts in meta-research. === Pre-registration === The practice of registering a


id: metascience-20260505-6
file: data/en.wikipedia.org/wiki/Metascience-6.md
category: reference
title: Metascience
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Metascience
date_saved: 2026-05-05T03:44:21.146005+00:00
instance: kb-cron
parent: metascience-20260505-0
summary:
Clinical research in medicine is often of low quality, and many studies cannot be replicated. An estimated 85% of research funding is wasted. Additionally, the presence of bias affects research quality. The pharmaceutical industry exerts substantial influence on the design and execution of medical research. Conflicts of interest are common among authors of medical literature and among editors of medical journals. While almost all medical journals require their authors to disclose conflicts of interest, editors are not required to do so. Financial conflicts of interest have been linked to higher rates of positive study results. In antidepressant trials, pharmaceutical sponsorship is the best predictor of trial outcome. Blinding is another focus of meta-research, as error caused by poor blinding is a source of experimental bias. Blinding is not well reported in medical literature, and widespread misunderstanding of the subject has resulted in poor implementation of blinding in clinical trials. Furthermore, failure of blinding is rarely measured or reported. Research showing the failure of blinding in antidepressant trials has led some scientists to argue that antidepressants are no better than placebo. In light of meta-research showing failures of blinding, CONSORT standards recommend that all clinical trials assess and report the quality


id: list-of-multiple-discoveries-20260505-0
file: data/en.wikipedia.org/wiki/List_of_multiple_discoveries-0.md
category: reference
title: List of multiple discoveries
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/List_of_multiple_discoveries
date_saved: 2026-05-05T03:44:22.470419+00:00
instance: kb-cron
parent: list-of-multiple-discoveries-20260505-0
summary:
Historians and sociologists have remarked the occurrence, in science, of "multiple independent discovery". Robert K. Merton defined such "multiples" as instances in which similar discoveries are made by scientists working independently of each other. "Sometimes", writes Merton, "the discoveries are simultaneous or almost so; sometimes a scientist will make a new discovery which, unknown to him, somebody else has made years before." Commonly cited examples of multiple independent discovery are the 17th-century independent formulation of calculus by Isaac Newton and Gottfried Wilhelm Leibniz; the 18th-century discovery of oxygen by Carl Wilhelm Scheele, Joseph Priestley, Antoine Lavoisier and others; and the theory of the evolution of species, independently advanced in the 19th century by Charles Darwin and Alfred Russel Wallace. Multiple independent discovery, however, is not limited to such famous historic instances. Merton believed that it is multiple discoveries, rather than unique ones, that represent the common pattern in science. Merton contrasted a "multiple" with a "singleton"—a discovery that has been made uniquely by a single scientist or group of scientists working together. The distinction may blur as science becomes increasingly collaborative. A distinction is drawn between a discovery and an invention, as discussed for example by Bolesław Prus. However, discoveries


id: list-of-multiple-discoveries-20260505-1
file: data/en.wikipedia.org/wiki/List_of_multiple_discoveries-1.md
category: reference
title: List of multiple discoveries
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/List_of_multiple_discoveries
date_saved: 2026-05-05T03:44:22.470419+00:00
instance: kb-cron
parent: list-of-multiple-discoveries-20260505-0
summary:
1730: Stirling numbers – James Stirling (1730) and Masanobu Saka (1782). 1740s: Platinum – Antonio de Ulloa and Charles Wood (both in the 1740s). 1745: Leyden Jar – Ewald Georg von Kleist (1745) and Pieter van Musschenbroek (1745–46). 1749: Lightning rod – Benjamin Franklin (1749) and Prokop Diviš (1754) (debated: Diviš's apparatus is assumed to have been more effective than Franklin's lightning rods in 1754, but was intended for a different purpose than lightning protection). 1756: Law of conservation of matter – discovered by Mikhail Lomonosov, 1756; and independently by Antoine Lavoisier, 1778. 1773: Oxygen – Carl Wilhelm Scheele (Uppsala, 1773), Joseph Priestley (Wiltshire, 1774). The term was coined by Antoine Lavoisier (1777). Michael Sendivogius (Polish: Michał Sędziwój; 1566–1636) is claimed as an earlier discoverer of oxygen. 1783: Black-hole theory – John Michell, in a 1783 paper in The Philosophical Transactions of the Royal Society, wrote: "If the semi-diameter of a sphere of the same density as the Sun in the proportion of five hundred to one, and by supposing light to be attracted by the same force in proportion to its [mass] with other bodies, all light emitted from such a body would be made to return towards it,


id: list-of-multiple-discoveries-20260505-2
file: data/en.wikipedia.org/wiki/List_of_multiple_discoveries-2.md
category: reference
title: List of multiple discoveries
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/List_of_multiple_discoveries
date_saved: 2026-05-05T03:44:22.470419+00:00
instance: kb-cron
parent: list-of-multiple-discoveries-20260505-0
summary:
1805: In a treatise written in 1805 and published in 1866, Carl Friedrich Gauss describes an efficient algorithm to compute the discrete Fourier transform. James W. Cooley and John W. Tukey reinvented a similar algorithm in 1965. 1817: Cadmium – Friedrich Strohmeyer, K.S.L Hermann (both in 1817). 1817: Grotthuss–Draper law (aka the Principle of Photochemical Activation) – first proposed in 1817 by Theodor Grotthuss, then independently, in 1842, by John William Draper. The law states that only that light which is absorbed by a system can bring about a photochemical change. 1825: Bromine discovered by Carl Jacob Löwig; and independently, in 1826, by Antoine Jérôme Balard. 1828: Beryllium – Friedrich Wöhler, A.A.B. Bussy (1828). 1830: Non-Euclidean geometry (hyperbolic geometry) – Nikolai Ivanovich Lobachevsky (1830), János Bolyai (1832); preceded by Gauss (unpublished result) c. 1805. 1831: Electromagnetic induction was discovered by Michael Faraday in England in 1831, and independently about the same time by Joseph Henry in the U.S. 1831: Chloroform – Samuel Guthrie in the United States (July 1831), and a few months later Eugène Soubeiran (France) and Justus von Liebig (Germany), all of them using variations of the haloform reaction. Dandelin–Gräffe method, aka Lobachevsky method – an algorithm for


id: list-of-multiple-discoveries-20260505-3
file: data/en.wikipedia.org/wiki/List_of_multiple_discoveries-3.md
category: reference
title: List of multiple discoveries
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/List_of_multiple_discoveries
date_saved: 2026-05-05T03:44:22.470419+00:00
instance: kb-cron
parent: list-of-multiple-discoveries-20260505-0
summary:
E = mc2, though only Einstein provided the accepted interpretation – Henri Poincaré, 1900; Olinto De Pretto, 1903; Albert Einstein, 1905; Paul Langevin, 1906. 1902: Walter Sutton and Theodor Boveri independently proposed that the hereditary information is carried in the chromosomes. 1902: Richard Assmann and Léon Teisserenc de Bort independently discovered the stratosphere. 1904: Epinephrine synthesized independently by Friedrich Stolz and by Henry Drysdale Dakin. 1905: Brownian motion was independently explained by Albert Einstein (in one of his 1905 papers) and by Marian Smoluchowski in 1906. 1905: The Einstein Relation was revealed independently by William Sutherland in 1905, by Albert Einstein in 1905, and by Marian Smoluchowski in 1906. 1905: The chromosomal XY sex-determination system—that males have XY, and females XX, sex chromosomes—was discovered independently by Nettie Stevens, at Bryn Mawr College, and by Edmund Beecher Wilson at Columbia University. 1907: Lutetium discovered independently by French scientist Georges Urbain and by Austrian mineralogist Baron Carl Auer von Welsbach. 1907: Hilbert space representation theorem, also known as Riesz representation theorem, the mathematical justification of the Bra-ket notation in the theory of quantum mechanics – independently proved by Frigyes Riesz and Maurice René Fréchet. 1908: The Hardy–Weinberg principle is a principle of


id: list-of-multiple-discoveries-20260505-4
file: data/en.wikipedia.org/wiki/List_of_multiple_discoveries-4.md
category: reference
title: List of multiple discoveries
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/List_of_multiple_discoveries
date_saved: 2026-05-05T03:44:22.470419+00:00
instance: kb-cron
parent: list-of-multiple-discoveries-20260505-0
summary:
It was originally proved in 1934 by Aleksandr Gelfond, and again independently in 1935 by Theodor Schneider. 1934: The Penrose triangle, also known as the "tribar", is an impossible object. It was first created by the Swedish artist Oscar Reutersvärd in 1934. The mathematician Roger Penrose independently devised and popularized it in the 1950s. 1936: In computer science, the concept of the "universal computing machine" (now generally called the "Turing Machine") was proposed by Alan Turing, but also independently by Emil Post, both in 1936. Similar approaches, also aiming to cover the concept of universal computing, were introduced by S.C. Kleene, Rózsa Péter, and Alonzo Church that same year. Also in 1936, Konrad Zuse tried to build a binary electrically driven mechanical calculator with limited programability; however, Zuse's machine was never fully functional. The later Atanasoff–Berry Computer ("ABC"), designed by John Vincent Atanasoff and Clifford Berry, was the first fully electronic digital computing device; while not programmable, it pioneered important elements of modern computing, including binary arithmetic and electronic switching elements, though its special-purpose nature and lack of a changeable, stored program distinguish it from modern computers. 1938: Benford's law, also known as the Newcomb–Benford law, the law of anomalous


id: list-of-multiple-discoveries-20260505-5
file: data/en.wikipedia.org/wiki/List_of_multiple_discoveries-5.md
category: reference
title: List of multiple discoveries
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/List_of_multiple_discoveries
date_saved: 2026-05-05T03:44:22.470419+00:00
instance: kb-cron
parent: list-of-multiple-discoveries-20260505-0
summary:
1967: The affine scaling method for solving linear programming was discovered by Soviet mathematician I.I. Dikin in 1967. It went unnoticed in the West for two decades, until two groups of researchers in the U.S. reinvented it in 1985. 1968: Neutral theory of molecular evolution was introduced by a Japanese biologist, Motoo Kimura, in 1968, and independently by two American biologists, Jack Lester King and Thomas Hughes Jukes, in 1969. 1969: Thyrotropin-releasing hormone (TRH) structure was determined, and the hormone synthesized, independently by Andrew V. Schally and Roger Guillemin, who shared the 1977 Nobel Prize in Medicine. 1970: Howard Temin and David Baltimore independently discovered reverse transcriptase enzymes. The Knuth–Morris–Pratt string searching algorithm was developed by Donald Knuth and Vaughan Pratt and independently by J. H. Morris. 1971: The Cook–Levin theorem (also known as "Cook's theorem"), a result in computational complexity theory, was proven independently by Stephen Cook (1971 in the U.S.) and by Leonid Levin (1973 in the USSR). Levin was not aware of Cook's achievement because of communication difficulties between East and West during the Cold War. The other way round, Levin's work was not widely known in the West until around 1978. 1972: The Bohlen–Pierce scale, a


id: list-of-multiple-discoveries-20260505-6
file: data/en.wikipedia.org/wiki/List_of_multiple_discoveries-6.md
category: reference
title: List of multiple discoveries
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/List_of_multiple_discoveries
date_saved: 2026-05-05T03:44:22.470419+00:00
instance: kb-cron
parent: list-of-multiple-discoveries-20260505-0
summary:
== 21st century == 2001: four different authors published different implementations of a distributed hash table. The Super Kamiokande and SNOLAB collaborations, whose findings were published in 1998 and 2001 respectively, both proved that neutrinos have mass. As a result, the 2015 Nobel Prize in Physics was shared by Takaaki Kajita of Japan and Arthur B. McDonald of Canada. 1996: James Allison of MD Anderson Cancer Center at the University of Texas at Houston discovered a mechanism enabling cancer immunotherapy. In 2002 Tasuku Honjo of Kyoto University discovered another such mechanism. This outcome, which led to them sharing the 2018 Nobel Prize in Physiology or Medicine, has been described as follows: "Each independently discovered that our immune system is restrained from attacking tumors by molecules that function as 'brakes.' Releasing these brakes (or 'brake receptors') allows our body to powerfully combat cancer." 2014: Paul Erdős' conjecture about prime gaps was proved by Kevin Ford, Ben Green, Sergei Konyagin, and Terence Tao, working together, and independently by James Maynard. 2020: Half of the 2020 Nobel Prize in Physics was awarded to Reinhard Genzel and Andrea Ghez, who each have led a group of astronomers focused since the early 1990s on a


id: the-no-nonsense-guide-to-science-20260505-0
file: data/en.wikipedia.org/wiki/The_No_Nonsense_Guide_To_Science-0.md
category: reference
title: The No Nonsense Guide To Science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/The_No_Nonsense_Guide_To_Science
date_saved: 2026-05-05T03:44:23.815827+00:00
instance: kb-cron
parent: the-no-nonsense-guide-to-science-20260505-0
summary:
The No Nonsense Guide to Science is a 2006 book on Post-normal science (PNS). It was written by American born British historian and philosopher of science Jerome Ravetz. == Main == What should a young person do who aspires to make the world a better place and to make their way in science? This is how this work's ambition was summarized. Written in 2006 by one of the founding fathers of Post-normal Science - the other being Silvio Funtowicz - its 142 pages cover several themes, in part synthesizing previous works such as Scientific Knowledge and Its Social Problems, The Merger of Knowledge with Power, and Uncertainty and Quality in Science for Policy (with Funtowicz), and introduces the ideas of Post-normal Science. Topics include: The problem of science being at once 'little' and big or 'mega', embedded in institutions and corporations The fallibility of science, against a possibly 'dogmatic' teaching of the power of science The democratization of science as a necessary and realistic antidote to its hubris The opportunity of forming extended peer communities - inclusive of whistle blowers and investigative journalists as well as academics and interested stakeholders, when science is called to answer conflicted policy questions. The


id: nursing-process-20260505-0
file: data/en.wikipedia.org/wiki/Nursing_process-0.md
category: reference
title: Nursing process
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Nursing_process
date_saved: 2026-05-05T03:44:25.053005+00:00
instance: kb-cron
parent: nursing-process-20260505-0
summary:
The nursing process is a modified scientific method that is a fundamental part of nursing practices in many countries around the world. Nursing practice was first described as a four-stage nursing process by Ida Jean Orlando in 1958. It should not be confused with nursing theories or health informatics. The diagnosis phase was added later. The nursing process uses clinical judgement to strike a balance of epistemology between personal interpretation and research evidence in which critical thinking may play a part to categorize the clients issue and course of action. Nursing offers diverse patterns of knowing. Nursing knowledge has embraced pluralism since the 1970s. Evidence based practice (EBP) Evidence based practice is a process that is used in the healthcare field to used as a problem-solving approach to make clinical decisions. This is collected by reviewing, analyzing, and forming the best sources for the patient-care. EBP assist with the nursing process by providing credible information that helps nurses make the knowledgeable choice. Person-centered care The nursing process helps orchestrate the nurses' decisions with the patient’s participation needed for recovery. Nurses utilize person-centered care (PCC), which focuses on identifying and addressing a patient's unique needs and preferences. PCC aligns well with


id: observation-20260505-0
file: data/en.wikipedia.org/wiki/Observation-0.md
category: reference
title: Observation
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Observation
date_saved: 2026-05-05T03:44:26.241404+00:00
instance: kb-cron
parent: observation-20260505-0
summary:
Observation in the natural sciences refers to the active acquisition of information from a primary source. It involves the act of noticing or perceiving phenomena and gathering data based on direct engagement with the subject of study. In living organisms, observation typically occurs through the senses. In science, it often extends beyond unaided perception, involving the use of scientific instruments to detect, measure, and record data. This enables the observation of phenomena not accessible to human senses alone. Observations in science are typically categorized as either qualitative or quantitative: Qualitative observations describe characteristics that are not expressed numerically, such as color, texture, or behavior. Quantitative observations involve numerical measurements, obtained through counting or using instruments to assign values to observed phenomena. The term observation may refer both to the process of observing and to the information recorded as a result of that process. == Science == The scientific method requires observations of natural phenomena to formulate and test hypotheses. The method involves an iterative series of steps intended to generate and refine scientific knowledge: Ask a question about a phenomenon Make observations of the phenomenon Formulate a hypothesis that tentatively answers the question Predict logical, observable consequences of the hypothesis


id: observation-20260505-1
file: data/en.wikipedia.org/wiki/Observation-1.md
category: reference
title: Observation
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Observation
date_saved: 2026-05-05T03:44:26.241404+00:00
instance: kb-cron
parent: observation-20260505-0
summary:
== Biases == Human senses do not function like an impartial recording device such as a video camcorder. Perception occurs through a complex, largely unconscious process of abstraction, in which certain elements of sensory input are selected and retained, while others are discarded. This selection process depends on an internal model of the world—referred to in psychology as a schema—that is shaped by past experiences. Sensory information is interpreted and stored based on this schema. During recall, gaps in memory may be unconsciously filled with information consistent with the schema, a process known as reconstructive memory. The degree of attention given to different aspects of a perceptual experience is influenced by an individual's internal value system, which prioritizes information based on perceived importance. As a result, two individuals observing the same event may remember it differently, potentially disagreeing on factual details. This subjectivity is a known limitation of eyewitness testimony, which research has shown to be frequently unreliable. In scientific practice, rigorous methods are employed to minimize such observational biases. These include careful documentation of experimental data, distinguishing clearly between raw observations and inferred conclusions, and implementing procedures such as blind and double blind experiment designs to control for subjective


id: open-label-trial-20260505-0
file: data/en.wikipedia.org/wiki/Open-label_trial-0.md
category: reference
title: Open-label trial
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open-label_trial
date_saved: 2026-05-05T03:44:27.552355+00:00
instance: kb-cron
parent: open-label-trial-20260505-0
summary:
An open-label trial, or open trial, is a type of clinical trial in which information is not withheld from trial participants. In particular, both the researchers and participants know which treatment is being administered. This contrasts with a double-blinded trial, where information is withheld both from the researchers and the participants to reduce bias. Open-label trials may be appropriate for comparing two similar treatments to determine which is most effective, such as a comparison of different prescription anticoagulants, or possible relief from symptoms of some disorders when a placebo is given. An open-label trial may still be randomized. Open-label trials may also be uncontrolled (without a placebo group), with all participants receiving the same treatment. This would be a single-arm study design. == References ==


id: operationalization-20260505-0
file: data/en.wikipedia.org/wiki/Operationalization-0.md
category: reference
title: Operationalization
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Operationalization
date_saved: 2026-05-05T03:44:34.920226+00:00
instance: kb-cron
parent: operationalization-20260505-0
summary:
In research design, especially in psychology, social sciences, life sciences and physics, operationalization (or operationalisation) is a process of defining the measurement of a phenomenon which is not directly measurable, though its existence is inferred from other phenomena. Operationalization thus defines a fuzzy concept so as to make it clearly distinguishable, measurable, and understandable by empirical observation. In a broader sense, it defines the extension of a concept—describing what is and is not an instance of that concept. For example, in medicine, the phenomenon of health might be operationalized by one or more indicators like body mass index or tobacco smoking. As another example, in visual processing the presence of a certain object in the environment could be inferred by measuring specific features of the light it reflects. In these examples, the phenomena are difficult to directly observe and measure because they are general/abstract (as in the example of health) or they are latent (as in the example of the object). Operationalization helps infer the existence, and some elements of the extension, of the phenomena of interest by means of some observable and measurable effects they have. Sometimes multiple or competing alternative operationalizations for the same phenomenon are available. Repeating


id: operationalization-20260505-1
file: data/en.wikipedia.org/wiki/Operationalization-1.md
category: reference
title: Operationalization
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Operationalization
date_saved: 2026-05-05T03:44:34.920226+00:00
instance: kb-cron
parent: operationalization-20260505-0
summary:
=== Anger example === For example, a researcher may wish to measure the concept "anger." Its presence, and the depth of the emotion, cannot be directly measured by an outside observer because anger is intangible. Rather, other measures are used by outside observers, such as facial expression, choice of vocabulary, loudness and tone of voice. If a researcher wants to measure the depth of "anger" in various persons, the most direct operation would be to ask them a question, such as "are you angry", or "how angry are you?". This operation is problematic, however, because it depends upon the definition of the individual. Some people might be subjected to a mild annoyance, and become slightly angry, but describe themselves as "extremely angry," whereas others might be subjected to a severe provocation, and become very angry, but describe themselves as "slightly angry." In addition, in many circumstances it is impractical to ask subjects whether they are angry. Since one of the measures of anger is loudness, the researcher can operationalize the concept of anger by measuring how loudly subjects speak compared to their normal tones. However, this must assume that loudness is a uniform measure. Some might respond verbally while others


id: outline-of-scientific-method-20260505-0
file: data/en.wikipedia.org/wiki/Outline_of_scientific_method-0.md
category: reference
title: Outline of scientific method
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Outline_of_scientific_method
date_saved: 2026-05-05T03:44:36.226294+00:00
instance: kb-cron
parent: outline-of-scientific-method-20260505-0
summary:
The following outline is provided as an overview of and topical guide to the scientific method: Scientific method – body of techniques for investigating phenomena and acquiring new knowledge, as well as for correcting and integrating previous knowledge. It is based on observable, empirical, reproducible, measurable evidence, and subject to the laws of reasoning. == Nature of scientific method == Scientific method Science Philosophy of science Sociology of knowledge Process of science Knowledge == Elements of scientific method == Research Basic research === Observation === Observation Scientific method Causation Investigation Measurement (Outline of metrology and measurement) === Hypothesis === Hypothesis pro:Karl Popper Falsifiability con:Paul Feyerabend Statistical hypothesis testing === Experiment === Experiment Laboratory Laboratory techniques Design of experiments Scientific control Natural experiment Observational study Field experiment Self-experimentation Self-experimentation in medicine Placebo effect === Theory === Scientific theory ==== Prediction ==== Prediction Bayesian inference – subjective use of statistical reasoning Deductive reasoning Retrodiction === Evaluation by scientific community === Peer review Medical peer review == Scientific method concepts == === Empirical methods === Empirical methods Empiricism Robert Grosseteste Peter Parker Empirical validation Operationalization === Use of statistics === Uncomfortable science — Inference from a limited sample of data Exploratory data analysis


id: pasteur-s-quadrant-20260505-0
file: data/en.wikipedia.org/wiki/Pasteur's_quadrant-0.md
category: reference
title: Pasteur's quadrant
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Pasteur's_quadrant
date_saved: 2026-05-05T03:44:37.455516+00:00
instance: kb-cron
parent: pasteur-s-quadrant-20260505-0
summary:
Pasteur's quadrant is a classification of scientific research projects that seek fundamental understanding of scientific problems, while also having immediate use for society. Louis Pasteur's research is thought to exemplify this type of method, which bridges the gap between "basic" and "applied" research. The term was introduced by Donald E. Stokes in his book, Pasteur's Quadrant. == Other quadrants == As shown in the following table, scientific research can be classified by whether it advances human knowledge by seeking a fundamental understanding of nature, or whether it is primarily motivated by the need to solve immediate problems. The result is three distinct classes of research: Pure basic research, exemplified by the work of Niels Bohr, early 20th century atomic physicist. Pure applied research, exemplified by the work of Thomas Edison, inventor. Use-inspired basic research, described here as "Pasteur's Quadrant". == Usage == Pasteur's quadrant is useful in distinguishing various perspectives within science, engineering and technology. For example, Daniel A. Vallero and Trevor M. Letcher in their book Unraveling Environmental Disasters applied the device to disaster preparedness and response. University science programs are concerned with knowledge-building, whereas engineering programs at the same university will apply existing and emerging knowledge to address


id: scholarly-peer-review-20260505-0
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-0.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T03:44:38.682666+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
Scholarly peer review or academic peer review (also known as refereeing) is the process of having a draft version of a researcher's methods and findings reviewed (usually anonymously) by experts (or "peers") in the same field. Peer review is widely used for helping the academic publisher (i.e., the editor-in-chief, the editorial board, or the program committee) decide whether the work should be accepted, considered acceptable with revisions, or rejected for official publication in an academic journal, a monograph, or in the proceedings of an academic conference. If the identities of authors are not revealed to each other, the procedure is called dual-anonymous peer review. Academic peer review requires a community of experts in a given (and often narrowly defined) academic field, who are qualified and able to perform reasonably impartial review. Impartial review, especially of work in less narrowly defined or inter-disciplinary fields, may be difficult to accomplish, and the significance (good or bad) of an idea may never be widely appreciated among its contemporaries. Peer review is generally considered necessary to academic quality and is used in most major scholarly journals. However, peer review does not prevent publication of invalid research, and as experimentally controlled studies of this process


id: scholarly-peer-review-20260505-1
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-1.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T03:44:38.682666+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
Workload. A small group of editors/assessors cannot devote sufficient time to each of the many articles submitted to many journals. Miscellany of ideas. Were the editor/assessor to judge all submitted material themselves, approved material would solely reflect their opinion. Limited expertise. An editor/assessor cannot be expected to be sufficiently expert in all areas covered by a single journal or funding agency to adequately judge all submitted material. Reviewers are often anonymous and independent. However, some reviewers may choose to waive their anonymity, and in other limited circumstances, such as the examination of a formal complaint against the referee, or a court order, the reviewer's identity may have to be disclosed. Anonymity may be unilateral or reciprocal (single- or double-blinded reviewing). Since reviewers are normally selected from experts in the fields discussed in the article, the process of peer review helps to keep some invalid or unsubstantiated claims out of the body of published research and knowledge. Scholars will read published articles outside their limited area of detailed expertise, and then rely, to some degree, on the peer-review process to have provided reliable and credible research that they can build upon for subsequent or related research. Significant scandal ensues when an


id: scholarly-peer-review-20260505-2
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-2.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T03:44:38.682666+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
to unconditionally accept the manuscript or the proposal, to accept it in the event that its authors improve it in certain ways to reject it, but encourage revision and invite re-submission to reject it outright. During this process, the role of the referees is advisory. The editor(s) is typically under no obligation to accept the opinions of the referees. However, the editor will most often follow the advice of the referees. Furthermore, the referees in scientific publication do not act as a group, do not communicate with each other, and typically are not aware of each other's identities or evaluations. Proponents argue that if the reviewers of a paper are unknown to each other, the editor(s) can more easily verify the objectivity of the reviews. There is usually no requirement that the referees achieve consensus, with the decision instead often made by the editor(s) based on her best judgement of the arguments. In situations where multiple referees disagree substantially about the quality of a work, there are a number of strategies for reaching a decision. The paper may be rejected outright, or the editor may choose which reviewer's point the authors should address. When a publisher receives very positive and


id: scholarly-peer-review-20260505-3
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-3.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T03:44:38.682666+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
== Recruiting referees == At a journal or book publisher, the task of picking reviewers typically falls to an editor. When a manuscript arrives, an editor solicits reviews from scholars or other experts who may or may not have already expressed a willingness to referee for that journal or book division. Granting agencies typically recruit a panel or committee of reviewers in advance of the arrival of applications. Referees are supposed to inform the editor of any conflict of interests that might arise. Journals or individual editors may invite a manuscript's authors to name people whom they consider qualified to referee their work. For some journals this is a requirement of submission. Authors are sometimes also given the opportunity to name natural candidates who should be disqualified, in which case they may be asked to provide justification (typically expressed in terms of conflict of interest). Editors solicit author input in selecting referees because academic writing typically is very specialized. Editors often oversee many specialties, and can not be experts in all of them. But after an editor selects referees from the pool of candidates, the editor typically is obliged not to disclose the referees' identities to the authors, and in


id: scholarly-peer-review-20260505-4
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-4.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T03:44:38.682666+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
=== Internal and external peer review === External peer review is the ordinary process of peer review for scholarly journals. The first evidence of formal external peer reviews dates back to 1752, and it became widely used and more standardized after World War II. In this process, scholars who are not beholden to the journal or the author review submitted articles, and in doing so, restrain academic and scientific journals from using solely their own commercial interests as a guide on what to publish. Nature began requiring external peer review in 1973, and other journals quickly followed suit. Internal peer review is having a colleague in the same department or academic institution read over a paper before submitting it to a journal for external peer review. For example, economists in the same government agency may review their peers' papers, as a type of peer professional accountability. People such as Nobel laureate Dan Shechtman and organizations such as the US National Institute of Standards and Technology use internal peer review because they believe that it gives them an opportunity to improve papers before submitting them to a journal. Internal peer review is also recommended for authors with weak writing skills. Unlike


id: scholarly-peer-review-20260505-5
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-5.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T03:44:38.682666+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
=== Transparent peer review === In transparent peer review the documents relating to the publication of the paper are published alongside the paper. This includes the initial report made by the peer reviewer, the authors' response to it, and the acceptance letter by the editor. The names of the reviewers may still be kept anonymous even in transparent peer review. === Pre- and post-publication peer review === The process of peer review is not restricted to the publication process managed by academic journals. In particular, some forms of peer review can occur before an article is submitted to a journal and/or after it is published by the journal. ==== Pre-publication peer review ==== Manuscripts are typically reviewed by colleagues before submission, and if the manuscript is uploaded to preprint servers, such as ArXiv, BioRxiv or SSRN, researchers can read and comment on the manuscript. The practice to upload to preprint servers, and the activity of discussion heavily depend on the field, and it allows an open pre-publication peer review. The advantage of this method is speed and transparency of the review process. Anyone can give feedback, typically in form of comments, and typically not anonymously. These comments are also public,


id: scholarly-peer-review-20260505-6
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-6.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T03:44:38.682666+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
Result-blind peer review or results blind peer review, first proposed 1966: Reviewers receive an edited version of the submitted paper which omits the results and conclusion section. In a two-stage version, a second round of reviews or editorial judgment is based on the full paper version, which was first proposed in 1977. Conclusion-blind review, proposed by Robin Hanson in 2007 extends this further asking all authors to submit a positive and a negative version, and only after the journal has accepted the article authors reveal which is the real version. Pre-accepted articles or outcome-unbiased journals or advance publication review or registered reports or prior to results submission or early acceptance extends study pre-registration to the point that journals accepted or reject papers based on the version of the paper written before the results or conclusions have been made (an enlarged study protocol), but instead describes the theoretical justification, experimental design, and statistical analysis. Only once the proposed hypothesis and methodology have been accepted by reviewers, the authors would collect the data or analyze previously collected data. A limited variant of a pre-accepted article was The Lancet's study protocol review from 1997 to 2015 reviewed and published randomized trial protocols with


id: scholarly-peer-review-20260505-7
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-7.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T03:44:38.682666+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
Scholarly peer review has been subject to several criticisms, and various proposals for reforming the system have been suggested over the years. Many studies have emphasized the problems inherent to the process of peer review. Moreover, Ragone et al. have shown that there is a low correlation between peer review outcomes and the future impact measured by citations. Various biomedical editors in particular have expressed criticism of peer review. A Cochrane review found little empirical evidence that peer review ensures quality in biomedical research, while a second systematic review and meta-analysis found a need for evidence-based peer review in biomedicine given the paucity of assessment of the interventions designed to improve the process. To an outsider, the anonymous, pre-publication peer review process is opaque. Certain journals are accused of not carrying out stringent peer review in order to more easily expand their customer base, particularly in journals where authors pay a fee before publication. Richard Smith, MD, former editor of the British Medical Journal, has claimed that peer review is "ineffective, largely a lottery, anti-innovatory, slow, expensive, wasteful of scientific time, inefficient, easily abused, prone to bias, unable to detect fraud and irrelevant; Several studies have shown that peer review


id: scholarly-peer-review-20260505-8
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-8.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T03:44:38.682666+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
=== Peer review and trust === Researchers have peer-reviewed manuscripts prior to publishing them in a variety of ways since the 18th century. The main goal of this practice is to improve the relevance and accuracy of scientific discussions. Even though experts often criticize peer review for a number of reasons, the process is still often considered the "gold standard" of science. Occasionally however, peer review approves studies that are later found to be wrong and rarely deceptive or fraudulent results are discovered prior to publication. Thus, there seems to be an element of discord between the ideology behind and the practice of peer review. By failing to effectively communicate that peer review is imperfect, the message conveyed to the wider public is that studies published in peer-reviewed journals are "true" and that peer review protects the literature from flawed science. A number of well-established criticisms exist of many elements of peer review. In the following we describe cases of the wider impact inappropriate peer review can have on public understanding of scientific literature. Multiple examples across several areas of science find that scientists elevated the importance of peer review for research that was questionable or corrupted. For example, climate


id: scholarly-peer-review-20260505-9
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-9.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T03:44:38.682666+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
The belief that scholars are incapable of evaluating the quality of work on their own, that they are in need of a gatekeeper to inform them of what is good and what is not. The belief that scholars need a "guardian" to make sure they are doing good work. Others argue that authors most of all have a vested interest in the quality of a particular piece of work. Only the authors could have, as Feynman (1974) puts it, the "extra type of integrity that is beyond not lying, but bending over backwards to show how you're maybe wrong, that you ought to have when acting as a scientist." If anything, the current peer review process and academic system could penalize, or at least fail to incentivize, such integrity. Instead, the credibility conferred by the "peer-reviewed" label could diminish what Feynman calls the culture of doubt necessary for science to operate a self-correcting, truth-seeking process. The effects of this can be seen in the ongoing replication crisis, hoaxes, and widespread outrage over the inefficacy of the current system. It's common to think that more oversight is the answer, as peer reviewers are not at all lacking in skepticism. But the


id: scholarly-peer-review-20260505-10
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-10.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T03:44:38.682666+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
=== Allegations of bias and suppression === The interposition of editors and reviewers between authors and readers may enable the intermediators to act as gatekeepers. Some sociologists of science argue that peer review makes the ability to publish susceptible to control by elites and to personal jealousy. The peer review process may sometimes impede progress and may be biased against novelty. A linguistic analysis of review reports suggests that reviewers focus on rejecting the applications by searching for weak points, and not on finding the high-risk/high-gain groundbreaking ideas that may be in the proposal. Reviewers tend to be especially critical of conclusions that contradict their own views, and lenient towards those that match them. At the same time, established scientists are more likely than others to be sought out as referees, particularly by high-prestige journals/publishers. As a result, ideas that harmonize with the established experts' are more likely to see print and to appear in premier journals than are iconoclastic or revolutionary ones. This accords with Thomas Kuhn's well-known observations regarding scientific revolutions. A theoretical model has been established whose simulations imply that peer review and over-competitive research funding foster mainstream opinion to monopoly. Criticisms of traditional anonymous peer review


id: scholarly-peer-review-20260505-11
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-11.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T03:44:38.682666+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
==== Plagiarism ==== Reviewers generally lack access to raw data, but do see the full text of the manuscript, and are typically familiar with recent publications in the area. Thus, they are in a better position to detect plagiarism of prose than fraudulent data. A few cases of such textual plagiarism by historians, for instance, have been widely publicized. On the scientific side, a poll of 3,247 scientists funded by the U.S. National Institutes of Health found 0.3% admitted faking data and 1.4% admitted plagiarism. Additionally, 4.7% of the same poll admitted to self-plagiarism or autoplagiarism, in which an author republishes the same material, data, or text, without citing their earlier work. Self-plagiarisms are less likely to be detected in double-blinded peer reviews. ==== Examples ==== "Perhaps the most widely recognized failure of peer review is its inability to ensure the identification of high-quality work. The list of important scientific papers that were rejected by some peer-reviewed journals goes back at least as far as the editor of Philosophical Transaction's 1796 rejection of Edward Jenner's report of the first vaccination against smallpox." The Soon and Baliunas controversy involved the publication in 2003 of a review study written by aerospace engineer


id: personoid-20260505-0
file: data/en.wikipedia.org/wiki/Personoid-0.md
category: reference
title: Personoid
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Personoid
date_saved: 2026-05-05T03:44:39.870655+00:00
instance: kb-cron
parent: personoid-20260505-0
summary:
Personoid is the concept coined by Stanisław Lem, a Polish science-fiction writer, in Non Serviam, from his book A Perfect Vacuum (1971). His personoids are an abstraction of functions of human mind and they live in computers; they do not need any human-like physical body. In cognitive and software modeling, personoid is a research approach to the development of intelligent autonomous agents. In frame of the IPK (Information, Preferences, Knowledge) architecture, it is a framework of abstract intelligent agent with a cognitive and structural intelligence. It can be seen as an essence of high intelligent entities. From the philosophical and systemics perspectives, personoid societies can also be seen as the carriers of a culture. According to N. Gessler, the personoids study can be a base for the research on artificial culture and culture evolution. == Personoids on TV and cinema == Welt am Draht (1973) The Thirteenth Floor (1999) == See also == Android Humanoid Intelligence Artificial Intelligence Culture Computer Science Cognitive Science Anticipatory science Memetics == References == Stanisław Lem's book Próżnia Doskonała (1971). The collection of book reviews of nonexistent books. Translated into English by Michael Kandel as A Perfect Vacuum (1983). Personetics. Personoids Organizations Framework: An Approach


id: pilot-experiment-20260505-0
file: data/en.wikipedia.org/wiki/Pilot_experiment-0.md
category: reference
title: Pilot experiment
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Pilot_experiment
date_saved: 2026-05-05T03:44:41.187055+00:00
instance: kb-cron
parent: pilot-experiment-20260505-0
summary:
A pilot experiment, pilot study, pilot test or pilot project is a small-scale preliminary study conducted to evaluate feasibility, duration, cost, adverse events, and improve upon the study design prior to performance of a full-scale research project. == Implementation == Pilot experiments are frequently carried out before large-scale quantitative research, in an attempt to avoid time and money being used on an inadequately designed project. A pilot study is usually carried out on members of the relevant population. A pilot study is used to formulate the design of the full-scale experiment which then can be adjusted. The pilot study is potentially a critical insight to clinical trial design, recruitment and sample size of participants, treatment testing, and statistical analysis to improve the power of testing the hypothesis of the study. Analysis from the pilot experiment can be added to the full-scale (and more expensive) experiment to improve the chances of a clear outcome. == Applications == In sociology, pilot studies can be referred to as small-scale studies that will help identify design issues before the main research is done. Although pilot experiments have a well-established tradition, their usefulness as a strategy for change has been questioned, at least in the


id: positivism-20260505-0
file: data/en.wikipedia.org/wiki/Positivism-0.md
category: reference
title: Positivism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Positivism
date_saved: 2026-05-05T03:44:42.580777+00:00
instance: kb-cron
parent: positivism-20260505-0
summary:
Positivism is a philosophical school that holds that all genuine knowledge is either true by definition or positive – the latter meaning a posteriori facts derived by reason and logic from sensory experience. Other ways of knowing, such as intuition, introspection, or religious faith, are rejected or considered meaningless. Although the positivist approach has been a recurrent theme in the history of Western thought, modern positivism was first articulated in the early 19th century by Auguste Comte. His school of sociological positivism holds that society, like the physical world, operates according to scientific laws. After Comte, positivist schools arose in logic, psychology, economics, historiography, and other fields of thought. Generally, positivists attempted to introduce scientific methods to their respective fields. Since the turn of the 20th century, positivism, although still popular, has declined under criticism within the social sciences by antipositivists and critical theorists, among others, for its alleged scientism, reductionism, overgeneralizations, and methodological limitations. Positivism also exerted an unusual influence on Kardecism. == Etymology == The English noun positivism in this meaning was imported in the 19th century from the French word positivisme, derived from positif in its philosophical sense of 'imposed on the mind by experience'. The corresponding


id: positivism-20260505-1
file: data/en.wikipedia.org/wiki/Positivism-1.md
category: reference
title: Positivism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Positivism
date_saved: 2026-05-05T03:44:42.580777+00:00
instance: kb-cron
parent: positivism-20260505-0
summary:
The most important thing to determine was the natural order in which the sciences stand—not how they can be made to stand, but how they must stand, irrespective of the wishes of any one. ... This Comte accomplished by taking as the criterion of the position of each the degree of what he called "positivity," which is simply the degree to which the phenomena can be exactly determined. This, as may be readily seen, is also a measure of their relative complexity, since the exactness of a science is in inverse proportion to its complexity. The degree of exactness or positivity is, moreover, that to which it can be subjected to mathematical demonstration, and therefore mathematics, which is not itself a concrete science, is the general gauge by which the position of every science is to be determined. Generalizing thus, Comte found that there were five great groups of phenomena of equal classificatory value but of successively decreasing positivity. To these he gave the names astronomy, physics, chemistry, biology, and sociology. Comte offered an account of social evolution, proposing that society undergoes three phases in its quest for the truth according to a general "law of three stages". Comte intended


id: positivism-20260505-2
file: data/en.wikipedia.org/wiki/Positivism-2.md
category: reference
title: Positivism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Positivism
date_saved: 2026-05-05T03:44:42.580777+00:00
instance: kb-cron
parent: positivism-20260505-0
summary:
Comte's fame today owes in part to Emile Littré, who founded The Positivist Review in 1867. As an approach to the philosophy of history, positivism was appropriated by historians such as Hippolyte Taine. Many of Comte's writings were translated into English by the Whig writer, Harriet Martineau, regarded by some as the first female sociologist. Debates continue to rage as to how much Comte appropriated from the work of his mentor, Saint-Simon. He was nevertheless influential: Brazilian thinkers turned to Comte's ideas about training a scientific elite in order to flourish in the industrialization process. Brazil's national motto, Ordem e Progresso ("Order and Progress") was taken from the positivism motto, "Love as principle, order as the basis, progress as the goal", which was also influential in Poland. In later life, Comte developed a 'religion of humanity' for positivist societies in order to fulfil the cohesive function once held by traditional worship. In 1849, he proposed a calendar reform called the 'positivist calendar'. For close associate John Stuart Mill, it was possible to distinguish between a "good Comte" (the author of the Course in Positive Philosophy) and a "bad Comte" (the author of the secular-religious system). The system was unsuccessful but


id: positivism-20260505-3
file: data/en.wikipedia.org/wiki/Positivism-3.md
category: reference
title: Positivism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Positivism
date_saved: 2026-05-05T03:44:42.580777+00:00
instance: kb-cron
parent: positivism-20260505-0
summary:
=== Historical positivism === In historiography, historical or documentary positivism is the belief that historians should pursue the objective truth of the past by allowing historical sources to "speak for themselves", without additional interpretation. In the words of the French historian Fustel de Coulanges, as a positivist, "It is not I who am speaking, but history itself". The heavy emphasis placed by historical positivists on documentary sources led to the development of methods of source criticism, which seek to expunge bias and uncover original sources in their pristine state. The origin of the historical positivist school is particularly associated with the 19th-century German historian Leopold von Ranke, who argued that the historian should seek to describe historical truth "wie es eigentlich gewesen ist" ("as it actually was")—though subsequent historians of the concept, such as Georg Iggers, have argued that its development owed more to Ranke's followers than Ranke himself. Historical positivism was critiqued in the 20th century by historians and philosophers of history from various schools of thought, including Ernst Kantorowicz in Weimar Germany—who argued that "positivism ... faces the danger of becoming Romantic when it maintains that it is possible to find the Blue Flower of truth without preconceptions"—and


id: positivism-20260505-4
file: data/en.wikipedia.org/wiki/Positivism-4.md
category: reference
title: Positivism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Positivism
date_saved: 2026-05-05T03:44:42.580777+00:00
instance: kb-cron
parent: positivism-20260505-0
summary:
== Criticism == Historically, positivism has been criticized for its reductionism, i.e., for contending that all "processes are reducible to physiological, physical or chemical events," "social processes are reducible to relationships between and actions of individuals," and that "biological organisms are reducible to physical systems." The consideration that laws in physics may not be absolute but relative, and, if so, this might be even more true of social sciences, was stated, in different terms, by G. B. Vico in 1725. Vico, in contrast to the positivist movement, asserted the superiority of the science of the human mind (the humanities, in other words), on the grounds that natural sciences tell us nothing about the inward aspects of things. Wilhelm Dilthey fought strenuously against the assumption that only explanations derived from science are valid. He reprised Vico's argument that scientific explanations do not reach the inner nature of phenomena and it is humanistic knowledge that gives us insight into thoughts, feelings and desires. Dilthey was in part influenced by the historism of Leopold von Ranke (1795–1886). The contesting views over positivism are reflected both in older debates (see the Positivism dispute) and current ones over the proper role of science in the


id: positivism-20260505-5
file: data/en.wikipedia.org/wiki/Positivism-5.md
category: reference
title: Positivism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Positivism
date_saved: 2026-05-05T03:44:42.580777+00:00
instance: kb-cron
parent: positivism-20260505-0
summary:
Although Karl Marx's theory of historical materialism drew upon positivism, the Marxist tradition would also go on to influence the development of antipositivist critical theory. Critical theorist Jürgen Habermas critiqued pure instrumental rationality (in its relation to the cultural "rationalisation" of the modern West) as a form of scientism, or science "as ideology". He argued that positivism may be espoused by "technocrats" who believe in the inevitability of social progress through science and technology. New movements, such as critical realism, have emerged in order to reconcile postpositivist aims with various so-called 'postmodern' perspectives on the social acquisition of knowledge. Max Horkheimer criticized the classic formulation of positivism on two grounds. First, he claimed that it falsely represented human social action. The first criticism argued that positivism systematically failed to appreciate the extent to which the so-called social facts it yielded did not exist 'out there', in the objective world, but were themselves a product of socially and historically mediated human consciousness. Positivism ignored the role of the 'observer' in the constitution of social reality and thereby failed to consider the historical and social conditions affecting the representation of social ideas. Positivism falsely represented the object of study by reifying social


id: positivism-20260505-6
file: data/en.wikipedia.org/wiki/Positivism-6.md
category: reference
title: Positivism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Positivism
date_saved: 2026-05-05T03:44:42.580777+00:00
instance: kb-cron
parent: positivism-20260505-0
summary:
== Positivism today == Echoes of the "positivist" and "antipositivist" debate persist today, though this conflict is hard to define. Authors writing in different epistemological perspectives do not phrase their disagreements in the same terms and rarely actually speak directly to each other. To complicate the issues further, few practising scholars explicitly state their epistemological commitments, and their epistemological position thus has to be guessed from other sources such as choice of methodology or theory. However, no perfect correspondence between these categories exists, and many scholars critiqued as "positivists" are actually postpositivists. One scholar has described this debate in terms of the social construction of the "other", with each side defining the other by what it is not rather than what it is, and then proceeding to attribute far greater homogeneity to their opponents than actually exists. Thus, it is better to understand this not as a debate but as two different arguments: the "antipositivist" articulation of a social meta-theory which includes a philosophical critique of scientism, and "positivist" development of a scientific research methodology for sociology with accompanying critiques of the reliability and validity of work that they see as violating such standards. Strategic positivism aims to bridge these


id: positivism-20260505-7
file: data/en.wikipedia.org/wiki/Positivism-7.md
category: reference
title: Positivism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Positivism
date_saved: 2026-05-05T03:44:42.580777+00:00
instance: kb-cron
parent: positivism-20260505-0
summary:
Any sound scientific theory, whether of time or of any other concept, should in my opinion be based on the most workable philosophy of science: the positivist approach put forward by Karl Popper and others. According to this way of thinking, a scientific theory is a mathematical model that describes and codifies the observations we make. A good theory will describe a large range of phenomena on the basis of a few simple postulates and will make definite predictions that can be tested. ... If one takes the positivist position, as I do, one cannot say what time actually is. All one can do is describe what has been found to be a very good mathematical model for time and say what predictions it makes. == See also == Cliodynamics Científico Charvaka Determinism Gödel's incompleteness theorems London Positivist Society Nature versus nurture Physics envy Scientific politics Sociological naturalism The New Paul and Virginia Vladimir Solovyov == Notes == == References == Amory, Frederic. "Euclides da Cunha and Brazilian Positivism". Luso-Brazilian Review. 36 (1 (Summer 1999)): 87–94. Armenteros, Carolina. 2017. "The Counterrevolutionary Comte: Theorist of the Two Powers and Enthusiastic Medievalist." In The Anthem Companion to Auguste Comte, edited by Andrew


id: post-normal-science-20260505-0
file: data/en.wikipedia.org/wiki/Post-normal_science-0.md
category: reference
title: Post-normal science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Post-normal_science
date_saved: 2026-05-05T03:44:43.904474+00:00
instance: kb-cron
parent: post-normal-science-20260505-0
summary:
Post-normal science (PNS) was developed in the 1990s by Silvio Funtowicz and Jerome R. Ravetz. It is a problem-solving strategy appropriate when "facts [are] uncertain, values in dispute, stakes high and decisions urgent", conditions often present in policy-relevant research. In those situations, PNS recommends suspending temporarily the traditional scientific ideal of truth, concentrating on quality as assessed by internal and extended peer communities. PNS can be considered as complementing the styles of analysis based on risk and cost-benefit analysis prevailing at that time and integrating concepts of a new critical science developed in previous works by the same authors. PNS is not a new scientific method following Aristotle and Bacon, a new paradigm in the Kuhnian sense, or an attempt to reach a new 'normal'. It is instead, a set of insights to guide actionable and robust knowledge production for policy decision making and action in challenges like pandemics, ecosystems collapse, biodiversity loss and, in general, sustainability transitions. == Context == According to its proponents Silvio Funtowicz and Jerome R. Ravetz, the name "post-normal science" echoes the seminal work on modern science by Thomas Kuhn. For Carrozza PNS can be "framed in terms of a call for the 'democratization of


id: post-normal-science-20260505-1
file: data/en.wikipedia.org/wiki/Post-normal_science-1.md
category: reference
title: Post-normal science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Post-normal_science
date_saved: 2026-05-05T03:44:43.904474+00:00
instance: kb-cron
parent: post-normal-science-20260505-0
summary:
== Applications == Beside its dominating influence in the literature on 'futures', PNS is considered to have influenced the ecological 'conservation versus preservation debate', especially via its reading by American pragmatist Bryan G. Norton. According to Jozef Keulartz the PNS concept of "extended peer community" influenced how Norton's developed his 'convergence hypothesis'. The hypothesis posits that ecologists of different orientation will converge once they start thinking 'as a mountain', or as a planet. For Norton this will be achieved via deliberative democracy, which will pragmatically overcome the black and white divide between conservationists and preservationists. More recently it has been argued that conservation science, embedded as it is in a multi-layered governance structures of policy-makers, practitioners, and stakeholders, is itself an 'extended peer community', and as a result conservation has always been 'post-normal'. Other authors attribute to PNS the role of having stimulated the take up of transdisciplinary methodological frameworks, reliant on the social constructivist perspective embedded in PNS. Post-normal science is intended as applicable to most instances where the use of evidence is contested due to different norms and values. Typical instances are in the use of evidence based policy and in evaluation. As summarized in a recent work


id: postnormal-times-20260505-0
file: data/en.wikipedia.org/wiki/Postnormal_times-0.md
category: reference
title: Postnormal times
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Postnormal_times
date_saved: 2026-05-05T03:44:45.401782+00:00
instance: kb-cron
parent: postnormal-times-20260505-0
summary:
Postnormal times (PNT) is a concept developed by Ziauddin Sardar as a development of post-normal science. Sardar describes the present as "postnormal times", "in an in-between period where old orthodoxies are dying, new ones have yet to be born, and very few things seem to make sense." == Context == In support of engaging communities of various scope and scale on how to best navigate PNT and imagine preferred pathways toward the future(s), Sardar and Sweeney published an article in the journal Futures outlining The Three Tomorrows method, which fills a gap in the field as "many methods of futures and foresight seldom incorporate pluralism and diversity intrinsically in their frameworks, and few, if any, emphasize the dynamic and merging nature of futures possibilities, or highlight the ignorance and uncertainties we constantly confront". == Theory and criticism == Rakesh Kapoor criticized PNT in 2011 as a Western concept that does not apply to India and other emerging markets. Sam Cole criticised the three Cs of PNT (chaos, complexity and contradictions) as "Alliterative Logic, theorizing through alliterative word-triads that is not based on empirical evidence". Jay Gary has suggested that PNT is embryonic, needs a more robust framework, and should be


id: prediction-20260505-0
file: data/en.wikipedia.org/wiki/Prediction-0.md
category: reference
title: Prediction
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Prediction
date_saved: 2026-05-05T03:44:50.620904+00:00
instance: kb-cron
parent: prediction-20260505-0
summary:
A prediction (from Latin prae- 'before' and dictum 'something said') or forecast is a statement about a future event or about future data. Predictions are often, but not always, based upon experience or knowledge of forecasters. There is no universal agreement about the exact difference between "prediction" and "estimation"; different authors and disciplines ascribe different connotations. Future events are necessarily uncertain, so guaranteed accurate information about the future is impossible. Prediction can be useful to assist in making plans about possible developments. == Opinion == In a non-statistical sense, the term "prediction" is often used to refer to an informed guess or opinion. A prediction of this kind might be informed by a predicting person's abductive reasoning, inductive reasoning, deductive reasoning, and experience; and may be useful—if the predicting person is a knowledgeable person in the field. The Delphi method is a technique for eliciting such expert-judgement-based predictions in a controlled way. This type of prediction might be perceived as consistent with statistical techniques in the sense that, at minimum, the "data" being used is the predicting expert's cognitive experiences forming an intuitive "probability curve." == Statistics == In statistics, prediction is a part of statistical inference. One particular approach


id: prediction-20260505-1
file: data/en.wikipedia.org/wiki/Prediction-1.md
category: reference
title: Prediction
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Prediction
date_saved: 2026-05-05T03:44:50.620904+00:00
instance: kb-cron
parent: prediction-20260505-0
summary:
In science, a prediction is a rigorous, often quantitative, statement, forecasting what would be observed under specific conditions; for example, according to theories of gravity, if an apple fell from a tree it would be seen to move towards the center of the Earth with a specified and constant acceleration. The scientific method is built on testing statements that are logical consequences of scientific theories. This is done through repeatable experiments or observational studies. A scientific theory whose predictions are contradicted by observations and evidence will be rejected. New theories that generate many new predictions can more easily be supported or falsified (see predictive power). Notions that make no testable predictions are usually considered not to be part of science (protoscience or nescience) until testable predictions can be made. Mathematical equations and models, and computer models, are frequently used to describe the past and future behaviour of a process within the boundaries of that model. In some cases the probability of an outcome, rather than a specific outcome, can be predicted, for example in much of quantum physics. In microprocessors, branch prediction permits avoidance of pipeline emptying at branch instructions. In engineering, possible failure modes are predicted and avoided by


id: prediction-20260505-2
file: data/en.wikipedia.org/wiki/Prediction-2.md
category: reference
title: Prediction
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Prediction
date_saved: 2026-05-05T03:44:50.620904+00:00
instance: kb-cron
parent: prediction-20260505-0
summary:
== Sports == Predicting the outcome of sporting events is a business which has grown in popularity in recent years. Handicappers predict the outcome of games using a variety of mathematical formulas, simulation models or qualitative analysis. Early, well known sports bettors, such as Jimmy the Greek, were believed to have access to information that gave them an edge. Information ranged from personal issues, such as gambling or drinking to undisclosed injuries; anything that may affect the performance of a player on the field. Recent times have changed the way sports are predicted. Predictions now typically consist of two distinct approaches: Situational plays and statistical based models. Situational plays are much more difficult to measure because they usually involve the motivation of a team. Dan Gordon, noted handicapper, wrote "Without an emotional edge in a game in addition to value in a line, I won't put my money on it". These types of plays consist of: Betting on the home underdog, betting against Monday Night winners if they are a favorite next week, betting the underdog in "look ahead" games etc. As situational plays become more widely known they become less useful because they will impact the way the line


id: prediction-20260505-3
file: data/en.wikipedia.org/wiki/Prediction-3.md
category: reference
title: Prediction
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Prediction
date_saved: 2026-05-05T03:44:50.620904+00:00
instance: kb-cron
parent: prediction-20260505-0
summary:
Predictions have often been made, from antiquity until the present, by using paranormal or supernatural means such as prophecy or by observing omens. Methods including water divining, astrology, numerology, fortune telling, interpretation of dreams, and many other forms of divination, have been used for millennia to attempt to predict the future. These means of prediction have not been proven by scientific experiments. In literature, vision and prophecy are literary devices used to present a possible timeline of future events. They can be distinguished by vision referring to what an individual sees happen. The book of Revelation, in the New Testament, thus uses vision as a literary device in this regard. It is also prophecy or prophetic literature when it is related by an individual in a sermon or other public forum. Divination is the attempt to gain insight into a question or situation by way of an occultic standardized process or ritual. It is an integral part of witchcraft and has been used in various forms for thousands of years. Diviners ascertain their interpretations of how a querent should proceed by reading signs, events, or omens, or through alleged contact with a supernatural agency, most often described as an angel


id: preference-test-20260505-0
file: data/en.wikipedia.org/wiki/Preference_test-0.md
category: reference
title: Preference test
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Preference_test
date_saved: 2026-05-05T03:44:51.746709+00:00
instance: kb-cron
parent: preference-test-20260505-0
summary:
A preference test is an experiment in which animals are allowed free access to multiple environments which differ in one or more ways. Various aspects of the animal's behaviour can be measured with respect to the alternative environments, such as latency and frequency of entry, duration of time spent, range of activities observed, or relative consumption of a goal object in the environment. These measures can be recorded either by the experimenter or by motion detecting software. Strength of preference can be inferred by the magnitude of the difference in the response, but see "Advantages and disadvantages" below. Statistical testing is used to determine whether observed differences in such measures support the conclusion that preference or aversion has occurred. Prior to testing, the animals are usually given the opportunity to explore the environments to habituate and reduce the effects of novelty. Preference tests can be used to test for preferences of only one characteristic of an environment, e.g. cage colour, or multiple characteristics e.g. a choice between hamster wheel, Habitrail tunnels or additional empty space for extended locomotion. == Types of test == === Two choices === The simplest of preference tests offers a choice between two alternatives. This can


id: principle-of-similitude-20260505-0
file: data/en.wikipedia.org/wiki/Principle_of_similitude-0.md
category: reference
title: Principle of similitude
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Principle_of_similitude
date_saved: 2026-05-05T03:44:52.932326+00:00
instance: kb-cron
parent: principle-of-similitude-20260505-0
summary:
The principle of similitude is a supplement to the scientific method advocated by John Strutt (Lord Rayleigh) (1842–1919) that requires that any suggested scientific law be examined for its relationship to similar laws. The principle of similitude is very often used in scientific studies. For example, the study of blood flow known as hemodynamics. This study sometimes require the modeling of blood vessels in vitro. Here, the principle of similitude plays a huge role in ensuring the conditions of the model reproduces all aspects of behavior as the blood vessel in study. Various techniques are implemented when applying the principle of similitude. A well known technique is dimensional analysis. == References == Lord Rayleigh (1915). "The principle of similitude". Nature. 95 (66): 591. Bibcode:1915Natur..95...66R. doi:10.1038/095066c0.


id: probabilistic-logic-20260505-0
file: data/en.wikipedia.org/wiki/Probabilistic_logic-0.md
category: reference
title: Probabilistic logic
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Probabilistic_logic
date_saved: 2026-05-05T03:44:54.389289+00:00
instance: kb-cron
parent: probabilistic-logic-20260505-0
summary:
Probabilistic logic (also probability logic and probabilistic reasoning) involves the use of probability and logic to deal with uncertain situations. Probabilistic logic extends traditional logic truth tables with probabilistic expressions. A difficulty of probabilistic logics is their tendency to multiply the computational complexities of their probabilistic and logical components. Other difficulties include the possibility of counter-intuitive results, such as in case of belief fusion in Dempster–Shafer theory. Source trust and epistemic uncertainty about the probabilities they provide, such as defined in subjective logic, are additional elements to consider. The need to deal with a broad variety of contexts and issues has led to many different proposals. == Logical background == There are numerous proposals for probabilistic logics. Very roughly, they can be categorized into two different classes: those logics that attempt to make a probabilistic extension to logical entailment, such as Markov logic networks, and those that attempt to address the problems of uncertainty and lack of evidence (evidentiary logics). That the concept of probability can have different meanings may be understood by noting that, despite the mathematization of probability in the Enlightenment, mathematical probability theory remains, to this very day, entirely unused in criminal courtrooms, when evaluating the "probability"


id: probabilistic-logic-20260505-1
file: data/en.wikipedia.org/wiki/Probabilistic_logic-1.md
category: reference
title: Probabilistic logic
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Probabilistic_logic
date_saved: 2026-05-05T03:44:54.389289+00:00
instance: kb-cron
parent: probabilistic-logic-20260505-0
summary:
The term "probabilistic logic" was first used by John von Neumann in a series of Caltech lectures 1952 and 1956 paper "Probabilistic logics and the synthesis of reliable organisms from unreliable components", and subsequently in a paper by Nils Nilsson published in 1986, where the truth values of sentences are probabilities. The proposed semantical generalization induces a probabilistic logical entailment, which reduces to ordinary logical entailment when the probabilities of all sentences are either 0 or 1. This generalization applies to any logical system for which the consistency of a finite set of sentences can be established. Gaifman and Snir have developed a globally consistent and empirically satisfactory unification of classic probability theory and first-order logic that is suitable for inductive reasoning. Their theory assigns probabilities or degrees of beliefs to sentences consistent with the knowledge base (probability 1 for facts and axioms), consistent with the standard (Kolmogorov) probability axioms and logical deduction, and allows (Bayesian) inductive reasoning and learning in the limit. Most importantly, unlike most alternative proposals, it allows confirmation of universally quantified hypotheses. The theory has also been extended to higher-order logic. Both solutions are purely theoretical but have spawned practical approximations. The central concept in the


id: progressive-contextualization-20260505-0
file: data/en.wikipedia.org/wiki/Progressive_contextualization-0.md
category: reference
title: Progressive contextualization
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Progressive_contextualization
date_saved: 2026-05-05T03:44:55.585570+00:00
instance: kb-cron
parent: progressive-contextualization-20260505-0
summary:
Progressive contextualization (PC) is a scientific method pioneered and developed by Andrew P. Vayda and research team between 1979 and 1984. The method was developed to help understand cause of damage and destruction of forest and land during the New Order Regime in Indonesia, as well as practical ethnography. Vayda proposed the Progressive contextualization method due to his dissatisfaction with several conventional anthropological methods to describe accurately and quickly cases of illegal logging, land destruction and the network of actor-investor protecting the actions, as well as various consequences detrimental to the environment and social life. The essence of this method is to track and assess: what the actor (actor-based) or network of certain actors (actor-based network) does in a certain location and time the series of consequences (intended or unintended) that result from what the actors and/or networks do, in a time and space that can be different from the original time and space, as long as it is in accordance with the interest of the research and the available time. Therefore, the PC method does not have to be bound to a certain research place and time pre-determined in the research design. It rejects the assumption of ecological and


id: protoscience-20260505-0
file: data/en.wikipedia.org/wiki/Protoscience-0.md
category: reference
title: Protoscience
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Protoscience
date_saved: 2026-05-05T03:44:56.796921+00:00
instance: kb-cron
parent: protoscience-20260505-0
summary:
In the philosophy of science, protoscience (adj. protoscientific) is a research field that has the characteristics of an undeveloped science that may ultimately develop into an established science. Philosophers use protoscience to understand the history of science and distinguish protoscience from science and pseudoscience. The word "protoscience" is a hybrid Greek-Latin compound of the roots proto- + scientia, meaning a first or primeval rational knowledge. Examples of protoscience include alchemy, Wegener's original theory of continental drift and political economy (the predecessor to the modern economic sciences). == History == Protoscience as a research field with the characteristics of an undeveloped science appeared in the early 20th century. In 1910, Jones described the field of political economy as it began the transition to the modern field of economics: I confess to a personal predilection for some term such as proto-science, pre-science, or nas-science, to give expression to what I conceive to be the true state of affairs, which I take to be this, that economics and kindred subjects are not sciences, but are on the way to become sciences. Thomas Kuhn later provided a more precise description, protoscience as a field that generates testable conclusions, faces "incessant criticism and continually strive


id: protoscience-20260505-1
file: data/en.wikipedia.org/wiki/Protoscience-1.md
category: reference
title: Protoscience
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Protoscience
date_saved: 2026-05-05T03:44:56.796921+00:00
instance: kb-cron
parent: protoscience-20260505-0
summary:
=== Science field === A science field is a research field that satisfies 12 conditions: 1) all components of the science field invariably change over time from research in the field, especially logical/mathematical tools and specific background/presuppositions from other fields; 2) the research community has special training, "hold strong information links", initiates or continues the "tradition of inquiry"; 3) researchers have autonomy to pursue research and receive support from the host society; 4) the researchers worldview is the real world as contains "lawfully changing concrete" objects, an adequate view of the scientific method, a vision of organized science achieving truthfull descriptions and explanations, ethical principles for conducting research, and the free search for truthful, deep and systematic understanding; 5) up-to-date logical/mathematical tools precisely determine and process information; 6) the domain of research are real objects/entities; 7) specific background knowledge is up-to-date, confirmed data, hypotheses and theories from relevant neighboring fields; 8) the set of problems investigated are from the domain of inquiry or within the research field; 9) the accumulated knowledge includes worldview-compatible, up-to-date testworthy/testable theories, hypotheses and data, and special knowledge previously accumlated in the research field; 10) the aims are find and apply laws and theories in the


id: provenance-20260505-0
file: data/en.wikipedia.org/wiki/Provenance-0.md
category: reference
title: Provenance
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Provenance
date_saved: 2026-05-05T03:44:58.110534+00:00
instance: kb-cron
parent: provenance-20260505-0
summary:
Provenance (from French provenir 'to come from/forth') is the chronology of the ownership, custody or location of a historical object. The term was originally mostly used in relation to works of art, but is now used in similar senses in a wide range of fields, including archaeology, paleontology, archival science, economy, computing, and scientific enquiry in general. The primary purpose of tracing the provenance of an object or entity is normally to provide contextual and circumstantial evidence for its original production or discovery, by establishing, as far as practicable, its later history, especially the sequences of its formal ownership, custody and places of storage. The practice has a particular value in helping authenticate objects. Comparative techniques, expert opinions and the results of scientific tests may also be used to these ends, but establishing provenance is essentially a matter of documentation. The term dates to the 1780s in English. Provenance is conceptually comparable to the legal term chain of custody. For museums and the art trade, in addition to helping establish the authorship and authenticity of an object, provenance has become increasingly important in helping establish the moral and legal validity of a chain of custody, given the increasing amount of


id: provenance-20260505-1
file: data/en.wikipedia.org/wiki/Provenance-1.md
category: reference
title: Provenance
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Provenance
date_saved: 2026-05-05T03:44:58.110534+00:00
instance: kb-cron
parent: provenance-20260505-0
summary:
The objective of provenance research is to produce a complete list of owners (together, where possible, with the supporting documentary proof) from when the painting was commissioned or in the artist's studio through to the present time. In practice, there are likely to be gaps in the list and documents that are missing or lost. The documented provenance should also list when the painting has been part of an exhibition and a bibliography of when it has been discussed, or illustrated in print. Where the research is proceeding backwards, to discover the previous provenance of a painting whose current ownership and location are known, it is important to record the physical details of the painting – style, subject, signature, materials, dimensions, frame, etc. The titles of paintings and the attribution to a particular artist may change over time. The size of the work and its description can be used to identify earlier references to the painting. The back of a painting can contain significant provenance information. There may be exhibition marks, dealer stamps, gallery labels and other indications of previous ownership. There may be shipping labels. In the BBC TV programme Fake or Fortune? the provenance of the painting Bords


id: provenance-20260505-2
file: data/en.wikipedia.org/wiki/Provenance-2.md
category: reference
title: Provenance
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Provenance
date_saved: 2026-05-05T03:44:58.110534+00:00
instance: kb-cron
parent: provenance-20260505-0
summary:
== Books == In the case of books, the study of provenance refers to the study of the ownership of individual copies of books. It is usually extended to include the study of the circumstances in which individual copies of books have changed ownership, and of evidence left in books that shows how readers interacted with them. Provenance studies may shed light on the books themselves, providing evidence of the role particular titles have played in social, intellectual and literary history. Such studies may also add to our knowledge of particular owners of books. For instance, looking at the books owned by a writer may help to show which works influenced him or her. Many provenance studies are historically focused, and concentrated on books owned by writers, politicians and public figures. The recent ownership of books is studied, however, as is evidence of how ordinary or anonymous readers have interacted with books. Provenance can be studied both by examining the books themselves, for instance looking at inscriptions, marginalia, bookplates, book rhymes, and bindings, and by reference to external sources of information such as auction catalogues. == Pianos == Provenance for pianos is authenticated before a piano is inducted into a


id: provenance-20260505-3
file: data/en.wikipedia.org/wiki/Provenance-3.md
category: reference
title: Provenance
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Provenance
date_saved: 2026-05-05T03:44:58.110534+00:00
instance: kb-cron
parent: provenance-20260505-0
summary:
Archaeology and anthropology researchers use the word provenience (or alternatively find-spot) to refer to the exact location of discovery of an artifact, a bone or other remains, a soil sample, or a feature within an ancient site, whereas the word provenance covers an object's complete documented history. Ideally, in modern excavations, the provenience is recorded in three dimensions on a site grid with great precision, and may also be recorded on video to provide additional proof and context. In older work, often undertaken by amateurs, only the general site or approximate area may be known, especially when an artifact was found outside a professional excavation and its specific position not recorded. The term provenience appeared in the 1880s, about a century after provenance. Outside of academic contexts, it has been used as a synonymous variant spelling of provenance, especially in American English. Digital analytical methods have also been applied to the reconstruction of archaeological provenience. For example, a 2025 study used high-resolution three-dimensional surface comparison to identify ancient Egyptian artefacts produced from the same mould, contributing to the reassessment of their original context. Any given antiquity may have both a provenience, where it was found, and a provenance, where it


id: provenance-20260505-4
file: data/en.wikipedia.org/wiki/Provenance-4.md
category: reference
title: Provenance
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Provenance
date_saved: 2026-05-05T03:44:58.110534+00:00
instance: kb-cron
parent: provenance-20260505-0
summary:
=== Computer science === Within computer science, informatics uses the term "provenance" to mean the lineage of data, as per data provenance, with research in the last decade extending the conceptual model of causality and relation to include processes that act on data and agents that are responsible for those processes. See, for example, the proceedings of the International Provenance Annotation Workshop (IPAW) and Theory and Practice of Provenance (TaPP). Semantic web standards bodies, including the World Wide Web Consortium in 2014, have ratified a standard data model for provenance representation known as PROV which draws from many of the better-known provenance representation systems that preceded it, such as the Proof Markup Language and the Open Provenance Model. Interoperability is a design goal of most recent computer science provenance theories and models, for example the Open Provenance Model (OPM) 2008 generation workshop aimed at "establishing inter-operability of systems" through information exchange agreements. Data models and serialisation formats for delivering provenance information typically reuse existing metadata models where possible to enable this. Both the OPM Vocabulary and the PROV Ontology make extensive use of metadata models such as Dublin Core and Semantic Web technologies such as the Web Ontology Language (OWL).


id: provenance-20260505-5
file: data/en.wikipedia.org/wiki/Provenance-5.md
category: reference
title: Provenance
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Provenance
date_saved: 2026-05-05T03:44:58.110534+00:00
instance: kb-cron
parent: provenance-20260505-0
summary:
== Computers and law == The term provenance is used when ascertaining the source of goods such as computer hardware to assess if they are genuine or counterfeit. Chain of custody is an equivalent term used in law, especially for evidence in criminal or commercial cases. Software provenance encompasses the origin of software and its licensing terms. For example, when incorporating a free, open source or proprietary software component in an application, one may wish to understand its provenance to ensure that licensing requirements are fulfilled and that other software characteristics can be understood. Data provenance covers the provenance of computerized data. There are two main aspects of data provenance: ownership of the data and data usage. Ownership will tell the user who is responsible for the source of the data, ideally including information on the originator of the data. Data usage gives details regarding how the data has been used and modified and often includes information on how to cite the data source or sources. Data provenance is of particular concern with electronic data, as data sets are often modified and copied without proper citation or acknowledgement of the originating data set. Databases make it easy to select specific


id: pseudoskepticism-20260505-0
file: data/en.wikipedia.org/wiki/Pseudoskepticism-0.md
category: reference
title: Pseudoskepticism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Pseudoskepticism
date_saved: 2026-05-05T03:44:59.310494+00:00
instance: kb-cron
parent: pseudoskepticism-20260505-0
summary:
Pseudoskepticism (also spelled as pseudoscepticism) is a philosophical or scientific position that appears to be that of skepticism or scientific skepticism but in reality is a form of dogmatism. == Nineteenth and early twentieth centuries == An early use of the word was in self-denigration: on 31 August 1869, Swiss philosopher Henri-Frédéric Amiel wrote in his diary: My instinct is in harmony with the pessimism of Buddha and of Schopenhauer. It is a doubt which never leaves me, even in my moments of religious fervor. Nature is indeed for me a Maïa; and I look at her, as it were, with the eyes of an artist. My intelligence remains skeptical. What, then, do I believe in? I do not know. And what is it I hope for? It would be difficult to say. Folly! I believe in goodness, and I hope that good will prevail. Deep within this ironical and disappointed being of mine there is a child hidden — a frank, sad, simple creature, who believes in the ideal, in love, in holiness, and all heavenly superstitions. A whole millennium of idyls sleeps in my heart; I am a pseudo-skeptic, a pseudo-scoffer. It soon acquired its usual meaning where


id: pseudoskepticism-20260505-1
file: data/en.wikipedia.org/wiki/Pseudoskepticism-1.md
category: reference
title: Pseudoskepticism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Pseudoskepticism
date_saved: 2026-05-05T03:44:59.310494+00:00
instance: kb-cron
parent: pseudoskepticism-20260505-0
summary:
== Subsequent usage == Susan Blackmore, who lost her initial belief in parapsychology and in 1991 became a CSICOP fellow, later described what she termed the "worst kind of pseudoskepticism": There are some members of the skeptics' groups who clearly believe they know the right answer prior to inquiry. They appear not to be interested in weighing alternatives, investigating strange claims, or trying out psychic experiences or altered states for themselves (heaven forbid!), but only in promoting their own particular belief structure and cohesion. Hugo Anthony Meynell from the Department of Religious Studies at the University of Calgary, labels the "extreme position that all significant evidence supporting paranormal phenomena is a result of deception or lies" as pseudoskepticism. While Truzzi's characterization was aimed at the holders of majority views whom he considered were excessively impatient of minority opinions, the term has been used to describe advocates of minority intellectual positions who engage in pseudoskeptical behavior when they characterize themselves as "skeptics" despite cherry picking evidence that conforms to a preexisting belief. Thus according to Richard Cameron Wilson, some advocates of AIDS denial are indulging in "bogus scepticism" when they argue in this way. Wilson argues that the characteristic feature of


id: quietism-philosophy-20260505-0
file: data/en.wikipedia.org/wiki/Quietism_(philosophy)-0.md
category: reference
title: Quietism (philosophy)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Quietism_(philosophy)
date_saved: 2026-05-05T03:45:01.593491+00:00
instance: kb-cron
parent: quietism-philosophy-20260505-0
summary:
Quietism in philosophy sees the role of philosophy as broadly therapeutic or remedial. Quietist philosophers believe that philosophy has no positive thesis to contribute; rather, it defuses confusions in the linguistic and conceptual frameworks of other subjects, including non-quietist philosophy. For quietists, advancing knowledge or settling debates (particularly those between realists and non-realists) is not the job of philosophy, rather philosophy should liberate the mind by diagnosing confusing concepts. == Status within philosophy == Crispin Wright said that "Quietism is the view that significant metaphysical debate is impossible." It has been described as "the view or stance that entails avoidance of substantive philosophical theorizing and is usually associated with certain forms of skepticism, pragmatism, and minimalism about truth. More particularly, it is opposed to putting forth positive theses and developing constructive arguments." Quietism by its nature is not a philosophical school as understood in the sense of a systematic body of truths. The objective of quietism is to show that philosophical positions or theories cannot solve problems, settle debates or advance knowledge. It is often raised in discussion as an opposite position to both philosophical realism and anti-realism. Specifically, quietists deny that there is any substantial debate between the positions


id: replicate-biology-20260505-0
file: data/en.wikipedia.org/wiki/Replicate_(biology)-0.md
category: reference
title: Replicate (biology)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Replicate_(biology)
date_saved: 2026-05-05T03:45:07.232020+00:00
instance: kb-cron
parent: replicate-biology-20260505-0
summary:
In the biological sciences, replicates are an experimental units that are treated identically. Replicates are an essential component of experimental design because they provide an estimate of between sample error. Without replicates, scientists are unable to assess whether observed treatment effects are due to the experimental manipulation or due to random error. There are also analytical replicates which is when an exact copy of a sample is analyzed, such as a cell, organism or molecule, using exactly the same procedure. This is done in order to check for analytical error. In the absence of this type of error replicates should yield the same result. However, analytical replicates are not independent and cannot be used in tests of the hypothesis because they are still the same sample. == See also == Self-replication Fold change == References ==


id: replication-crisis-20260505-0
file: data/en.wikipedia.org/wiki/Replication_crisis-0.md
category: reference
title: Replication crisis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Replication_crisis
date_saved: 2026-05-05T03:45:08.741659+00:00
instance: kb-cron
parent: replication-crisis-20260505-0
summary:
The replication crisis, also known as the reproducibility or replicability crisis, refers to widespread failures to reproduce published scientific results. Because the reproducibility of empirical results is the cornerstone of the scientific method, such failures undermine the credibility of theories and challenge substantial parts of scientific knowledge. Psychology and medicine have been focal points for replication efforts, with researchers systematically reexamining classic studies to verify their reliability and, when failures emerge, to identify the underlying causes. Data strongly indicates that other natural and social sciences are also affected. The phrase "replication crisis" was coined in the early 2010s as part of a growing awareness of the problem. Considerations of causes and remedies have given rise to a new scientific discipline known as metascience, which uses methods of empirical research to examine empirical research practice. Researchers distinguish two forms of reproducibility. Reproducibility in a narrow sense refers to reexamining and validating the analysis of a set of data. The second category, replication, involves repeating an experiment or study with new, independent data to verify the original conclusions. == Background == === Replication === Replication has been called "the cornerstone of science". Environmental health scientist Stefan Schmidt began a 2009 review with


id: replication-crisis-20260505-1
file: data/en.wikipedia.org/wiki/Replication_crisis-1.md
category: reference
title: Replication crisis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Replication_crisis
date_saved: 2026-05-05T03:45:08.741659+00:00
instance: kb-cron
parent: replication-crisis-20260505-0
summary:
A null hypothesis test is a decision procedure which takes in some data, and outputs either H 0 {\displaystyle H_{0}} or H 1 {\displaystyle H_{1}} . If it outputs H 1 {\displaystyle H_{1}} , it is usually stated as "there is a statistically significant effect" or "the null hypothesis is rejected". Often, the statistical test is a (one-sided) threshold test, which is structured as follows: Gather data D {\displaystyle D} . Compute a test statistic t [ D ] {\displaystyle t[D]} for the data. Compare the test statistic against a critical value/threshold t threshold {\displaystyle t_{\text{threshold}}} . If t [ D ] > t threshold {\displaystyle t[D]>t_{\text{threshold}}} , then output H 1 {\displaystyle H_{1}} , else, output H 0 {\displaystyle H_{0}} . A two-sided threshold test is similar, but with two thresholds, such that it outputs H 1 {\displaystyle H_{1}} if either t [ D ] < t threshold − {\displaystyle t[D]<t_{\text{threshold}}^{-}} or t [ D ] > t threshold + {\displaystyle t[D]>t_{\text{threshold}}^{+}} There are 4 possible outcomes of a null hypothesis test: false negative, true negative, false positive, true positive. A false negative means that H 0 {\displaystyle H_{0}} is true, but the test outcome is H 1 {\displaystyle


id: replication-crisis-20260505-2
file: data/en.wikipedia.org/wiki/Replication_crisis-2.md
category: reference
title: Replication crisis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Replication_crisis
date_saved: 2026-05-05T03:45:08.741659+00:00
instance: kb-cron
parent: replication-crisis-20260505-0
summary:
Social priming failures: In the early 2010s, two direct replication attempts failed to reproduce results from social psychologist John Bargh's much-cited "elderly-walking" study (originally published in 1996). This experiment was part of a series of three studies that had been widely cited throughout the years, was regularly taught in university courses, and had inspired many conceptual replications. These replication failures triggered intense disagreement between replication researchers and the original authors. Notably, many of the conceptual replications of the original studies also failed to replicate in subsequent direct replications. Experiments on extrasensory perception: Social psychologist Daryl Bem conducted a series of experiments supposedly providing evidence for the controversial phenomenon of extrasensory perception. Bem faced substantial criticism of his study's methodology. Reanalysis of his data found no evidence for extrasensory perception. The experiment also failed to replicate in subsequent direct replications. According to Romero, what the community found particularly upsetting was that many of the flawed procedures and statistical tools used in Bem's studies were part of common research practice in psychology. Biomedical replication failures: Scientists from biotech companies Amgen and Bayer Healthcare reported alarmingly low replication rates (11–20%) of landmark findings in preclinical oncological research. P-hacking studies and questionable research practices:


id: replication-crisis-20260505-3
file: data/en.wikipedia.org/wiki/Replication_crisis-3.md
category: reference
title: Replication crisis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Replication_crisis
date_saved: 2026-05-05T03:45:08.741659+00:00
instance: kb-cron
parent: replication-crisis-20260505-0
summary:
=== In psychology === Several factors have combined to put psychology at the center of the conversation. Some areas of psychology once considered solid, such as social priming and ego depletion, have come under increased scrutiny due to failed replications. Much of the focus has been on social psychology, although other areas of psychology such as clinical psychology, developmental psychology, and educational research have also been implicated. In August 2015, the first open empirical study of reproducibility in psychology was published, called The Reproducibility Project: Psychology. Coordinated by psychologist Brian Nosek, researchers redid 100 studies in psychological science from three high-ranking psychology journals (Journal of Personality and Social Psychology, Journal of Experimental Psychology: Learning, Memory, and Cognition, and Psychological Science). Of 97 original studies with significant effects, 36% replicated successfully (p value below 0.05), with effect sizes averaging half the original magnitude. Among non-replications, 25% directly contradicted the original while 49% were inconclusive due to underpowered designs. The same paper examined the reproducibility rates and effect sizes by journal and discipline. Study replication rates were 23% for the Journal of Personality and Social Psychology, 48% for Journal of Experimental Psychology: Learning, Memory, and Cognition, and 38% for Psychological Science. Studies


id: replication-crisis-20260505-4
file: data/en.wikipedia.org/wiki/Replication_crisis-4.md
category: reference
title: Replication crisis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Replication_crisis
date_saved: 2026-05-05T03:45:08.741659+00:00
instance: kb-cron
parent: replication-crisis-20260505-0
summary:
=== Across fields === A 2016 survey by Nature on 1,576 researchers who took a brief online questionnaire on reproducibility found that more than 70% of researchers have tried and failed to reproduce another scientist's experiment results (including 87% of chemists, 77% of biologists, 69% of physicists and engineers, 67% of medical researchers, 64% of earth and environmental scientists, and 62% of all others), and more than half have failed to reproduce their own experiments. But fewer than 20% had been contacted by another researcher unable to reproduce their work. The survey found that fewer than 31% of researchers believe that failure to reproduce results means that the original result is probably wrong, although 52% agree that a significant replication crisis exists. Most researchers said they still trust the published literature. In 2010, Fanelli (2010) found that 91.5% of psychiatry/psychology studies confirmed the effects they were looking for, and concluded that the odds of this happening (a positive result) was around five times higher than in fields such as astronomy or geosciences. Fanelli argued that this is because researchers in "softer" sciences have fewer constraints to their conscious and unconscious biases. Early analysis of result-blind peer review, which is less


id: replication-crisis-20260505-5
file: data/en.wikipedia.org/wiki/Replication_crisis-5.md
category: reference
title: Replication crisis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Replication_crisis
date_saved: 2026-05-05T03:45:08.741659+00:00
instance: kb-cron
parent: replication-crisis-20260505-0
summary:
Academic "publish or perish" culture exacerbates publication bias. Intense pressure to publish in recognized journals, driven by hypercompetitive environments and bibliometric career evaluations, incentivizes researchers to prioritize publishable results over validity. According to Fanelli, this pushes scientists to employ a number of strategies aimed at making results "publishable". In the context of publication bias, this can mean adopting behaviors aimed at making results positive or statistically significant, often at the expense of their validity. According to Center for Open Science founder Brian Nosek and his colleagues, "publish or perish" culture created a situation whereby the goals and values of single scientists (e.g., publishability) are not aligned with the general goals of science (e.g., pursuing scientific truth). This is detrimental to the validity of published findings. Philosopher Brian D. Earp and psychologist Jim A. C. Everett argue that, although replication is in the best interests of academics and researchers as a group, features of academic psychological culture discourage replication by individual researchers. They argue that performing replications can be time-consuming, and take away resources from projects that reflect the researcher's original thinking. They are harder to publish, largely because they are unoriginal, and even when they can be published they are


id: replication-crisis-20260505-6
file: data/en.wikipedia.org/wiki/Replication_crisis-6.md
category: reference
title: Replication crisis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Replication_crisis
date_saved: 2026-05-05T03:45:08.741659+00:00
instance: kb-cron
parent: replication-crisis-20260505-0
summary:
==== In medicine ==== Irreproducible medical studies commonly share these characteristics: investigators not being blinded to the experimental versus the control arms; failure to repeat experiments; lack of positive and negative controls; failing to report all the data; inappropriate use of statistical tests; and use of reagents that were not appropriately validated. ==== In AI research ==== In machine learning research, a range of questionable practices have emerged due to intense pressure to achieve state-of-the-art benchmark results. Common evaluation questionable practices include "benchmark overfitting" by repeatedly tuning hyperparameters on held-out test sets, selectively reporting the best of multiple random seeds or experimental runs, and "metric hacking" through unreported post hoc decisions such as choice of tokenization or evaluation scripts that inflate scores. Researchers also frequently cherry-pick tasks or datasets where their proposed model outperforms baselines, omit negative or lower-performing results, and obscure compute budgets to present energy-intensive methods as efficient. Such practices can give a misleading impression of model progress and hinder fair comparisons across methods. Moreover, many machine learning studies suffer from irreproducible research practices that impede replication and auditing. Key issues include incomplete disclosure of training details—such as dataset preprocessing, exact model hyperparameters, random seeds, and hardware configurations—and


id: replication-crisis-20260505-7
file: data/en.wikipedia.org/wiki/Replication_crisis-7.md
category: reference
title: Replication crisis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Replication_crisis
date_saved: 2026-05-05T03:45:08.741659+00:00
instance: kb-cron
parent: replication-crisis-20260505-0
summary:
=== Statistical issues === ==== Low statistical power ==== Low statistical power hinders replication for three reasons: (1) low-power replications have reduced ability to detect true effects, (2) low-power original studies produce biased effect size estimates leading to undersized replications, and (3) low-power original studies yield results unlikely to reflect true effects. Mathematically, the probability of replicating a previous publication that rejected a null hypothesis H 0 {\displaystyle H_{0}} in favor of an alternative H 1 {\displaystyle H_{1}} is ( significance ) P r ( H 0 | publication ) + ( power ) P r ( H 1 | publication ) ≤ ( power ) {\displaystyle ({\text{significance}})Pr(H_{0}|{\text{publication}})+({\text{power}})Pr(H_{1}|{\text{publication}})\leq ({\text{power}})} assuming significance is less than power. Thus, low power implies low probability of replication, regardless of how the previous publication was designed, and regardless of which hypothesis is really true. Stanley and colleagues estimated the average statistical power of psychological literature by analyzing data from 200 meta-analyses. They found that on average, psychology studies have between 33.1% and 36.4% statistical power. These values are quite low compared to the 80% considered adequate statistical power for an experiment. Across the 200 meta-analyses, the median of studies with adequate statistical power was between


id: replication-crisis-20260505-8
file: data/en.wikipedia.org/wiki/Replication_crisis-8.md
category: reference
title: Replication crisis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Replication_crisis
date_saved: 2026-05-05T03:45:08.741659+00:00
instance: kb-cron
parent: replication-crisis-20260505-0
summary:
Various statistical methods can be applied to make the p-value appear smaller than it really is. This need not be malicious, as moderately flexible data analysis, routine in research, can increase the false-positive rate to above 60%. For example, if one collects some data, applies several different significance tests to it, and publishes only the one that happens to have a p-value less than 0.05, then the total p-value for "at least one significance test reaches p < 0.05" can be much larger than 0.05, because even if the null hypothesis were true, the probability that one out of many significance tests is extreme is not itself extreme. Typically, a statistical study has multiple steps, with several choices at each step, such as during data collection, outlier rejection, choice of test statistic, choice of one-tailed or two-tailed test, etc. These choices in the "garden of forking paths" multiply, creating many "researcher degrees of freedom". The effect is similar to the file-drawer problem, as the paths not taken are not published. Consider a simple illustration. Suppose the null hypothesis is true, and we have 20 possible significance tests to apply to the dataset. Also suppose the outcomes to the significance tests


id: replication-crisis-20260505-9
file: data/en.wikipedia.org/wiki/Replication_crisis-9.md
category: reference
title: Replication crisis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Replication_crisis
date_saved: 2026-05-05T03:45:08.741659+00:00
instance: kb-cron
parent: replication-crisis-20260505-0
summary:
==== Statistical heterogeneity ==== As also reported by Stanley and colleagues, a further reason studies might fail to replicate is high heterogeneity of the to-be-replicated effects. Heterogeneity (variance in research findings due to multiple true effect sizes rather than one) is measured by the I-squared statistic, which quantifies unexplained variation in effect sizes across studies. This variation can be due to differences in experimental methods, populations, cohorts, and statistical methods between replication studies. Heterogeneity poses a challenge to studies attempting to replicate previously found effect sizes. When heterogeneity is high, subsequent replications have a high probability of finding an effect size radically different than that of the original study. Importantly, significant levels of heterogeneity are also found in direct/exact replications of a study. Stanley and colleagues discuss this while reporting a study by quantitative behavioral scientist Richard Klein and colleagues, where the authors attempted to replicate 15 psychological effects across 36 different sites in Europe and the U.S. In the study, Klein and colleagues found significant amounts of heterogeneity in 8 out of 16 effects (I-squared = 23% to 91%). Importantly, while the replication sites intentionally differed on a variety of characteristics, such differences could account for very little heterogeneity


id: replication-crisis-20260505-10
file: data/en.wikipedia.org/wiki/Replication_crisis-10.md
category: reference
title: Replication crisis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Replication_crisis
date_saved: 2026-05-05T03:45:08.741659+00:00
instance: kb-cron
parent: replication-crisis-20260505-0
summary:
and the probability of replicating the statistical study is P r ( replication | find H 1 ) = P r ( find H 1 | H 1 ) P r ( H 1 | find H 1 ) + P r ( find H 1 | H 0 ) P r ( H 0 | find H 1 ) {\displaystyle Pr({\text{replication}}|{\text{ find }}H_{1})=Pr({\text{find }}H_{1}|H_{1})Pr(H_{1}|{\text{ find }}H_{1})+Pr({\text{find }}H_{1}|H_{0})Pr(H_{0}|{\text{ find }}H_{1})} which is also different from P r ( H 1 | find H 1 ) {\displaystyle Pr(H_{1}|{\text{ find }}H_{1})} . In particular, for a fixed level of significance, the probability of replication increases with power, and prior probability for H 1 {\displaystyle H_{1}} . If the prior probability for H 1 {\displaystyle H_{1}} is small, then one would require a high power for replication. For example, if the prior probability of the null hypothesis is P r ( H 0 ) = 0.9 {\displaystyle Pr(H_{0})=0.9} , and the study found a positive result, then the posterior probability for H 1 {\displaystyle H_{1}} is P r ( H 1 | find H 1 ) = 0.50 {\displaystyle Pr(H_{1}|{\text{ find }}H_{1})=0.50} , and the replication probability is P r ( replication |


id: replication-crisis-20260505-11
file: data/en.wikipedia.org/wiki/Replication_crisis-11.md
category: reference
title: Replication crisis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Replication_crisis
date_saved: 2026-05-05T03:45:08.741659+00:00
instance: kb-cron
parent: replication-crisis-20260505-0
summary:
== Consequences == When effects are wrongly stated as relevant in the literature, failure to detect this by replication will lead to the canonization of such false facts. A 2021 study found that papers in leading general interest, psychology and economics journals with findings that could not be replicated tend to be cited more over time than reproducible research papers, likely because these results are surprising or interesting. The trend is not affected by publication of failed reproductions, after which only 12% of papers that cite the original research will mention the failed replication. Further, experts are able to predict which studies will be replicable, leading the authors of the 2021 study, Marta Serra-Garcia and Uri Gneezy, to conclude that experts apply lower standards to interesting results when deciding whether to publish them. === Public awareness and perceptions === Concerns have been expressed within the scientific community that the general public may consider science less credible due to failed replications. Research supporting this concern is sparse, but a nationally representative survey in Germany showed that more than 75% of Germans have not heard of replication failures in science. The study also found that most Germans have positive perceptions of replication


id: replication-crisis-20260505-12
file: data/en.wikipedia.org/wiki/Replication_crisis-12.md
category: reference
title: Replication crisis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Replication_crisis
date_saved: 2026-05-05T03:45:08.741659+00:00
instance: kb-cron
parent: replication-crisis-20260505-0
summary:
In response to concerns in psychology about publication bias and data dredging, more than 140 psychology journals have adopted result-blind peer review. In this approach, studies are accepted not on the basis of their findings and after the studies are completed, but before they are conducted and on the basis of the methodological rigor of their experimental designs, and the theoretical justifications for their statistical analysis techniques before data collection or analysis is done. Early analysis of this procedure has estimated that 61% of result-blind studies have led to null results, in contrast to an estimated 5% to 20% in earlier research. In addition, large-scale collaborations between researchers working in multiple labs in different countries that regularly make their data openly available for different researchers to assess have become much more common in psychology. ==== Pre-registration of studies ==== Scientific publishing has begun using pre-registration reports to address the replication crisis. The registered report format requires authors to submit a description of the study methods and analyses prior to data collection. Once the method and analysis plan is vetted through peer-review, publication of the findings is provisionally guaranteed, based on whether the authors follow the proposed protocol. One goal of


id: replication-crisis-20260505-13
file: data/en.wikipedia.org/wiki/Replication_crisis-13.md
category: reference
title: Replication crisis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Replication_crisis
date_saved: 2026-05-05T03:45:08.741659+00:00
instance: kb-cron
parent: replication-crisis-20260505-0
summary:
==== Cross-validation ==== One common statistical problem is overfitting, that is, when researchers fit a regression model over a large number of variables but a small number of data points. For example, a typical fMRI study of emotion, personality, and social cognition has fewer than 100 subjects, but each subject has 10,000 voxels. The study would fit a sparse linear regression model that uses the voxels to predict a variable of interest, such as self-reported stress. But the study would then report on the p-value of the model on the same data it was fitted to. The standard approach in statistics, where data is split into a training and a validation set, is resisted because test subjects are expensive to acquire. One possible solution is cross-validation, which allows model validation while also allowing the whole dataset to be used for model-fitting. === Replication efforts === ==== Funding ==== In July 2016, the Netherlands Organisation for Scientific Research made €3 million available for replication studies. The funding is for replication based on reanalysis of existing data and replication by collecting and analysing new data. Funding is available in the areas of social sciences, health research and healthcare innovation. In 2013, the


id: replication-crisis-20260505-14
file: data/en.wikipedia.org/wiki/Replication_crisis-14.md
category: reference
title: Replication crisis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Replication_crisis
date_saved: 2026-05-05T03:45:08.741659+00:00
instance: kb-cron
parent: replication-crisis-20260505-0
summary:
The dominant scientific and statistical model of causation is the linear model. The linear model assumes that mental variables are stable properties which are independent of each other. In other words, these variables are not expected to influence each other. Instead, the model assumes that the variables will have an independent, linear effect on observable outcomes. Social scientists Sebastian Wallot and Damian Kelty-Stephen argue that the linear model is not always appropriate. An alternative is the complex system model which assumes that mental variables are interdependent. These variables are not assumed to be stable, rather they will interact and adapt to each specific context. They argue that the complex system model is often more appropriate in psychology, and that the use of the linear model when the complex system model is more appropriate will result in failed replications. ...psychology may be hoping for replications in the very measurements and under the very conditions where a growing body of psychological evidence explicitly discourages predicting replication. Failures to replicate may be plainly baked into the potentially incomplete, but broadly sweeping failure of human behavior to conform to the standard of independen[ce] ... ==== Replication should seek to revise theories ==== Replication is


id: reproducibility-20260505-0
file: data/en.wikipedia.org/wiki/Reproducibility-0.md
category: reference
title: Reproducibility
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Reproducibility
date_saved: 2026-05-05T03:45:09.848006+00:00
instance: kb-cron
parent: reproducibility-20260505-0
summary:
Reproducibility, closely related to replicability and repeatability, is a major principle underpinning the scientific method. For the findings of a study to be reproducible means that results obtained by an experiment or an observational study or in a statistical analysis of a data set should be achieved again with a high degree of reliability when the study is replicated. There are different kinds of replication but typically replication studies involve different researchers using the same methodology. Only after one or several such successful replications should a result be recognized as scientific knowledge. == History == The first to stress the importance of reproducibility in science was the Anglo-Irish chemist Robert Boyle, in England in the 17th century. Boyle's air pump was designed to generate and study vacuum, which at the time was a very controversial concept. Indeed, distinguished philosophers such as René Descartes and Thomas Hobbes denied the very possibility of vacuum existence. Historians of science Steven Shapin and Simon Schaffer, in their 1985 book Leviathan and the Air-Pump, describe the debate between Boyle and Hobbes, ostensibly over the nature of vacuum, as fundamentally an argument about how useful knowledge should be gained. Boyle, a pioneer of the experimental method,


id: reproducibility-20260505-1
file: data/en.wikipedia.org/wiki/Reproducibility-1.md
category: reference
title: Reproducibility
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Reproducibility
date_saved: 2026-05-05T03:45:09.848006+00:00
instance: kb-cron
parent: reproducibility-20260505-0
summary:
=== Reproducible research method === The term reproducible research refers to the idea that scientific results should be documented in such a way that their deduction is fully transparent. This requires a detailed description of the methods used to obtain the data and making the full dataset and the code to calculate the results easily accessible. This is the essential part of open science. To make any research project computationally reproducible, general practice involves all data and files being clearly separated, labelled, and documented. All operations should be fully documented and automated as much as practicable, avoiding manual intervention where feasible. The workflow should be designed as a sequence of smaller steps that are combined so that the intermediate outputs from one step directly feed as inputs into the next step. Version control should be used as it lets the history of the project be easily reviewed and allows for the documenting and tracking of changes in a transparent manner. A basic workflow for reproducible research involves data acquisition, data processing and data analysis. Data acquisition primarily consists of obtaining primary data from a primary source such as surveys, field observations, experimental research, or obtaining data from an existing source.


id: reproducibility-20260505-2
file: data/en.wikipedia.org/wiki/Reproducibility-2.md
category: reference
title: Reproducibility
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Reproducibility
date_saved: 2026-05-05T03:45:09.848006+00:00
instance: kb-cron
parent: reproducibility-20260505-0
summary:
== Noteworthy irreproducible results == Hideyo Noguchi became famous for correctly identifying the bacterial agent of syphilis, but also claimed that he could culture this agent in his laboratory. Nobody else has been able to produce this latter result. In March 1989, University of Utah chemists Stanley Pons and Martin Fleischmann reported the production of excess heat that could only be explained by a nuclear process ("cold fusion"). The report was astounding given the simplicity of the equipment: it was essentially an electrolysis cell containing heavy water and a palladium cathode which rapidly absorbed the deuterium produced during electrolysis. The news media reported on the experiments widely, and it was a front-page item on many newspapers around the world (see science by press conference). Over the next several months others tried to replicate the experiment, but were unsuccessful. Nikola Tesla claimed as early as 1899 to have used a high frequency current to light gas-filled lamps from over 25 miles (40 km) away without using wires. In 1904 he built Wardenclyffe Tower on Long Island to demonstrate means to send and receive power without connecting wires. The facility was never fully operational and was not completed due to economic problems,


id: reproducibility-project-20260505-0
file: data/en.wikipedia.org/wiki/Reproducibility_Project-0.md
category: reference
title: Reproducibility Project
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Reproducibility_Project
date_saved: 2026-05-05T03:45:10.989762+00:00
instance: kb-cron
parent: reproducibility-project-20260505-0
summary:
The Reproducibility Project is a series of crowdsourced collaborations aiming to reproduce published scientific studies, finding high rates of results which could not be replicated. It has resulted in two major initiatives focusing on the fields of psychology and cancer biology. The project has brought attention to the replication crisis, and has contributed to shifts in scientific culture and publishing practices to address it. The project was led by the Center for Open Science and its co-founder, Brian Nosek, who started the project in November 2011. == Results == Brian Nosek of University of Virginia and colleagues sought out to replicate 100 different studies, all published in 2008. The project pulled these studies from three different journals, Psychological Science, the Journal of Personality and Social Psychology, and the Journal of Experimental Psychology: Learning, Memory, and Cognition, published in 2008 to see if they could get the same results as the initial findings. 97 of the original studies had significant effects, but of those 97, only 36% of the replications yielded significant findings (p value below 0.05), and the effects were often smaller than those in the original papers. The authors emphasized that the findings reflect a problem that affects all


id: research-20260505-0
file: data/en.wikipedia.org/wiki/Research-0.md
category: reference
title: Research
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Research
date_saved: 2026-05-05T03:45:12.257936+00:00
instance: kb-cron
parent: research-20260505-0
summary:
Research is creative and systematic work undertaken to increase the stock of knowledge. It involves the collection, organization, and analysis of evidence to increase understanding of a topic, characterized by a particular attentiveness to controlling sources of bias and error. These activities are characterized by accounting and controlling for biases. A research project may be an expansion of past work in the field. To test the validity of instruments, procedures, or experiments, research may replicate elements of prior projects or the project as a whole. The primary purposes of basic research (as opposed to applied research) are documentation, discovery, interpretation, and the research and development (R&D) of methods and systems for the advancement of human knowledge. Approaches to research depend on epistemologies, which vary considerably both within and between humanities and sciences. There are several forms of research: scientific, humanities, artistic, economic, social, business, marketing, practitioner research, life, technological, etc. The scientific study of research practices is known as meta-research. A researcher is a person who conducts research. == Etymology == The word research is derived from the Middle French "recherche", which means "to go about seeking", the term itself being derived from the Old French term "recerchier", a compound


id: research-20260505-1
file: data/en.wikipedia.org/wiki/Research-1.md
category: reference
title: Research
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Research
date_saved: 2026-05-05T03:45:12.257936+00:00
instance: kb-cron
parent: research-20260505-0
summary:
=== Artistic research === Artistic research, also seen as 'practice-based research', can take form when creative works are considered both the research and the object of research itself. It is the debatable body of thought which offers an alternative to purely scientific methods in research in its search for knowledge and truth. The controversial trend of artistic teaching becoming more academics-oriented is leading to artistic research being accepted as the primary mode of enquiry in art as in the case of other disciplines. One of the characteristics of artistic research is that it must accept subjectivity as opposed to the classical scientific methods. As such, it is similar to the social sciences in using qualitative research and intersubjectivity as tools to apply measurement and critical analysis. Artistic research has been defined by the School of Dance and Circus (Dans och Cirkushögskolan, DOCH), Stockholm in the following manner – "Artistic research is to investigate and test with the purpose of gaining knowledge within and for our artistic disciplines. It is based on artistic practices, methods, and criticality. Through presented documentation, the insights gained shall be placed in a context." Artistic research aims to enhance knowledge and understanding with presentation of the


id: research-20260505-2
file: data/en.wikipedia.org/wiki/Research-2.md
category: reference
title: Research
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Research
date_saved: 2026-05-05T03:45:12.257936+00:00
instance: kb-cron
parent: research-20260505-0
summary:
Identification of research problem Literature review Specifying the purpose of research Determining specific research questions Specification of a conceptual framework, sometimes including a set of hypotheses Choice of a methodology (for data collection) Data collection Verifying data Analyzing and interpreting the data Reporting and evaluating research Communicating the research findings and, possibly, recommendations The steps generally represent the overall process; however, they should be viewed as an ever-changing iterative process rather than a fixed set of steps. Most research begins with a general statement of the problem, or rather, the purpose for engaging in the study. The literature review identifies flaws or holes in previous research which provides justification for the study. Often, a literature review is conducted in a given subject area before a research question is identified. A gap in the current literature, as identified by a researcher, then engenders a research question. The research question may be parallel to the hypothesis. The hypothesis is the supposition to be tested. The researcher(s) collects data to test the hypothesis. The researcher(s) then analyzes and interprets the data via a variety of statistical methods, engaging in what is known as empirical research. The results of the data analysis in rejecting


id: research-20260505-3
file: data/en.wikipedia.org/wiki/Research-3.md
category: reference
title: Research
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Research
date_saved: 2026-05-05T03:45:12.257936+00:00
instance: kb-cron
parent: research-20260505-0
summary:
Exploratory research, which helps to identify and define a problem or question. Constructive research, which tests theories and proposes solutions to a problem or question. Empirical research, which tests the feasibility of a solution using empirical evidence. There are two major types of empirical research design: qualitative research and quantitative research. Researchers choose qualitative or quantitative methods according to the nature of the research topic they want to investigate and the research questions they aim to answer: Qualitative research Qualitative research refers to much more subjective non-quantitative, use different methods of collecting data, analyzing data, interpreting data for meanings, definitions, characteristics, symbols metaphors of things. Qualitative research further classified into the following types: Ethnography: This research mainly focus on culture of group of people which includes share attributes, language, practices, structure, value, norms and material things, evaluate human lifestyle. Ethno: people, Grapho: to write, this disciple may include ethnic groups, ethno genesis, composition, resettlement and social welfare characteristics. Phenomenology: It is very powerful strategy for demonstrating methodology to health professions education as well as best suited for exploring challenging problems in health professions educations. In addition, PMP researcher Mandy Sha argued that a project management approach is necessary to control


id: research-20260505-4
file: data/en.wikipedia.org/wiki/Research-4.md
category: reference
title: Research
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Research
date_saved: 2026-05-05T03:45:12.257936+00:00
instance: kb-cron
parent: research-20260505-0
summary:
In many disciplines, Western methods of conducting research are predominant. Researchers are overwhelmingly taught Western methods of data collection and study. The increasing participation of indigenous peoples as researchers has brought increased attention to the scientific lacuna in culturally sensitive methods of data collection. Western methods of data collection may not be the most accurate or relevant for research on non-Western societies. For example, "Hua Oranga" was created as a criterion for psychological evaluation in Māori populations, and is based on dimensions of mental health important to the Māori people – "taha wairua (the spiritual dimension), taha hinengaro (the mental dimension), taha tinana (the physical dimension), and taha whanau (the family dimension)". Even though Western dominance seems to be prominent in research, some scholars, such as Simon Marginson, argue for "the need [for] a plural university world". Marginson argues that the East Asian Confucian model could take over the Western model. This could be due to changes in funding for research both in the East and the West. Focused on emphasizing educational achievement, East Asian cultures, mainly in China and South Korea, have encouraged the increase of funding for research expansion. In contrast, in the Western academic world, notably in


id: research-20260505-5
file: data/en.wikipedia.org/wiki/Research-5.md
category: reference
title: Research
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Research
date_saved: 2026-05-05T03:45:12.257936+00:00
instance: kb-cron
parent: research-20260505-0
summary:
Academic publishing is a system that is necessary for academic scholars to peer review the work and make it available for a wider audience. The system varies widely by field and is also always changing, if often slowly. Most academic work is published in journal article or book form. There is also a large body of research that exists in either a thesis or dissertation form. These forms of research can be found in databases explicitly for theses and dissertations. In publishing, STM publishing is an abbreviation for academic publications in science, technology, and medicine. Most established academic fields have their own scientific journals and other outlets for publication, though many academic journals are somewhat interdisciplinary, and publish work from several distinct fields or subfields. The kinds of publications that are accepted as contributions of knowledge or research vary greatly between fields, from the print to the electronic format. A study suggests that researchers should not give great consideration to findings that are not replicated frequently. It has also been suggested that all published studies should be subjected to some measure for assessing the validity or reliability of its procedures to prevent the publication of unproven findings. Business models are


id: research-ethics-20260505-0
file: data/en.wikipedia.org/wiki/Research_ethics-0.md
category: reference
title: Research ethics
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Research_ethics
date_saved: 2026-05-05T03:45:13.584991+00:00
instance: kb-cron
parent: research-ethics-20260505-0
summary:
Research ethics is a discipline within the study of applied ethics. Its scope ranges from general scientific integrity and misconduct to the treatment of human and animal subjects. The social responsibilities of scientists and researchers are not traditionally included and are less well defined. The discipline is most developed in medical research. Beyond the issues of falsification, fabrication, and plagiarism that arise in every scientific field, research design in human subject research and animal testing are the areas that raise ethical questions most often. The list of historic cases includes many large-scale violations and crimes against humanity such as Nazi human experimentation and the Tuskegee syphilis experiment which led to international codes of research ethics. No approach has been universally accepted, but typically cited codes are the 1947 Nuremberg Code, the 1964 Declaration of Helsinki, and the 1978 Belmont Report. Today, research ethics committees, such as those of the US, UK, and EU, govern and oversee the responsible conduct of research. One major goal being to reduce questionable research practices. Research in other fields such as social sciences, information technology, biotechnology, or engineering may generate ethical concerns. == History == The list of historic cases includes many large scale violations


id: retrodiction-20260505-0
file: data/en.wikipedia.org/wiki/Retrodiction-0.md
category: reference
title: Retrodiction
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Retrodiction
date_saved: 2026-05-05T03:45:16.005780+00:00
instance: kb-cron
parent: retrodiction-20260505-0
summary:
Retrodiction is the act of making a prediction about the past. It is also known as postdiction (but this should not be confused with the use of the term in criticisms of parapsychological research). == Activity == The activity of retrodiction (or postdiction) involves moving backwards in time, step-by-step, in as many stages as are considered necessary, from the present into the speculated past to establish the ultimate cause of a specific event (for instance, in the case of reverse engineering, forensics, etc.). Given that retrodiction is a process in which "past observations, events and data are used as evidence to infer the process(es) that produced them" and that diagnosis "involve[s] going from visible effects such as symptoms, signs and the like to their prior causes", the essential balance between prediction and retrodiction could be characterized as: retrodiction : diagnosis :: prediction : prognosis regardless of whether the prognosis is of the course of the disease in the absence of treatment, or of the application of a specific treatment regimen to a specific disorder in a particular patient: "We consider diagnostic inference to be based on causal thinking, although in doing diagnosis one has to mentally reverse the time order


id: role-of-chance-in-scientific-discoveries-20260505-0
file: data/en.wikipedia.org/wiki/Role_of_chance_in_scientific_discoveries-0.md
category: reference
title: Role of chance in scientific discoveries
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Role_of_chance_in_scientific_discoveries
date_saved: 2026-05-05T03:45:17.281687+00:00
instance: kb-cron
parent: role-of-chance-in-scientific-discoveries-20260505-0
summary:
The role of chance, or "luck", in science comprises all ways in which unexpected discoveries are made. Many domains, especially psychology, are concerned with the way science interacts with chance –particularly "serendipity" (accidents that, through sagacity, are transformed into opportunity). Psychologist Kevin Dunbar and colleagues estimate that between 30% and 50% of all scientific discoveries are accidental in some sense (see examples below). Scientists themselves in the 19th and 20th century acknowledged the role of fortunate luck or serendipity in discoveries. Psychologist Alan A. Baumeister says a scientist must be "sagacious" (attentive and clever) to benefit from an accident. Dunbar quotes Louis Pasteur's saying that "Chance favors only the prepared mind". The prepared mind, Dunbar suggests, is one trained for observational rigor. Dunbar adds that there is a great deal of writing about the role that serendipity ("happy accidents") plays in the scientific method. Research suggests that scientists are taught various heuristics and practices that allow their investigations to benefit, and not suffer, from accidents. First, careful control conditions allow scientists to properly identify something as "unexpected". Once a finding is recognized as legitimately unexpected and in need of explaining, researchers can attempt to explain it: They work across various


id: royal-commission-on-animal-magnetism-20260505-0
file: data/en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism-0.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 Royal Commission on Animal Magnetism involved two entirely separate and independent French Royal Commissions, each appointed by Louis XVI in 1784, that were conducted simultaneously by a committee composed of four physicians from the Paris Faculty of Medicine (Faculté de médecine de Paris) and five scientists from the Royal Academy of Sciences (Académie des sciences) (the "Franklin Commission", named for Benjamin Franklin), and a second committee composed of five physicians from the Royal Society of Medicine (Société Royale de Médecine) (the "Society Commission"). Each Commission took five months to complete its investigations. The "Franklin" Report was presented to the King on 11 August 1784 – and was immediately published and very widely circulated throughout France and neighbouring countries – and the "Society" Report was presented to the King five days later on 16 August 1784. The "Franklin Commission's" investigations are notable as a very early "classic" example of a systematic controlled trial, which not only applied "sham" and "genuine" procedures to patients with "sham" and "genuine" disorders, but, significantly, was the first to use the "blindfolding" of both the investigators and their subjects. The report of the ["Franklin"] Royal Commission of 1784 ... is a masterpiece of its genre,


id: royal-commission-on-animal-magnetism-20260505-1
file: data/en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism-1.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:
From the perspective of a given phenomenon, one can look down to the entities and activities composing it. One can look up to the higher-level mechanisms of which it is a component. One can look back to the mechanisms that come before it or by which it developed. One can look forward to what comes after it. [And, finally] one can look around to see the wider context with which it operates. === Tensions within the royal family === Prior to his arrival in Paris in 1777 – with a letter of recommendation from Chancellor von Kaunitz of the Habsburg monarchy to the Austrian Ambassador to France, the Comte de Mercy-Argenteau (who, in turn, introduced Mesmer to Jean-Baptiste Le Roy (1720–1800), the Director of the Academy of Sciences) – Mesmer was already known to Marie Antoinette. At the urging of her two closest friends, Marie-Paule Angélique d’Albert de Luynes (1744–1781), "the Duchesse de Chaulnes" and Marie Thérèse Louise de Savoie-Carignan (1749–1792), "the Princess of Lamballe", both of whom "had benefited from Mesmer's treatment", Marie Antoinette had been able to arrange for both Mesmer and d'Eslon to be officially "interviewed" by (an otherwise unidentified) representative of the King on 14 March


id: royal-commission-on-animal-magnetism-20260505-2
file: data/en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism-2.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:
=== Scientific issues === In a prevailing atmosphere of "[an overall] redefinition of frontiers in the legitimacy of knowledge" – and, in relation to Mesmer's claims, a redefinition "which did not necessarily match the public popularity that they attracted" (Zanetti, 2018), p. 59) – the issue of the existence (or not) of a substantial "magnetic fluid" and/or "animal magnetism" required resolution. === Medical issues === At a time when, in relation to "healers and healing", the conglomerate of "physicians, empirics, surgeons, apothecaries, folk healers, and religious personalities all vied with each other (as well as worked together) for medical legitimacy and patients" (Broomhall, 2004, p. 5), Mesmer was not only a "foreign national", but also one that had no affiliation of any kind with any known professional medical association within France (or elsewhere in Europe); and, as a consequence, his professional conduct, his medical practice, his medico-commercial enterprises, and his therapeutic endeavours were not regulated in any way. Moreover, the efficacy of Mesmer's interventions had never been objectively tested, neither the agency nor the (pre- and post-intervention) veracity of his supposed "cures" had ever been objectively verified, and, finally, in relation to the presenting conditions of those with (supposedly) 'real'


id: royal-commission-on-animal-magnetism-20260505-3
file: data/en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism-3.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 second of the two Royal Commissions, usually referred to as the "Society Commission", was appointed on 5 April 1784. It was composed of five eminent physicians from the Royal Society of Medicine – the physician and one of the first members of the Royal Society, Charles-Louis-François Andry (1741–1829), the physician Claude-Antoine Caille (b. 1743), the botanist Antoine Laurent de Jussieu (1748–1836), the physician, Collège de France professor, one of the original directors of the Royal Society, and committed advocate of the therapeutic applications of electricity, Pierre Jean Claude Mauduyt de La Varenne (1732–1792), and the physician and Professor of chemistry in the Collège de France, Pierre-Isaac Poissonnier (1720–1798) – and, as Pattie remarks (1994, p. 156), "the impression given by [their] report is that the commissioners were busy practitioners who wanted to devote no more time to the project than was necessary". Although the investigations of the "Society Commission" were less thorough and less detailed than those of the "Franklin Commission" they were essentially of the same nature, and it is a matter of fact that neither Commission examined Mesmer's practices – they only examined the practices of d'Eslon. == Franz Mesmer == Franz Anton Mesmer (1734–1815), born in


id: royal-commission-on-animal-magnetism-20260505-4
file: data/en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism-4.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:
=== A concept that must not be reified === It is clear from his Mémoire (1799) that Mesmer was very aware of the human propensity – in the normal, conventional use of language (la langue de convention) – to speak of "properties" or "qualities" (i.e., these "metaphysical abstractions", illusions de la méthaphysique), as if they were "substances": in Mesmer's words, "substantivise the properties", substantisia les propriétés (Mesmer 1799, pp. 15–17). – in other words, "reification", in the manner of Whitehead's "fallacy of misplaced concreteness". Mesmer was also well aware of the extent to which, through the "distortion" caused by these "substantive words" (mot substantif) – which inappropriately "personified" (personnifia) these metaphysical abstractions (p. 16) – one is induced to believe in the actual physical existence of the "substance" itself. Given these observations, Mesmer was most emphatic in his continuous warnings that his abstract "principles" should not be "substantivised". It is significant that Mesmer (1799) describes how, once he had formulated the abstract, overarching (metaphorical) construct/concept of "animal magnetism" as the therapeutic agent (a quarter of a century earlier) – and with his hope that this newly described "principle of action" (principe de action), when considered as an agent, "could become


id: royal-commission-on-animal-magnetism-20260505-5
file: data/en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism-5.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:
=== Post-Mesmer === In late 1782, and eighteen months before the Royal Commission, d'Eslon had (acrimoniously) parted ways with Mesmer; and, despite a brief reconciliation, the relationship was terminated in late 1783. On 28 December 1783, d'Eslon wrote a letter to the Journal de Paris, which not only described the difficulties he had experienced with Mesmer, but also announced that he was opening his own (entirely independent) clinic. Following his break with Mesmer, d'Eslon not only launched his own clinical operation on his break with Mesmer, d'Eslon took all of the patients he had brought to Mesmer with him – but also began teaching his own theories and practices (i.e., rather than those of Mesmer). According to d'Eslon's own account (d'Eslon, 1784b, pp. 25–26), Mesmer had taught 300 students, 160 of whom were medical men (médecins), and d'Eslon himself had taught 160 medical men (this group included 21 members of the Paris Faculty of Medicine). Given that many of those who had privately paid Mesmer for the details of "the secret" were greatly dissatisfied, and had "[justifiably] accused [Mesmer] of having enunciated a theory which was merely a collection of obscure principles" (Binet & Féré, 1888, p. 13), it seems


id: royal-commission-on-animal-magnetism-20260505-6
file: data/en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism-6.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 baquet] is a vat about six to seven feet, more or less, in diameter by eighteen inches in height. There is a double bottom in the interior of this vat, in which fragments of broken bottles, gravel, stones, and sticks of pounded sulfur and iron filings are placed. All of this is filled with water and covered up with a floor nailed into the vat. On the surface of the lid, six inches in from the rim, one makes various holes in order to allow the passage of iron rods which are arranged so that one end penetrates the bottom of the vat and the other is directed, by means of a curve, over the pit of the stomach of the patient or other affected parts of the body. Mesmer specifically stressed the primary importance of the patients' hand-holding as a factor in the "augmentation" of the force/quality of the power of the "animal magnetism". Moreover, and significantly, Mesmer (separately) acknowledged that, if it was ever to come to pass that he had a suitable "establishment" – i.e., one with sufficient space available for all the assembled patients to hold hands – he would "abolish the use of baquets"


id: royal-commission-on-animal-magnetism-20260505-7
file: data/en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism-7.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:
In his response to the Reports (d'Eslon, 1784b, pp. 21–22), d'Eslon complained that the Commissioners' emphasis on "convulsions" was not justified: among those who received group treatment during the commission's investigations (i.e., involving "50 to 60 individuals"), he wrote, there were never more than six or seven who displayed "convulsions" to any degree – and, further, of the more than 500 patients he had treated over the preceding three years, only 20 of those had manifested "convulsions" (and almost all of those had been suffering from "convulsions" before presenting for any treatment from d'Eslon). He also rejected the suggestions of any connection between the "convulsions" of epilepsy and those of the "crises", citing the cases of two of his patients, who were epileptic and "frequently had seizures at home", who never had a single "attack" during their treatment at his clinic (p. 23). Joseph Michel Antoine Servan, the one time Advocate-General to the Parlement of Grenoble, who reported (at Servan, 1784, p. 3) that, in "the Provinces" – where the various social classes were not kept apart around "the baquet", as they were in Paris – that, in relation to the concerns that the Commissioners expressed in relation to the


id: royal-commission-on-animal-magnetism-20260505-8
file: data/en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism-8.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 substantial existence of "animal magnetism" and "magnetic fluid" were investigated === Rather than being concerned with the applications, utility, and clinical efficacy of d'Eslon's "animal magnetism", the primary concern of each Commission was the significant, crucial, and exclusive question of whether or not d'Eslon's (supposed) "animal magnetic fluid" actually existed in some substantial physical way – for the simple reason that, as the two sets of Commissioners each noted in their independent reports, "Animal magnetism may well exist without being useful but it cannot be useful if it does not exist." === Mesmer's earlier refusal to have his "magnetic" interventions scrutinized === Already, in his earlier (18 September 1780) interaction with the Paris Faculty of Medicine, Mesmer had refused to have his therapeutic interventions on a set of entirely "new" patients directly scrutinized, claiming that his already-achieved "cures" were an objective matter of record. Mesmer justified his refusal as follows: Here is what I said to M. de Lassonne; however bizarre [it may seem] at first sight it is nevertheless entirely serious and very much applicable to the question. When a thief is convicted of theft he is hanged: when a murderer is convicted of murder he is


id: royal-commission-on-animal-magnetism-20260505-9
file: data/en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism-9.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:
it was [their] duty ... to confine themselves to arguments purely physical, that is, to the momentaneous [sic] effects of the fluid upon the animal frame, excluding from these effects all the illusions which might mix with them, and assuring themselves that they could proceed from no other cause than the animal magnetism." === Problems with objectively determining the precise therapeutic action of any supposed "efficacious remedy" === In support of its decision, the "Franklin Commission" produced a cogent, extended argument, consistent with the medical knowledge of the day, that is equally relevant to similar investigations in the present day: The majority of diseases have their seat in the interior part of our frame. The collective experience of a great number of centuries has made us acquainted with the symptoms, which indicate and discriminate them; the same experience has taught the method in which they are to be treated. What is the object of the efforts of the physician in this method? It is not to oppose and to subdue nature, it is to assist her in her operations. Nature, says the father of the medical science [viz., Hippocrates], cures the diseased; but sometimes she encounters obstacles, which constrain her


id: royal-commission-on-animal-magnetism-20260505-10
file: data/en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism-10.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:
They saw in the centre of a large apartment a circular box, made of oak, and about a foot or a foot and an [sic] half deep, which is called the bucket [baquet]; the lid of this box is pierced with a number of holes, in which are inserted branches of iron, elbowed and moveable. The patients are arranged in ranks about this [baquet], and each has his branch of iron, which by means of the elbow may be applied immediately to the part affected; a cord passed round their bodies connects them one with the other: sometimes a second means of communication is introduced, by the insertion of the thumb of each patient between the forefinger and thumb of the patient next him; the thumb thus inserted is pressed by the person holding it; the impression received by the left hand of the patient, communicates through his right, and thus passes through the whole circle.A piano forté is placed in one corner of the apartment, and different airs are played with various degrees of rapidity; vocal music is sometimes added to the instrumental.The persons who superintend the process, have each of them an iron rod in his hand, from


id: royal-commission-on-animal-magnetism-20260505-11
file: data/en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism-11.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:
==== Immediate publication and dissemination ==== The report was immediately published by the government printer; and at least 20,000 copies were rapidly and very widely circulated throughout France and neighbouring countries. Within four months (16 December 1784), the London publishing house of Joseph Johnson was announcing the publication of a complete English version, translated by William Godwin (i.e., Godwin, 1785), and, in between February and July 1785, four different "periodical abridgements of the Franklin report, each printed multiple times in the Atlantic coast publications" were published in the United States (Ogden, 2012, p. 167); and, in 1837, Godwin's complete translation was published, in Philadelphia, as part of a collected work. ==== Touch, imagination, and imitation ==== Clearly "recogniz[ing] that publicly endorsing the curative effects of a technique that had no demonstrable basis in the science of the late 18th century could lead to a proliferation of medical quackery" (McConkey & Perry, 2002, p. 328) and, based on their own "experiments" and "observations", the Commissioners concluded that "the true causes of the effects attributed to this new agent known by the name of animal magnetism, [and] to this fluid which is said to circulate in the body and to communicate itself


id: royal-commission-on-animal-magnetism-20260505-12
file: data/en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism-12.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:
On 4 September 1784, Bailly presented a third, brief (15 page) courtesy report to the Royal Academy of Sciences (Bailly, 1784b) on behalf of himself, Franklin, Le Roy, de Bory, and Lavoisier (i.e., those Commissioners who were also Academy members), which provided their Academy colleagues with a brief account of the commission's proceedings, the rationale behind its investigations, and the results. Noting that all of their investigations were jointly conducted with the four members of the Paris Faculty, and that all nine shared the same "interest in [discovering] the truth", they stressed that all the findings of their combined efforts were unanimous (p. 2). ==== The importance of "the Sciences" ==== Further (p. 4), given that the understanding of the Sciences – "which [collectively] are increased by [establishing] the truth" (qui s'accroissent par les vérités) – is increased by "the suppression of error": i.e., given that "error" is always "a bad leaven that ferments and, in the long run, corrupts the mass into which it has been introduced". By contrast, however, in those cases wherein the "error" has been generated by "The Empire of Science", and has spread to "the multitude" – not only to divide and agitate minds, but


id: royal-commission-on-animal-magnetism-20260505-13
file: data/en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism-13.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:
Part One (pp. 2–21), discussing the theories of the practices known as "Animal Magnetism". It commences with d'Eslon's definition of "animal magnetism"; namely that it is "the action which one man exercises on another, either through immediate contact or at a certain distance by the mere pointing of a finger or any kind of conduct", and that "this action", according to d'Eslon, "is the effect of a fluid that is distributed throughout the universe" Part Two (pp. 22–37), discussing the procedures and practices of "Animal Magnetism", as well as addressing the issues of their therapeutic efficacy (or not), and those of whether (or not) the procedures/practices should be admitted to conventional medical practice ("doivent-ils être admis en Médecine!": p. 22). The conclusions drawn (pp. 37–39) were, in brief, that they had found no evidence of d'Eslon's "magnetic fluid", that there were "no grounds for any belief in animal magnetism", that "the effects attributed to it are due to known causes", including not only the influence of "contact", "imagination", and/or "imitation", but also the influence of "the environment of the treatment room with its closed windows, fetid air, dim light, and the sight of other patients [and their responses to their


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


