# kb

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: 2026-05-05T03:42:43.135775+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: 2026-05-05T03:42:43.135775+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: 2026-05-05T03:42:43.135775+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: preregistration-science-20260505-0
file: data/en.wikipedia.org/wiki/Preregistration_(science)-0.md
category: reference
title: Preregistration (science)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Preregistration_(science)
date_saved: 2026-05-05T03:42:44.389462+00:00
instance: kb-cron
parent: preregistration-science-20260505-0
summary:
Preregistration is the practice of registering the hypotheses, methods, or analyses of a scientific study before it is conducted. Clinical trial registration is similar, although it may not require the registration of a study's analysis protocol. Finally, registered reports include the peer review and in principle acceptance of a study protocol prior to data collection. Preregistration has the goal to transparently evaluate the severity of hypothesis tests, and can have a number of secondary goals (which can also be achieved without preregistering ), including (a) facilitating and documenting research plans, (b) identifying and reducing questionable research practices and researcher biases, (c) distinguishing between confirmatory and exploratory analyses, and, in the case of Registered Reports, (d) facilitating results-blind peer review, and (e) reducing publication bias. Although the idea of preregistration is old, the practice of preregistering studies has gained prominence to mitigate certain issues that contribute to the replication crisis in scientific studies. Among others, these issues include publication bias and questionable research practices, such as p-hacking and HARKing. == Types == === Standard preregistration === In the standard preregistration format, researchers prepare a research protocol document prior to conducting their research. Ideally, this document indicates the research hypotheses, sampling procedure,


id: preregistration-science-20260505-1
file: data/en.wikipedia.org/wiki/Preregistration_(science)-1.md
category: reference
title: Preregistration (science)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Preregistration_(science)
date_saved: 2026-05-05T03:42:44.389462+00:00
instance: kb-cron
parent: preregistration-science-20260505-0
summary:
=== Creation and development === For many years, scientists and others have worried about reporting biases such that negative or null results from initiated clinical trials may be less likely to be published than positive results, thus skewing the literature and our understanding of how well interventions work. This worry has been international and written about for over 50 years. One of the proposals to address this potential bias was a comprehensive register of initiated clinical trials that would inform the public which trials had been started. Ethical issues were those that seemed to interest the public most, as trialists (including those with potential commercial gain) benefited from those who enrolled in trials, but were not required to "give back," telling the public what they had learned. Those who were particularly concerned by the double standard were systematic reviewers, those who summarize what is known from clinical trials. If the literature is skewed, then the results of a systematic review are also likely to be skewed, possibly favoring the test intervention when in fact the accumulated data do not show this, if all data were made public. ClinicalTrials.gov was originally developed largely as a result of breast cancer consumer lobbying,


id: preregistration-science-20260505-2
file: data/en.wikipedia.org/wiki/Preregistration_(science)-2.md
category: reference
title: Preregistration (science)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Preregistration_(science)
date_saved: 2026-05-05T03:42:44.389462+00:00
instance: kb-cron
parent: preregistration-science-20260505-0
summary:
== Benefits == Several articles have outlined the rationale for preregistration (e.g., Lakens, 2019; Nosek et al., 2018; Wagenmakers et al., 2012). The primary goal of preregistration is to improve the transparency of reported hypothesis tests, which allows readers to evaluate the extent to which decisions during the data analysis were pre-planned (maintaining statistical error control) or data-driven (increasing the Type 1 or Type 2 error rate). Meta-scientific research has revealed additional benefits. Researchers indicate preregistering a study leads to a more carefully thought through research hypothesis, experimental design, and statistical analysis. In addition, preregistration has been shown to encourage better learning of Open Science concepts and students felt that they understood their dissertation and it improved the clarity of the manuscript writing, promoted rigour and were more likely to avoid questionable research practices. In addition, it becomes a tool that supervisors can use to shape students to combat any questionable research practices. A 2024 study in the Journal of Political Economy: Microeconomics preregistration in economics journals found that preregistration reduced p-hacking and publication bias if the preregistration was accompanied by a preanalysis plan, but not if the preregistration did not specify the planned analyses. == Criticisms == === Analytical


id: preregistration-science-20260505-3
file: data/en.wikipedia.org/wiki/Preregistration_(science)-3.md
category: reference
title: Preregistration (science)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Preregistration_(science)
date_saved: 2026-05-05T03:42:44.389462+00:00
instance: kb-cron
parent: preregistration-science-20260505-0
summary:
=== Practical Implementation === There are also concerns about the practical implementation of preregistration. Many preregistered protocols leave plenty of room for p-hacking (Bakker et al., 2020; Heirene et al., 2021; Ikeda et al., 2019; Singh et al., 2021; Van den Akker et al., 2023), and researchers rarely follow the exact research methods and analyses that they preregister (Abrams et al., 2020; Claesen et al., 2019; Heirene et al., 2021; Clayson et al., 2025; see also Boghdadly et al., 2018; Singh et al., 2021; Sun et al., 2019). In terms of credibility, pre-registered studies are only of higher quality than non-pre-registered studies if the former has a power analysis and higher sample size than the latter but other than that they do not seem to prevent p-hacking and HARKing, as both the proportion of positive results and effect sizes are similar between preregistered and non-preregistered studies (Van den Akker et al., 2023). In terms of adherence, a study of 92 EEG/ERP studies showed that only 60% of studies adhered to their preregistrations or disclosed all deviations. Notably, registered reports had the higher adherence rates (92%) than unreviewed preregistrations (60%). In general, around three-quarters of preregistered studies included at least one


id: publication-bias-20260505-0
file: data/en.wikipedia.org/wiki/Publication_bias-0.md
category: reference
title: Publication bias
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Publication_bias
date_saved: 2026-05-05T03:42:45.581729+00:00
instance: kb-cron
parent: publication-bias-20260505-0
summary:
In published academic research, publication bias occurs when the outcome of an experiment or research study biases the decision to publish or otherwise distribute it. Publishing only results that show a significant finding disturbs the balance of findings in favor of positive results. The study of publication bias is an important topic in metascience. Despite similar quality of execution and design, papers with statistically significant results are three times more likely to be published than those with null results. This unduly motivates researchers to manipulate their practices to ensure statistically significant results, such as by data dredging. Many factors contribute to publication bias. For instance, once a scientific finding is well established, it may become newsworthy to publish reliable papers that fail to reject the null hypothesis. Most commonly, investigators simply decline to submit results, leading to non-response bias. Investigators may also assume they made a mistake, find that the null result fails to support a known finding, lose interest in the topic, or anticipate that others will be uninterested in the null results. Attempts to find unpublished studies often prove difficult or are unsatisfactory. In an effort to combat this problem, some journals require that authors preregister their methods


id: publication-bias-20260505-1
file: data/en.wikipedia.org/wiki/Publication_bias-1.md
category: reference
title: Publication bias
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Publication_bias
date_saved: 2026-05-05T03:42:45.581729+00:00
instance: kb-cron
parent: publication-bias-20260505-0
summary:
Meta-analyses and systematic reviews can account for publication bias by including evidence from unpublished studies and the grey literature. The presence of publication bias can also be explored by constructing a funnel plot in which the estimate of the reported effect size is plotted against a measure of precision or sample size. The premise is that the scatter of points should reflect a funnel shape, indicating that the reporting of effect sizes is not related to their statistical significance. However, when small studies are predominately in one direction (usually the direction of larger effect sizes), asymmetry will ensue and this may be indicative of publication bias. Because an inevitable degree of subjectivity exists in the interpretation of funnel plots, several tests have been proposed for detecting funnel plot asymmetry. These are often based on linear regression including the popular Eggers regression test, and may adopt a multiplicative or additive dispersion parameter to adjust for the presence of between-study heterogeneity. Some approaches may even attempt to compensate for the (potential) presence of publication bias, which is particularly useful to explore the potential impact on meta-analysis results. In ecology and environmental biology, a study found that publication bias impacted the effect size,


id: 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: 2026-05-05T03:42:46.886632+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: 2026-05-05T03:42:46.886632+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: 2026-05-05T03:42:46.886632+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: 2026-05-05T03:42:46.886632+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: 2026-05-05T03:42:46.886632+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: 2026-05-05T03:42:46.886632+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: 2026-05-05T03:42:46.886632+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: 2026-05-05T03:42:46.886632+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: 2026-05-05T03:42:46.886632+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: 2026-05-05T03:42:46.886632+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: 2026-05-05T03:42:46.886632+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: 2026-05-05T03:42:46.886632+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: 2026-05-05T03:42:46.886632+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: 2026-05-05T03:42:46.886632+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: 2026-05-05T03:42:46.886632+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: 2026-05-05T03:42:48.119894+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: 2026-05-05T03:42:48.119894+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: 2026-05-05T03:42:48.119894+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: 2026-05-05T03:42:53.024047+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: rescience-c-20260505-0
file: data/en.wikipedia.org/wiki/ReScience_C-0.md
category: reference
title: ReScience C
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/ReScience_C
date_saved: 2026-05-05T03:42:54.236538+00:00
instance: kb-cron
parent: rescience-c-20260505-0
summary:
ReScience C is a scientific journal established in 2015 by Nicolas Rougier and Konrad Hinsen with the aim of publishing researchers' attempts to replicate computations made by other authors, using independently written, free and open-source software (FOSS), with an open process of peer review. The journal states that requiring the replication software to be free and open-source ensures the reproducibility of the original research. == Creation == The journal was established in 2015 by Nicolas Rougier and Konrad Hinsen in the context of the replication crisis of the early 2010s, in which concern about difficulty in replicating (different data or details of method) or reproducing (same data, same method) peer-reviewed, published research papers was widely discussed. The journal's scope is computational research, with the motivation that journals rarely require the provision of source code, and when source code is provided, it is rarely checked against the results claimed in the research article. == Policies and methods == The scope of the journal is mainly focussed on researchers' attempts to replicate computations made by other authors, using independently written, free and open-source software (FOSS). Articles are submitted using the "issues" feature of a git repository run by GitHub, together with other


id: research-question-20260505-0
file: data/en.wikipedia.org/wiki/Research_question-0.md
category: reference
title: Research question
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Research_question
date_saved: 2026-05-05T03:42:55.419390+00:00
instance: kb-cron
parent: research-question-20260505-0
summary:
A research question is "a question that a research project sets out to answer". Choosing a research question is an essential element of both quantitative and qualitative research. Investigation will require data collection and analysis, and the methodology for this will vary widely. Good research questions seek to improve knowledge on an important topic, and are usually narrow and specific. To form a research question, one must determine what type of study will be conducted such as a qualitative, quantitative, or mixed study. Additional factors, such as project funding, may not only affect the research question itself but also when and how it is formed during the research process. Literature suggests several variations on criteria selection for constructing a research question, such as the FINER or PICOT methods. == Definition == The answer to a research question will help address a research problem or question. Specifying a research question, "the central issue to be resolved by a formal dissertation, thesis, or research project," is typically one of the first steps an investigator takes when undertaking research. Considerations, such as project funding or methodological approaches may influence the research process, including when and how the research question is developed. Clearly and


id: research-question-20260505-1
file: data/en.wikipedia.org/wiki/Research_question-1.md
category: reference
title: Research question
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Research_question
date_saved: 2026-05-05T03:42:55.419390+00:00
instance: kb-cron
parent: research-question-20260505-0
summary:
In terms of priorities and related concepts, the proposed strategy of differential technological development suggests research to focus primarily on questions and tools that are thought to increase safety and mitigate issues rather than risky technologies which are instead best to delay. Concerning control strategies for gene drives, researchers have however cautioned that such may lead to a counterproductive false sense of security. Not all technological progress may be beneficial in general or in contemporary contexts (environments or systems) and various research may for example result in engineered pandemics. Many studies "ask uninteresting research questions, [and] make only marginal contributions". One study suggests that while research on climate change "is valuable, it does not tackle head-on the most urgent question: how to change society to mitigate climate change right now". In the ethical framework of effective altruism, research questions with the greatest potential benefits from investments (not necessarily of financial nature) are identified to maximize research benefits. 80,000 Hours has compiled a small list of "Research questions that could have a big social impact, organised by discipline". In public health research, "it is vital that research questions posed are important and that funded research meets a research need or a


id: research-question-20260505-2
file: data/en.wikipedia.org/wiki/Research_question-2.md
category: reference
title: Research question
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Research_question
date_saved: 2026-05-05T03:42:55.419390+00:00
instance: kb-cron
parent: research-question-20260505-0
summary:
Does _________ (name the theory) explain the relationship between _________ (independent variable) and _________ (dependent variable), controlling for the effects of _________ (control variable)? Alternatively, a script for a quantitative null hypothesis might be as follows: There is no significant difference between _________ (the control and experimental groups on the independent variable) on _________ (dependent variable). Quantitative studies also fall into two categories: Correlational studies: A correlational study is non-experimental, requiring the writer to research relationships without manipulating or randomly selecting the subjects of the research. The research question for a correlational study may look like this: What is the relationship between long-distance commuters and eating disorders? Experimental studies: An experimental study is experimental in that it requires the writer to manipulate and randomly select the subjects of the research. The research question for an experimental study may look like this: Does the consumption of fast food lead to eating disorders? === Mixed study === A mixed study integrates both qualitative and quantitative studies, so the writer's research must be directed at determining the why or how and the what, where, or when of the research topic. Therefore, the writer will need to craft a research question for each study


id: science-exchange-company-20260505-0
file: data/en.wikipedia.org/wiki/Science_Exchange_(company)-0.md
category: reference
title: Science Exchange (company)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Science_Exchange_(company)
date_saved: 2026-05-05T03:42:56.578766+00:00
instance: kb-cron
parent: science-exchange-company-20260505-0
summary:
Science Exchange is a cloud-based software company offering an R&D marketplace to buy and sell scientific services. The marketplace gives life sciences companies access to the outsourced research they need and the platform fully automates R&D outsourcing from source to pay. Commercial contract research organizations (CROs) and academic core facilities can sell their products and services directly through the marketplace. Science Exchange's enterprise clients include top pharma and emerging biotechnology companies, including Merck, Amgen, Gilead Sciences, Astellas Pharma, AbbVie, and Regeneron Pharmaceuticals. Science Exchange was founded in 2011 by Elizabeth Iorns, Ryan Abbott, and Dan Knox, taking part in the startup accelerator program Y Combinator in the summer of 2011. == History == In 2011, as an assistant professor at the University of Miami Miller School of Medicine, Iorns came up with the idea for Science Exchange after needing to conduct immunology experiments, but having difficulty finding potential collaborators or providers to work with. Iorns formed Science Exchange with Knox and Abbott, and the company applied for a place in the Y Combinator startup accelerator program. The company was accepted into the Summer 2011 batch of Y Combinator and launched the first version of its website in August 2011. In


id: science-of-science-policy-20260505-0
file: data/en.wikipedia.org/wiki/Science_of_science_policy-0.md
category: reference
title: Science of science policy
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Science_of_science_policy
date_saved: 2026-05-05T03:42:57.727484+00:00
instance: kb-cron
parent: science-of-science-policy-20260505-0
summary:
Science of science policy (SoSP) is an emerging interdisciplinary research area that seeks to develop theoretical and empirical models of the scientific enterprise. This scientific basis can be used to help government, and society in general, make better R&D management decisions by establishing a scientifically rigorous, quantitative basis from which policy makers and researchers may assess the impacts of the nation's scientific and engineering enterprise, improve their understanding of its dynamics, and assess the likely outcomes. Examples of research in the science of science policy include models to understand the production of science, qualitative, quantitative and computational methods to estimate the impact of science, and processes for choosing from alternative science portfolios. == Federal effort == The federal government of the United States has long been a supporter of SoSP. In 2006, in response to Office of Science and Technology Policy Director John H. Marburger's challenge for a new "science of science policy", the National Science and Technology Council's Subcommittee on Social, Behavioral and Economic Sciences (SBE) established an Interagency Task Group on Science of Science Policy (ITG) to serve as part of the internal deliberative process of the Subcommittee. In 2008, the SoSP ITG developed and published "The Science


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: 2026-05-05T03:42:58.968342+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: 2026-05-05T03:42:58.968342+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: 2026-05-05T03:42:58.968342+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: 2026-05-05T03:42:58.968342+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: 2026-05-05T03:42:58.968342+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: 2026-05-05T03:42:58.968342+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: 2026-05-05T03:42:58.968342+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: scientometrics-20260505-0
file: data/en.wikipedia.org/wiki/Scientometrics-0.md
category: reference
title: Scientometrics
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientometrics
date_saved: 2026-05-05T03:43:00.220153+00:00
instance: kb-cron
parent: scientometrics-20260505-0
summary:
Scientometrics is a subfield of informetrics that studies science through mathematical and quantitative methods, analyzing both scientific output and the dynamics of research. Major research issues in scientometrics include the measurement of the impact of research papers and academic journals, the understanding of scientific citations, and the use of such measurements in policy and management contexts. In practice, there is a substantial overlap between scientometrics and other scientific fields, including information systems, information science, the science of science policy, the sociology of science, and metascience. Critics have argued that overreliance on scientometrics has created a system of perverse incentives, producing a publish or perish environment that leads to low-quality research. == Historical development == Modern scientometrics is mostly based on the work of Derek J. de Solla Price and Eugene Garfield. The latter created the Science Citation Index and founded the Institute for Scientific Information which is heavily used for scientometric analysis. A dedicated academic journal, Scientometrics, was established in 1978. The industrialization of science increased the number of publications and research outcomes and the rise of computers allowed effective analysis of this data. While the sociology of science focused on the behavior of scientists, scientometrics focused on the analysis


id: scientometrics-20260505-1
file: data/en.wikipedia.org/wiki/Scientometrics-1.md
category: reference
title: Scientometrics
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientometrics
date_saved: 2026-05-05T03:43:00.220153+00:00
instance: kb-cron
parent: scientometrics-20260505-0
summary:
An acknowledgment index (British acknowledgement index) is a method for indexing and analyzing acknowledgments in the scientific literature and, thus, quantifies the impact of acknowledgments. Typically, a scholarly article has a section in which the authors acknowledge entities such as funding, technical staff, colleagues, etc. that have contributed materials or knowledge or have influenced or inspired their work. Like a citation index, it measures influences on scientific work, but in a different sense; it measures institutional and economic influences as well as informal influences of individual people, ideas, and artifacts. Unlike the impact factor, it does not produce a single overall metric, but analyzes the components separately. However, the total number of acknowledgments to an acknowledged entity can be measured and so can the number of citations to the papers in which the acknowledgment appears. The ratio of this total number of citations to the total number of papers in which the acknowledge entity appears can be construed as the impact of that acknowledged entity. == Altmetrics == In scholarly and scientific publishing, altmetrics are nontraditional bibliometrics proposed as an alternative or complement to more traditional citation impact metrics, such as impact factor and h-index. The term altmetrics was proposed


id: statcheck-20260505-0
file: data/en.wikipedia.org/wiki/Statcheck-0.md
category: reference
title: Statcheck
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Statcheck
date_saved: 2026-05-05T03:43:01.416303+00:00
instance: kb-cron
parent: statcheck-20260505-0
summary:
Statcheck is an R package designed to detect statistical errors in peer-reviewed psychology articles by searching papers for statistical results, redoing the calculations described in each paper, and comparing the two values to see if they match. It takes advantage of the fact that psychological research papers tend to report their results in accordance with the guidelines published by the American Psychological Association (APA). This leads to several disadvantages: it can only detect results reported completely and in exact accordance with the APA's guidelines, and it cannot detect statistics that are only included in tables in the paper. Another limitation is that Statcheck cannot deal with statistical corrections to test statistics, like Greenhouse–Geisser or Bonferroni corrections, which actually make tests more conservative. Some journals have begun piloting Statcheck as part of their peer review process. Statcheck is free software published under the GNU GPL v3. == Validity == In 2017, Statcheck's developers published a preprint paper concluding that the program accurately identified statistical errors over 95% of the time. This validity study comprised more than 1,000 hand-checked tests among which 5.00% turned out to be inconsistent. The study found that Statcheck recognized 60% of all statistical tests. A reanalysis of


id: why-most-published-research-findings-are-false-20260505-0
file: data/en.wikipedia.org/wiki/Why_Most_Published_Research_Findings_Are_False-0.md
category: reference
title: Why Most Published Research Findings Are False
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Why_Most_Published_Research_Findings_Are_False
date_saved: 2026-05-05T03:43:02.627925+00:00
instance: kb-cron
parent: why-most-published-research-findings-are-false-20260505-0
summary:
"Why Most Published Research Findings Are False" is a 2005 essay written by John Ioannidis, a professor at the Stanford School of Medicine, and published in PLOS Medicine. It is considered foundational to the field of metascience. In the paper, Ioannidis argued that a large number, if not the majority, of published medical research papers contain results that cannot be replicated. In simple terms, the essay states that scientists use hypothesis testing to determine whether scientific discoveries are significant. Statistical significance is formalized in terms of probability, with its p-value measure being reported in the scientific literature as a screening mechanism. Ioannidis posited assumptions about the way people perform and report these tests; then he constructed a statistical model which indicates that most published findings are likely false positive results. While the general arguments in the paper recommending reforms in scientific research methodology were well-received, Ionnidis received criticism for the validity of his model and his claim that the majority of scientific findings are false. Responses to the paper suggest lower false positive and false negative rates than what Ioannidis puts forth. == Argument == Suppose that in a given scientific field there is a known baseline probability that a


id: why-most-published-research-findings-are-false-20260505-1
file: data/en.wikipedia.org/wiki/Why_Most_Published_Research_Findings_Are_False-1.md
category: reference
title: Why Most Published Research Findings Are False
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Why_Most_Published_Research_Findings_Are_False
date_saved: 2026-05-05T03:43:02.627925+00:00
instance: kb-cron
parent: why-most-published-research-findings-are-false-20260505-0
summary:
== Reception == Despite skepticism about extreme statements made in the paper, Ioannidis's broader argument and warnings have been accepted by a large number of researchers. The growth of metascience and the recognition of a scientific replication crisis have bolstered the paper's credibility, and led to calls for methodological reforms in scientific research. In commentaries and technical responses, statisticians Goodman and Greenland identified several weaknesses in Ioannidis' model. Ioannidis's use of dramatic and exaggerated language that he "proved" that most research findings' claims are false and that "most research findings are false for most research designs and for most fields" [italics added] was rejected, and yet they agreed with his paper's conclusions and recommendations. Biostatisticians Jager and Leek criticized the model as being based on justifiable but arbitrary assumptions rather than empirical data, and did an investigation of their own which calculated that the false positive rate in biomedical studies was estimated to be around 14%, not over 50% as Ioannidis asserted. Their paper was published in a 2014 special edition of the journal Biostatistics along with extended, supporting critiques from other statisticians. Leek summarized the key points of agreement as: when talking about the science-wise false discovery rate one


id: yuasa-phenomenon-20260505-0
file: data/en.wikipedia.org/wiki/Yuasa_Phenomenon-0.md
category: reference
title: Yuasa Phenomenon
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Yuasa_Phenomenon
date_saved: 2026-05-05T03:43:03.834238+00:00
instance: kb-cron
parent: yuasa-phenomenon-20260505-0
summary:
The Yuasa Phenomenon, named after Japanese physicist and science historian Mitsutomo Yuasa (sometimes referred to as Mintomo Yuasa), suggests that, in the modern era, the world center of scientific activity (defined as producing more than 25% of the world's scientific achievements) moves from one country to another about every 80–100 years. == Analysis == Analyzed data indicate that the "modern world science centre has shifted from Italy (1504–1610) to the United Kingdom (1660–1750), to France (1760– 1840), to Germany (1875–1920), and to the United States (1920 to the present)." This phenomenon and its study methodology are an emerging scientometrics study area. Indicators point to China's rise as a world center of scientific activity. This phenomenon is also described by other names, including "the Bernal—Yuasa phenomenon". Shigeo Minowa links Yuasa's finding to Joseph Ben-David's movements of Centers of Learning. Ben-David's Centers of Learning migration observations are discussed in various works. == See also == Historic recurrence History of science == References ==


id: scientific-method-20260505-0
file: data/en.wikipedia.org/wiki/Scientific_method-0.md
category: reference
title: Scientific method
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_method
date_saved: 2026-05-05T03:43:09.265477+00:00
instance: kb-cron
parent: scientific-method-20260505-0
summary:
The scientific method is an empirical method for acquiring knowledge through careful observation, rigorous skepticism, hypothesis testing, and experimental validation. Developed from ancient and medieval practices, it acknowledges that cognitive assumptions can distort the interpretation of the observation. The scientific method has characterized science since at least the 17th century. Scientific inquiry includes creating a testable hypothesis through inductive reasoning, testing it through experiments and statistical analysis, and adjusting or discarding the hypothesis based on the results. Although procedures vary across fields, the underlying process is often similar. In more detail: the scientific method involves making conjectures (hypothetical explanations), predicting the logical consequences of hypothesis, then carrying out experiments or empirical observations based on those predictions. A hypothesis is a conjecture based on knowledge obtained while seeking answers to the question. Hypotheses can be very specific or broad but must be falsifiable, implying that it is possible to identify a possible outcome of an experiment or observation that conflicts with predictions deduced from the hypothesis; otherwise, the hypothesis cannot be meaningfully tested. While the scientific method is often presented as a fixed sequence of steps, it actually represents a set of general principles. Not all steps take place in every


id: scientific-method-20260505-1
file: data/en.wikipedia.org/wiki/Scientific_method-1.md
category: reference
title: Scientific method
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_method
date_saved: 2026-05-05T03:43:09.265477+00:00
instance: kb-cron
parent: scientific-method-20260505-0
summary:
The scientific method is the process by which science is carried out. As in other areas of inquiry, science (through the scientific method) can build on previous knowledge, and unify understanding of its studied topics over time. Historically, the development of the scientific method was critical to the Scientific Revolution. The overall process involves making conjectures (hypotheses), predicting their logical consequences, then carrying out experiments based on those predictions to determine whether the original conjecture was correct. However, there are difficulties in a formulaic statement of method. The scientific method represents general principles rather than a fixed sequence, not all steps occur in every inquiry, nor always in the same order. It requires intelligence, imagination, and creativity rather than rigid adherence to procedure. ==== Factors of scientific inquiry ==== There are different ways of outlining the basic method used for scientific inquiry. The scientific community and philosophers of science generally agree on the following classification of method components. These methodological elements and organization of procedures tend to be more characteristic of experimental sciences than social sciences. Nonetheless, the cycle of formulating hypotheses, testing and analyzing the results, and formulating new hypotheses, will resemble the cycle described below.The scientific method is


id: scientific-method-20260505-2
file: data/en.wikipedia.org/wiki/Scientific_method-2.md
category: reference
title: Scientific method
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_method
date_saved: 2026-05-05T03:43:09.265477+00:00
instance: kb-cron
parent: scientific-method-20260505-0
summary:
I am not accustomed to saying anything with certainty after only one or two observations. ==== Definition ==== The scientific definition of a term sometimes differs substantially from its natural language usage. For example, mass and weight overlap in meaning in common discourse, but have distinct meanings in mechanics. Scientific quantities are often characterized by their units of measure which can later be described in terms of conventional physical units when communicating the work. New theories are sometimes developed after realizing certain terms have not previously been sufficiently clearly defined. For example, Albert Einstein's first paper on relativity begins by defining simultaneity and the means for determining length. These ideas were skipped over by Isaac Newton with, "I do not define time, space, place and motion, as being well known to all." Einstein's paper then demonstrates that they (viz., absolute time and length independent of motion) were approximations. Francis Crick cautions us that when characterizing a subject, however, it can be premature to define something when it remains ill-understood. In Crick's study of consciousness, he actually found it easier to study awareness in the visual system, rather than to study free will, for example. His cautionary example was the gene;


id: scientific-method-20260505-3
file: data/en.wikipedia.org/wiki/Scientific_method-3.md
category: reference
title: Scientific method
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_method
date_saved: 2026-05-05T03:43:09.265477+00:00
instance: kb-cron
parent: scientific-method-20260505-0
summary:
Watson and Crick showed an initial (and incorrect) proposal for the structure of DNA to a team from King's College London – Rosalind Franklin, Maurice Wilkins, and Raymond Gosling. Franklin immediately spotted the flaws which concerned the water content. Later Watson saw Franklin's photo 51, a detailed X-ray diffraction image, which showed an X-shape and was able to confirm the structure was helical. Once predictions are made, they can be sought by experiments. If the test results contradict the predictions, the hypotheses which entailed them are called into question and become less tenable. Sometimes the experiments are conducted incorrectly or are not very well designed when compared to a crucial experiment. If the experimental results confirm the predictions, then the hypotheses are considered more likely to be correct, but might still be wrong and continue to be subject to further testing. The experimental control is a technique for dealing with observational error. This technique uses the contrast between multiple samples, or observations, or populations, under differing conditions, to see what varies or what remains the same. We vary the conditions for the acts of measurement, to help isolate what has changed. Mill's canons can then help us figure out what


id: scientific-method-20260505-4
file: data/en.wikipedia.org/wiki/Scientific_method-4.md
category: reference
title: Scientific method
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_method
date_saved: 2026-05-05T03:43:09.265477+00:00
instance: kb-cron
parent: scientific-method-20260505-0
summary:
Science is a social enterprise, and scientific work tends to be accepted by the scientific community when it has been confirmed. Crucially, experimental and theoretical results must be reproduced by others within the scientific community. Researchers have given their lives for this vision; Georg Wilhelm Richmann was killed by ball lightning (1753) when attempting to replicate the 1752 kite-flying experiment of Benjamin Franklin. If an experiment cannot be repeated to produce the same results, this implies that the original results might have been in error. As a result, it is common for a single experiment to be performed multiple times, especially when there are uncontrolled variables or other indications of experimental error. For significant or surprising results, other scientists may also attempt to replicate the results for themselves, especially if those results would be important to their own work. Replication has become a contentious issue in social and biomedical science where treatments are administered to groups of individuals. Typically an experimental group gets the treatment, such as a drug, and the control group gets a placebo. John Ioannidis in 2005 pointed out that the method being used has led to many findings that cannot be replicated. Peer review—anonymous expert evaluation


id: scientific-method-20260505-5
file: data/en.wikipedia.org/wiki/Scientific_method-5.md
category: reference
title: Scientific method
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_method
date_saved: 2026-05-05T03:43:09.265477+00:00
instance: kb-cron
parent: scientific-method-20260505-0
summary:
=== Empiricism, rationalism, and more pragmatic views === Scientific endeavour can be characterised as the pursuit of truths about the natural world or as the elimination of doubt about the same. The former is the direct construction of explanations from empirical data and logic, the latter the reduction of potential explanations. It was established above how the interpretation of empirical data is theory-laden, so neither approach is trivial. The ubiquitous element in the scientific method is empiricism, which holds that knowledge is created by a process involving observation; scientific theories generalize observations. This is in opposition to stringent forms of rationalism, which holds that knowledge is created by the human intellect; later clarified by Popper to be built on prior theory. The scientific method embodies the position that reason alone cannot solve a particular scientific problem; it unequivocally refutes claims that revelation, political or religious dogma, appeals to tradition, commonly held beliefs, common sense, or currently held theories pose the only possible means of demonstrating truth. In 1877, C. S. Peirce characterized inquiry in general not as the pursuit of truth per se but as the struggle to move from irritating, inhibitory doubts born of surprises, disagreements, and the like,


id: scientific-method-20260505-6
file: data/en.wikipedia.org/wiki/Scientific_method-6.md
category: reference
title: Scientific method
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_method
date_saved: 2026-05-05T03:43:09.265477+00:00
instance: kb-cron
parent: scientific-method-20260505-0
summary:
An example for how inductive and deductive reasoning works can be found in the history of gravitational theory. It took thousands of years of measurements, from the Chaldean, Indian, Persian, Greek, Arabic, and European astronomers, to fully record the motion of planet Earth. Kepler(and others) were then able to build their early theories by generalizing the collected data inductively, and Newton was able to unify prior theory and measurements into the consequences of his laws of motion in 1727. Another common example of inductive reasoning is the observation of a counterexample to current theory inducing the need for new ideas. Le Verrier in 1859 pointed out problems with the perihelion of Mercury that showed Newton's theory to be at least incomplete. The observed difference of Mercury's precession between Newtonian theory and observation was one of the things that occurred to Einstein as a possible early test of his theory of relativity. His relativistic calculations matched observation much more closely than Newtonian theory did. Though, today's Standard Model of physics suggests that we still do not know at least some of the concepts surrounding Einstein's theory, it holds to this day and is being built on deductively. A theory being assumed


id: scientific-method-20260505-7
file: data/en.wikipedia.org/wiki/Scientific_method-7.md
category: reference
title: Scientific method
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_method
date_saved: 2026-05-05T03:43:09.265477+00:00
instance: kb-cron
parent: scientific-method-20260505-0
summary:
=== Hypothetico-deductive method === The hypothetico-deductive model, or hypothesis-testing method, or "traditional" scientific method is, as the name implies, based on the formation of hypotheses and their testing via deductive reasoning. A hypothesis stating implications, often called predictions, that are falsifiable via experiment is of central importance here, as not the hypothesis but its implications are what is tested. Basically, scientists will look at the hypothetical consequences a (potential) theory holds and prove or disprove those instead of the theory itself. If an experimental test of those hypothetical consequences shows them to be false, it follows logically that the part of the theory that implied them was false also. If they show as true however, it does not prove the theory definitively. The logic of this testing is what affords this method of inquiry to be reasoned deductively. The formulated hypothesis is assumed to be 'true', and from that 'true' statement implications are inferred. If the following tests show the implications to be false, it follows that the hypothesis was false also. If test show the implications to be true, new insights will be gained. It is important to be aware that a positive test here will at best strongly


id: scientific-method-20260505-8
file: data/en.wikipedia.org/wiki/Scientific_method-8.md
category: reference
title: Scientific method
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_method
date_saved: 2026-05-05T03:43:09.265477+00:00
instance: kb-cron
parent: scientific-method-20260505-0
summary:
== Scientific inquiry == Scientific inquiry generally aims to obtain knowledge in the form of testable explanations that scientists can use to predict the results of future experiments. This allows scientists to gain a better understanding of the topic under study, and later to use that understanding to intervene in its causal mechanisms (such as to cure disease). The better an explanation is at making predictions, the more useful it frequently can be, and the more likely it will continue to explain a body of evidence better than its alternatives. The most successful explanations – those that explain and make accurate predictions in a wide range of circumstances – are often called scientific theories. Most experimental results do not produce large changes in human understanding; improvements in theoretical scientific understanding typically result from a gradual process of development over time, sometimes across different domains of science. Scientific models vary in the extent to which they have been experimentally tested and for how long, and in their acceptance in the scientific community. In general, explanations become accepted over time as evidence accumulates on a given topic, and the explanation in question proves more powerful than its alternatives at explaining the evidence.


id: scientific-method-20260505-9
file: data/en.wikipedia.org/wiki/Scientific_method-9.md
category: reference
title: Scientific method
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_method
date_saved: 2026-05-05T03:43:09.265477+00:00
instance: kb-cron
parent: scientific-method-20260505-0
summary:
We are to admit no more causes of natural things than such as are both true and sufficient to explain their appearances. The concept of parsimony should not be held to imply complete frugality in the pursuit of scientific truth. The general process starts at the opposite end of there being a vast number of potential explanations and general disorder. An example can be seen in Paul Krugman's process, who makes explicit to "dare to be silly". He writes that in his work on new theories of international trade he reviewed prior work with an open frame of mind and broadened his initial viewpoint even in unlikely directions. Once he had a sufficient body of ideas, he would try to simplify and thus find what worked among what did not. Specific to Krugman here was to "question the question". He recognised that prior work had applied erroneous models to already present evidence, commenting that "intelligent commentary was ignored". Thus touching on the need to bridge the common bias against other circles of thought. ==== Elegance ==== Occam's razor might fall under the heading of "simple elegance", but it is arguable that parsimony and elegance pull in different directions. Introducing additional


id: scientific-method-20260505-10
file: data/en.wikipedia.org/wiki/Scientific_method-10.md
category: reference
title: Scientific method
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_method
date_saved: 2026-05-05T03:43:09.265477+00:00
instance: kb-cron
parent: scientific-method-20260505-0
summary:
Scientific pluralism is a position within the philosophy of science that rejects various proposed unities of scientific method and subject matter. Scientific pluralists hold that science is not unified in one or more of the following ways: the metaphysics of its subject matter, the epistemology of scientific knowledge, or the research methods and models that should be used. Some pluralists believe that pluralism is necessary due to the nature of science. Others say that since scientific disciplines already vary in practice, there is no reason to believe this variation is wrong until a specific unification is empirically proven. Finally, some hold that pluralism should be allowed for normative reasons, even if unity were possible in theory. === Unificationism === Unificationism, in science, was a central tenet of logical positivism. Different logical positivists construed this doctrine in several different ways, e.g. as a reductionist thesis, that the objects investigated by the special sciences reduce to the objects of a common, putatively more basic domain of science, usually thought to be physics; as the thesis that all theories and results of the various sciences can or ought to be expressed in a common language or "universal slang"; or as the thesis that


id: scientific-method-20260505-11
file: data/en.wikipedia.org/wiki/Scientific_method-11.md
category: reference
title: Scientific method
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_method
date_saved: 2026-05-05T03:43:09.265477+00:00
instance: kb-cron
parent: scientific-method-20260505-0
summary:
==== Thought collectives ==== A perhaps accessible lead into what is claimed is Fleck's thought, echoed in Kuhn's concept of normal science. According to Fleck, scientists' work is based on a thought-style, that cannot be rationally reconstructed. It gets instilled through the experience of learning, and science is then advanced based on a tradition of shared assumptions held by what he called thought collectives. Fleck also claims this phenomenon to be largely invisible to members of the group. Comparably, following the field research in an academic scientific laboratory by Latour and Woolgar, Karin Knorr Cetina has conducted a comparative study of two scientific fields (namely high energy physics and molecular biology) to conclude that the epistemic practices and reasonings within both scientific communities are different enough to introduce the concept of "epistemic cultures", in contradiction with the idea that a so-called "scientific method" is unique and a unifying concept. ==== Situated cognition and relativism ==== On the idea of Fleck's thought collectives sociologists built the concept of situated cognition: that the perspective of the researcher fundamentally affects their work; and, too, more radical views. Norwood Russell Hanson, alongside Thomas Kuhn and Paul Feyerabend, extensively explored the theory-laden nature of observation


id: scientific-method-20260505-12
file: data/en.wikipedia.org/wiki/Scientific_method-12.md
category: reference
title: Scientific method
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_method
date_saved: 2026-05-05T03:43:09.265477+00:00
instance: kb-cron
parent: scientific-method-20260505-0
summary:
=== Science of complex systems === Science applied to complex systems can involve elements such as transdisciplinarity, systems theory, control theory, and scientific modelling. In general, the scientific method may be difficult to apply stringently to diverse, interconnected systems and large data sets. In particular, practices used within Big data, such as predictive analytics, may be considered to be at odds with the scientific method, as some of the data may have been stripped of the parameters which might be material in alternative hypotheses for an explanation; thus the stripped data would only serve to support the null hypothesis in the predictive analytics application. Fleck (1979), pp. 38–50 notes "a scientific discovery remains incomplete without considerations of the social practices that condition it". == Relationship with mathematics == Science is the process of gathering, comparing, and evaluating proposed models against observables. A model can be a simulation, mathematical or chemical formula, or set of proposed steps. Science is like mathematics in that researchers in both disciplines try to distinguish what is known from what is unknown at each stage of discovery. Models, in both science and mathematics, need to be internally consistent and also ought to be falsifiable (capable of


id: history-of-scientific-method-20260505-0
file: data/en.wikipedia.org/wiki/History_of_scientific_method-0.md
category: reference
title: History of scientific method
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/History_of_scientific_method
date_saved: 2026-05-05T03:43:10.599999+00:00
instance: kb-cron
parent: history-of-scientific-method-20260505-0
summary:
The history of scientific method considers changes in the methodology of scientific inquiry, as distinct from the history of science itself. The development of rules for scientific reasoning has not been straightforward; scientific method has been the subject of intense and recurring debate throughout the history of science, and eminent natural philosophers and scientists have argued for the primacy of one or another approach to establishing scientific knowledge. Rationalist explanations of nature, including atomism, appeared both in ancient Greece in the thought of Leucippus and Democritus, and in ancient India, in the Nyaya, Vaisheshika and Buddhist schools, while Charvaka materialism rejected inference as a source of knowledge in favour of an empiricism that was always subject to doubt. Aristotle pioneered scientific method in ancient Greece alongside his empirical biology and his work on logic, rejecting a purely deductive framework in favour of generalisations made from observations of nature. Some of the most important debates in the history of scientific method center on: rationalism, especially as advocated by René Descartes; inductivism, which rose to particular prominence with Isaac Newton and his followers; and hypothetico-deductivism, which came to the fore in the early 19th century. In the late 19th and early 20th


id: history-of-scientific-method-20260505-1
file: data/en.wikipedia.org/wiki/History_of_scientific_method-1.md
category: reference
title: History of scientific method
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/History_of_scientific_method
date_saved: 2026-05-05T03:43:10.599999+00:00
instance: kb-cron
parent: history-of-scientific-method-20260505-0
summary:
=== Aristotle === Aristotle's inductive-deductive method used inductions from observations to infer general principles, deductions from those principles to check against further observations, and more cycles of induction and deduction to continue the advance of knowledge. The Organon (Greek: Ὄργανον, meaning "instrument, tool, organ") is the standard collection of Aristotle's six works on logic. The name Organon was given by Aristotle's followers, the Peripatetics. The order of the works is not chronological (the chronology is now difficult to determine) but was deliberately chosen by Theophrastus to constitute a well-structured system. Indeed, parts of them seem to be a scheme of a lecture on logic. The arrangement of the works was made by Andronicus of Rhodes around 40 BCE. The Organon comprises the following six works: The Categories (Greek: Κατηγορίαι, Latin: Categoriae) introduces Aristotle's 10-fold classification of that which exists: substance, quantity, quality, relation, place, time, situation, condition, action, and passion. On Interpretation (Greek: Περὶ Ἑρμηνείας, Latin: De Interpretatione) introduces Aristotle's conception of proposition and judgment, and the various relations between affirmative, negative, universal, and particular propositions. Aristotle discusses the square of opposition or square of Apuleius in Chapter 7 and its appendix Chapter 8. Chapter 9 deals with the problem


id: history-of-scientific-method-20260505-2
file: data/en.wikipedia.org/wiki/History_of_scientific_method-2.md
category: reference
title: History of scientific method
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/History_of_scientific_method
date_saved: 2026-05-05T03:43:10.599999+00:00
instance: kb-cron
parent: history-of-scientific-method-20260505-0
summary:
=== Epicurus === In his work Kαvώv ('canon', a straight edge or ruler, thus any type of measure or standard, referred to as 'canonic'), Epicurus laid out his first rule for inquiry in physics: 'that the first concepts be seen, and that they not require demonstration '. His second rule for inquiry was that prior to an investigation, we are to have self-evident concepts, so that we might infer [ἔχωμεν οἷς σημειωσόμεθα] both what is expected [τò προσμένον], and also what is non-apparent [τò ἄδηλον]. Epicurus applies his method of inference (the use of observations as signs, Asmis' summary, p. 333: the method of using the phenomena as signs (σημεῖα) of what is unobserved) immediately to the atomic theory of Democritus. In Aristotle's Prior Analytics, Aristotle himself employs the use of signs. But Epicurus presented his 'canonic' as rival to Aristotle's logic. See: Lucretius (c. 99 BCE – c. 55 BCE) De rerum natura (On the nature of things) a didactic poem explaining Epicurus' philosophy and physics. == Emergence of inductive experimental method == During the Middle Ages issues of what is now termed science began to be addressed. There was greater emphasis on combining theory with practice in the


id: history-of-scientific-method-20260505-3
file: data/en.wikipedia.org/wiki/History_of_scientific_method-3.md
category: reference
title: History of scientific method
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/History_of_scientific_method
date_saved: 2026-05-05T03:43:10.599999+00:00
instance: kb-cron
parent: history-of-scientific-method-20260505-0
summary:
=== Al-Biruni === The Persian scientist Abū Rayhān al-Bīrūnī introduced early scientific methods for several different fields of inquiry during the 1020s and 1030s. For example, in his treatise on mineralogy, Kitab al-Jawahir (Book of Precious Stones), al-Biruni is "the most exact of experimental scientists", while in the introduction to his study of India, he declares that "to execute our project, it has not been possible to follow the geometric method" and thus became one of the pioneers of comparative sociology in insisting on field experience and information. He also developed an early experimental method for mechanics. Al-Biruni's methods resembled the modern scientific method, particularly in his emphasis on repeated experimentation. Biruni was concerned with how to conceptualize and prevent both systematic errors and observational biases, such as "errors caused by the use of small instruments and errors made by human observers." He argued that if instruments produce errors because of their imperfections or idiosyncratic qualities, then multiple observations must be taken, analyzed qualitatively, and on this basis, arrive at a "common-sense single value for the constant sought", whether an arithmetic mean or a "reliable estimate." In his scientific method, "universals came out of practical, experimental work" and "theories are


id: history-of-scientific-method-20260505-4
file: data/en.wikipedia.org/wiki/History_of_scientific_method-4.md
category: reference
title: History of scientific method
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/History_of_scientific_method
date_saved: 2026-05-05T03:43:10.599999+00:00
instance: kb-cron
parent: history-of-scientific-method-20260505-0
summary:
=== Renaissance humanism and medicine === Aristotle's ideas became a framework for critical debate beginning with absorption of the Aristotelian texts into the university curriculum in the first half of the 13th century. Contributing to this was the success of medieval theologians in reconciling Aristotelian philosophy with Christian theology. Within the sciences, medieval philosophers were not afraid of disagreeing with Aristotle on many specific issues, although their disagreements were stated within the language of Aristotelian philosophy. All medieval natural philosophers were Aristotelians, but "Aristotelianism" had become a somewhat broad and flexible concept. With the end of Middle Ages, the Renaissance rejection of medieval traditions coupled with an extreme reverence for classical sources led to a recovery of other ancient philosophical traditions, especially the teachings of Plato. By the 17th century, those who clung dogmatically to Aristotle's teachings were faced with several competing approaches to nature. The discovery of the Americas at the close of the 15th century showed the scholars of Europe that new discoveries could be found outside of the authoritative works of Aristotle, Pliny, Galen, and other ancient writers. Galen of Pergamon (129 – c. 200 AD) had studied with four schools in antiquity — Platonists, Aristotelians, Stoics,


id: history-of-scientific-method-20260505-5
file: data/en.wikipedia.org/wiki/History_of_scientific_method-5.md
category: reference
title: History of scientific method
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/History_of_scientific_method
date_saved: 2026-05-05T03:43:10.599999+00:00
instance: kb-cron
parent: history-of-scientific-method-20260505-0
summary:
The first modern science, in which practitioners were prepared to revise or reject long-held beliefs in the light of new evidence, was astronomy, and Tycho Brahe was the first modern astronomer. See Sextant, right. Note the explicit reduction of geometrical diagrams to practice (real objects with actual lengths and angles). In 1572, Tycho noticed a completely new star that was brighter than any star or planet. Astonished by the existence of a star that ought not to have been there and gaining the patronage of King Frederick II of Denmark, Tycho built the Uraniborg observatory at enormous cost. Over a period of fifteen years (1576–1591), Tycho and upwards of thirty assistants charted the positions of stars, planets, and other celestial bodies at Uraniborg with unprecedented accuracy. In 1600, Tycho hired Johannes Kepler to assist him in analyzing and publishing his observations. Kepler later used Tycho's observations of the motion of Mars to deduce the laws of planetary motion, which were later explained in terms of Newton's law of universal gravitation. Besides Tycho's specific role in advancing astronomical knowledge, Tycho's single-minded pursuit of ever-more-accurate measurement was enormously influential in creating a modern scientific culture in which theory and evidence were understood


id: history-of-scientific-method-20260505-6
file: data/en.wikipedia.org/wiki/History_of_scientific_method-6.md
category: reference
title: History of scientific method
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/History_of_scientific_method
date_saved: 2026-05-05T03:43:10.599999+00:00
instance: kb-cron
parent: history-of-scientific-method-20260505-0
summary:
In 1619, René Descartes began writing his first major treatise on proper scientific and philosophical thinking, the unfinished Rules for the Direction of the Mind. His aim was to create a complete science that he hoped would overthrow the Aristotelian system and establish himself as the sole architect of a new system of guiding principles for scientific research. This work was continued and clarified in his 1637 treatise, Discourse on Method, and in his 1641 Meditations. Descartes describes the intriguing and disciplined thought experiments he used to arrive at the idea we instantly associate with him: I think therefore I am. From this foundational thought, Descartes finds proof of the existence of a God who, possessing all possible perfections, will not deceive him provided he resolves "[...] never to accept anything for true which I did not clearly know to be such; that is to say, carefully to avoid precipitancy and prejudice, and to comprise nothing more in my judgment than what was presented to my mind so clearly and distinctly as to exclude all ground of methodic doubt." This rule allowed Descartes to progress beyond his own thoughts and judge that there exist extended bodies outside of his own


id: history-of-scientific-method-20260505-7
file: data/en.wikipedia.org/wiki/History_of_scientific_method-7.md
category: reference
title: History of scientific method
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/History_of_scientific_method
date_saved: 2026-05-05T03:43:10.599999+00:00
instance: kb-cron
parent: history-of-scientific-method-20260505-0
summary:
Both Bacon and Descartes wanted to provide a firm foundation for scientific thought that avoided the deceptions of the mind and senses. Bacon envisaged that foundation as essentially empirical, whereas Descartes provides a metaphysical foundation for knowledge. If there were any doubts about the direction in which scientific method would develop, they were set to rest by the success of Isaac Newton. Implicitly rejecting Descartes' emphasis on rationalism in favor of Bacon's empirical approach, he outlines his four "rules of reasoning" in the Principia, We are to admit no more causes of natural things than such as are both true and sufficient to explain their appearances. Therefore to the same natural effects we must, as far as possible, assign the same causes. The qualities of bodies, which admit neither intension nor remission of degrees, and which are found to belong to all bodies within the reach of our experiments, are to be esteemed the universal qualities of all bodies whatsoever. In experimental philosophy we are to look upon propositions collected by general induction from phænomena as accurately or very nearly true, notwithstanding any contrary hypotheses that may be imagined, until such time as other phænomena occur, by which they may


id: history-of-scientific-method-20260505-8
file: data/en.wikipedia.org/wiki/History_of_scientific_method-8.md
category: reference
title: History of scientific method
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/History_of_scientific_method
date_saved: 2026-05-05T03:43:10.599999+00:00
instance: kb-cron
parent: history-of-scientific-method-20260505-0
summary:
"In order to achieve completeness in our knowledge of nature, we must start from two extremes, from experience and from the intellect itself. ... The former method must conclude with natural laws, which it has abstracted from experience, while the latter must begin with principles, and gradually, as it develops more and more, it becomes ever more detailed. Of course, I speak here about the method as manifested in the process of the human intellect itself, not as found in textbooks, where the laws of nature which have been abstracted from the consequent experiences are placed first because they are required to explain the experiences. When the empiricist in his regression towards general laws of nature meets the metaphysician in his progression, science will reach its perfection." Ørsted's "First Introduction to General Physics" (1811) exemplified the steps of observation, hypothesis, deduction and experiment. In 1805, based on his researches on electromagnetism Ørsted came to believe that electricity is propagated by undulatory action (i.e., fluctuation). By 1820, he felt confident enough in his beliefs that he resolved to demonstrate them in a public lecture, and in fact observed a small magnetic effect from a galvanic circuit (i.e., voltaic circuit), without rehearsal;


id: history-of-scientific-method-20260505-9
file: data/en.wikipedia.org/wiki/History_of_scientific_method-9.md
category: reference
title: History of scientific method
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/History_of_scientific_method
date_saved: 2026-05-05T03:43:10.599999+00:00
instance: kb-cron
parent: history-of-scientific-method-20260505-0
summary:
In the late 19th century, Charles Sanders Peirce proposed a schema that would turn out to have considerable influence in the further development of scientific method generally. Peirce's work quickly accelerated the progress on several fronts. Firstly, speaking in broader context in "How to Make Our Ideas Clear" (1878), Peirce outlined an objectively verifiable method to test the truth of putative knowledge on a way that goes beyond mere foundational alternatives, focusing upon both Deduction and Induction. He thus placed induction and deduction in a complementary rather than competitive context (the latter of which had been the primary trend at least since David Hume a century before). Secondly, and of more direct importance to scientific method, Peirce put forth the basic schema for hypothesis-testing that continues to prevail today. Extracting the theory of inquiry from its raw materials in classical logic, he refined it in parallel with the early development of symbolic logic to address the then-current problems in scientific reasoning. Peirce examined and articulated the three fundamental modes of reasoning that play a role in scientific inquiry today, the processes that are currently known as abductive, deductive, and inductive inference. Thirdly, he played a major role in the progress


id: history-of-scientific-method-20260505-10
file: data/en.wikipedia.org/wiki/History_of_scientific_method-10.md
category: reference
title: History of scientific method
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/History_of_scientific_method
date_saved: 2026-05-05T03:43:10.599999+00:00
instance: kb-cron
parent: history-of-scientific-method-20260505-0
summary:
Investigation, or the art of inquiring into the nature of causes and their operation, is a leading characteristic of reason [...] Investigation implies three things – Observation, Hypothesis, and Experiment [...] The first step in the process, it will be perceived, is to observe... In 1885, the words "Scientific method" appear together with a description of the method in Francis Ellingwood Abbot's 'Scientific Theism', Now all the established truths which are formulated in the multifarious propositions of science have been won by the use of Scientific Method. This method consists in essentially three distinct steps (1) observation and experiment, (2) hypothesis, (3) verification by fresh observation and experiment. The Eleventh Edition of Encyclopædia Britannica did not include an article on scientific method; the Thirteenth Edition listed scientific management, but not method. By the Fifteenth Edition, a 1-inch article in the Micropædia of Britannica was part of the 1975 printing, while a fuller treatment (extending across multiple articles, and accessible mostly via the index volumes of Britannica) was available in later printings. == Current issues == In the past few centuries, some statistical methods have been developed, for reasoning in the face of uncertainty, as an outgrowth of methods for eliminating


id: allocation-concealment-20260505-0
file: data/en.wikipedia.org/wiki/Allocation_concealment-0.md
category: reference
title: Allocation concealment
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Allocation_concealment
date_saved: 2026-05-05T03:43:11.862091+00:00
instance: kb-cron
parent: allocation-concealment-20260505-0
summary:
In a randomized experiment, allocation concealment hides the sorting of trial participants into treatment groups so that this knowledge cannot be exploited. Adequate allocation concealment serves to prevent study participants from influencing treatment allocations for subjects. Studies with poor allocation concealment (or none at all) are prone to selection bias. Some standard methods of ensuring allocation concealment include sequentially numbered, opaque, sealed envelopes (SNOSE); sequentially numbered containers; pharmacy controlled randomization; and central randomization. CONSORT guidelines recommend that allocation concealment methods be included in a study's protocol, and that the allocation concealment methods be reported in detail in their publication; however, a 2005 study determined that most clinical trials have unclear allocation concealment in their protocols, in their publications, or both. A 2008 study of 146 meta-analyses concluded that the results of randomized controlled trials with inadequate or unclear allocation concealment tended to be biased toward beneficial effects only if the trials' outcomes were subjective as opposed to objective. Allocation concealment is different from blinding. An allocation concealment method prevents influence on the randomization process, while blinding conceals the outcome of the randomization. However, allocation concealment may also be called "randomization blinding". == Impact == Without the use of allocation concealment,


id: alphachip-controversy-20260505-0
file: data/en.wikipedia.org/wiki/AlphaChip_(controversy)-0.md
category: reference
title: AlphaChip (controversy)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/AlphaChip_(controversy)
date_saved: 2026-05-05T03:43:13.051556+00:00
instance: kb-cron
parent: alphachip-controversy-20260505-0
summary:
The AlphaChip controversy refers to a series of public, scholarly, and legal disputes surrounding a 2021 Nature paper by Google-affiliated researchers. The paper describes an approach to macro placement, a stage of chip floorplanning, based on reinforcement learning (RL), a machine learning method in which a system iteratively improves its decisions by optimizing performance-based reward signals. The primary technical question is whether the new techniques are better than existing (non-AI) techniques. Both internal Google studies and external attempts to replicate the algorithm have failed to show the claimed benefits. No head-to-head comparison is available because the data used in the paper is proprietary, and Google has not released any results from running its algorithm on public benchmarks. This has resulted in considerable skepticism over the paper's claims. In addition, the inability of others (both inside and outside of Google) to replicate the claimed results have sparked concerns about the paper’s methodology, reproducibility, and scientific integrity. The lead researchers of the Nature paper were affiliated with Google Brain, which became part of Google DeepMind, and later spun off into the company Ricursive. == Motivation for research: Macro placement in chip layout == Chip design for modern integrated circuits is a complex,


id: alphachip-controversy-20260505-1
file: data/en.wikipedia.org/wiki/AlphaChip_(controversy)-1.md
category: reference
title: AlphaChip (controversy)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/AlphaChip_(controversy)
date_saved: 2026-05-05T03:43:13.051556+00:00
instance: kb-cron
parent: alphachip-controversy-20260505-0
summary:
=== Internal dispute at Google and legal proceedings === In 2022, Satrajit Chatterjee, a Google engineer involved in reviewing the AlphaChip work, raised concerns internally and drafted an alternative analysis, (Stronger Baselines) arguing that established methods outperformed the RL approach under fair comparison. In March 2022, Google declined to publish this analysis and terminated Chatterjee's employment. Chatterjee filed a wrongful dismissal lawsuit, alleging that representations related to the AlphaChip research involved fraud and scientific misconduct. According to court documents, Chatterjee's study was conducted "in the context of a large potential Google Cloud deal". He noted that it "would have been unethical to imply that we had revolutionary technology when our tests showed otherwise" and claimed Google was deliberately withholding material information. Furthermore, the committee that reviewed his paper and disapproved its publication was allegedly chaired by subordinates of Jeff Dean, a senior co-author of the Nature paper. Google’s subsequent motion to dismiss was denied, holding that Chatterjee had plausibly alleged retaliation for refusing to engage in conduct he believed would violate state or federal law. === External controversy === The external questions can be summarized in four main points: (a) Are the claims supported by the evidence provided? (b) Did


id: alphachip-controversy-20260505-2
file: data/en.wikipedia.org/wiki/AlphaChip_(controversy)-2.md
category: reference
title: AlphaChip (controversy)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/AlphaChip_(controversy)
date_saved: 2026-05-05T03:43:13.051556+00:00
instance: kb-cron
parent: alphachip-controversy-20260505-0
summary:
== Nature editorial actions == In April 2022, the peer review file for the Nature article was included as a supplementary information file. In September 2023, Nature added an editor's note to "A graph placement methodology for fast chip design" stating that the paper's performance claims had been called into question and that the editors were investigating the concerns. On 21 September 2023, Andrew B. Kahng's accompanying News & Views article was retracted; the retraction notice said that new information about the methods used in the Google paper had become available after publication and had changed the author’s assessment, and it also said that Nature was conducting an independent investigation of the paper’s performance claims. By late September 2024, the editor's note was removed without explanation, but Nature published an addendum to the original paper (dated 26 September 2024). The addendum introduced the name AlphaChip for the proposed RL technique and described methodological details that critics had previously identified as missing, including the use of initial ( x , y ) {\displaystyle (x,y)} locations. The addendum addressed some methodological details but still lacked the full training and evaluation inputs needed for independent replication. == Author responses and ensuing debate ==


id: analysis-20260505-0
file: data/en.wikipedia.org/wiki/Analysis-0.md
category: reference
title: Analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Analysis
date_saved: 2026-05-05T03:43:14.317109+00:00
instance: kb-cron
parent: analysis-20260505-0
summary:
Analysis (pl.: analyses) is the process of breaking a complex topic or substance into smaller parts in order to gain a better understanding of it. The technique has been applied in the study of mathematics and logic since before Aristotle, though analysis as a formal concept is a relatively recent development. The word comes from the Ancient Greek ἀνάλυσις (analysis, "a breaking-up" or "an untying" from ana- "up, throughout" and lysis "a loosening"). From it also comes the word's plural, analyses. As a formal concept, the method has variously been ascribed to René Descartes (Discourse on the Method), and Galileo Galilei. It has also been ascribed to Isaac Newton, in the form of a practical method of physical discovery (which he did not name). The converse of analysis is synthesis: putting the pieces back together again in a new or different whole. == Academic analysis == Academic analysis is a systematic, methodological approach to inquiry used across scholarly disciplines to deconstruct complex ideas, texts, data, or systems. Its primary aim is to foster a deeper, evidence-based understanding, challenge assumptions, and contribute to a body of knowledge through critical examination and rigorous argumentation. This form of analysis is foundational to higher


id: analysis-20260505-1
file: data/en.wikipedia.org/wiki/Analysis-1.md
category: reference
title: Analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Analysis
date_saved: 2026-05-05T03:43:14.317109+00:00
instance: kb-cron
parent: analysis-20260505-0
summary:
=== Music === Musical analysis – a process attempting to answer the question "How does this music work?" Musical Analysis is a study of how the composers use the notes together to compose music. Those studying music will find differences with each composer's musical analysis, which differs depending on the culture and history of music studied. An analysis of music is meant to simplify the music for you. Schenkerian analysis Schenkerian analysis is a collection of music analysis that focuses on the production of the graphic representation. This includes both analytical procedure as well as the notational style. Simply put, it analyzes tonal music which includes all chords and tones within a composition. === Philosophy === Philosophical analysis – a general term for the techniques used by philosophers Philosophical analysis refers to the clarification and composition of words put together and the entailed meaning behind them. Philosophical analysis dives deeper into the meaning of words and seeks to clarify that meaning by contrasting the various definitions. It is the study of reality, justification of claims, and the analysis of various concepts. Branches of philosophy include logic, justification, metaphysics, values and ethics. If questions can be answered empirically, meaning it can


id: analysis-20260505-2
file: data/en.wikipedia.org/wiki/Analysis-2.md
category: reference
title: Analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Analysis
date_saved: 2026-05-05T03:43:14.317109+00:00
instance: kb-cron
parent: analysis-20260505-0
summary:
The synthetic proof proceeds by shewing that the proposed new truth involves certain admitted truths. An analytic proof begins by an assumption, upon which a synthetic reasoning is founded. The Greeks distinguished theoretic from problematic analysis. A theoretic analysis is of the following kind. To prove that A is B, assume first that A is B. If so, then, since B is C and C is D and D is E, therefore A is E. If this be known a falsity, A is not B. But if this be a known truth and all the intermediate propositions be convertible, then the reverse process, A is E, E is D, D is C, C is B, therefore A is B, constitutes a synthetic proof of the original theorem. Problematic analysis is applied in all cases where it is proposed to construct a figure which is assumed to satisfy a given condition. The problem is then converted into some theorem which is involved in the condition and which is proved synthetically, and the steps of this synthetic proof taken backwards are a synthetic solution of the problem. === Psychotherapy === Psychoanalysis – seeks to elucidate connections among unconscious components of patients' mental


id: anomalistic-psychology-20260505-0
file: data/en.wikipedia.org/wiki/Anomalistic_psychology-0.md
category: reference
title: Anomalistic psychology
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Anomalistic_psychology
date_saved: 2026-05-05T03:43:15.577192+00:00
instance: kb-cron
parent: anomalistic-psychology-20260505-0
summary:
In psychology, anomalistic psychology is the study of human behaviour and experience connected with what is often called the paranormal, with few assumptions made about the validity of the reported phenomena. == Early history == According to anomalistic psychology, paranormal phenomena have naturalistic explanations resulting from psychological and physical factors which have given the false impression of paranormal activity to some people. There were many early publications that gave rational explanations for alleged paranormal experiences. The physician John Ferriar wrote An Essay Towards a Theory of Apparitions in 1813 in which he argued that sightings of ghosts were the result of optical illusions. Later, the French physician Alexandre Jacques François Brière de Boismont published On Hallucinations: Or, the Rational History of Apparitions, Dreams, Ecstasy, Magnetism, and Somnambulism in 1845 in which he claimed sightings of ghosts were the result of hallucinations. William Benjamin Carpenter, in his book Mesmerism, Spiritualism, Etc: Historically and Scientifically Considered (1877), wrote that Spiritualist practices could be explained by fraud, delusion, hypnotism and suggestion. The British psychiatrist Henry Maudsley, in Natural Causes and Supernatural Seemings (1886), wrote that so-called supernatural experiences could be explained in terms of disorders of the mind and were simply "malobservations and


id: anomalistic-psychology-20260505-1
file: data/en.wikipedia.org/wiki/Anomalistic_psychology-1.md
category: reference
title: Anomalistic psychology
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Anomalistic_psychology
date_saved: 2026-05-05T03:43:15.577192+00:00
instance: kb-cron
parent: anomalistic-psychology-20260505-0
summary:
=== Mediumship === Research and empirical evidence from psychology for over a hundred years has revealed that where there is not fraud, mediumship and Spiritualistic practices can be explained by psychological factors. Trance mediumship, which is claimed by the Spiritualists to be caused by discarnate spirits speaking through the medium, has been proven in some cases to be the emergence of alternate personalities from the medium's subconscious mind. The medium may obtain information about their clients, called sitters, by secretly eavesdropping on sitter's conversations or searching telephone directories, the internet and newspapers before the sittings. Mediums are known for employing a technique called cold reading which involves obtaining information from the sitter's behavior, clothing, posture, and jewellery. In a series of fake seance experiments (Wiseman et al. 2003), an actor suggested to paranormal believers and disbelievers that a table was levitating when, in fact, it remained stationary. After the seance, approximately one third of the participants incorrectly reported that the table had moved. The results showed a greater percentage of believers reporting that the table had moved. In another experiment the believers had also reported that a handbell had moved when it had remained stationary and expressed their belief that


id: anomalistic-psychology-20260505-2
file: data/en.wikipedia.org/wiki/Anomalistic_psychology-2.md
category: reference
title: Anomalistic psychology
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Anomalistic_psychology
date_saved: 2026-05-05T03:43:15.577192+00:00
instance: kb-cron
parent: anomalistic-psychology-20260505-0
summary:
The difference between anomalistic psychology and parapsychology is in terms of the aims of what each discipline is about. Parapsychologists typically are actually searching for evidence to prove the reality of paranormal forces, to prove they really do exist. So the starting assumption is that paranormal things do happen, whereas anomalistic psychologists tend to start from the position that paranormal forces probably don't exist and that therefore we should be looking for other kinds of explanations, in particular the psychological explanations for those experiences that people typically label as paranormal. Anomalistic psychology has been reported to be on the rise. It is now offered as an option on many psychology degree programmes and is also an option on the A2 psychology syllabus in the UK. == See also == Australian Sheep-Goat Scale Psychology of paranormal belief == References == == Further reading == Gustav Jahoda. (1974). The Psychology of Superstition. Jason Aronson, Inc. Publisher. ISBN 978-0876681855 David Marks. (2000). The Psychology of the Psychic. Prometheus Books. ISBN 978-1573927987 Andrew Neher. (2011). Paranormal and Transcendental Experience: A Psychological Examination. Dover Publications. ISBN 978-0486261676 John Schumaker. (1990). Wings of Illusion: The Origin, Nature and Future of Paranormal Belief. Prometheus Books. ISBN 978-0879756246


id: blinded-experiment-20260505-0
file: data/en.wikipedia.org/wiki/Blinded_experiment-0.md
category: reference
title: Blinded experiment
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Blinded_experiment
date_saved: 2026-05-05T03:43:16.884871+00:00
instance: kb-cron
parent: blinded-experiment-20260505-0
summary:
In a blind or blinded experiment, information that could influence participants or investigators is withheld until the experiment is completed. Blinding is used to reduce or eliminate potential sources of bias, such as participants’ expectations, the observer-expectancy effect, observer bias, confirmation bias, and other cognitive or procedural influences. Blinding can be applied to different participants in an experiment, including study subjects, researchers, technicians, data analysts, and outcome assessors. When multiple groups are blinded simultaneously (for example, both participants and researchers), the design is referred to as a double-blind study. In some cases, blinding is desirable but impractical or unethical. For example, it is not possible to blind a participant receiving a physical therapy intervention, or a surgeon performing an operative procedure. Well-designed clinical protocols therefore aim to maximize the effectiveness of blinding within ethical and practical constraints. During the course of an experiment, a participant becomes unblinded if they deduce or otherwise obtain information that has been masked to them. For example, a patient who experiences a side effect may correctly guess their treatment, becoming unblinded. Unblinding is common in blinded experiments, particularly in pharmacological trials. In particular, trials on pain medication and antidepressants are poorly blinded. Unblinding that occurs


id: blinded-experiment-20260505-1
file: data/en.wikipedia.org/wiki/Blinded_experiment-1.md
category: reference
title: Blinded experiment
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Blinded_experiment
date_saved: 2026-05-05T03:43:16.884871+00:00
instance: kb-cron
parent: blinded-experiment-20260505-0
summary:
== Unblinding == "Unblinding" occurs in a blinded experiment when information is revealed to someone to whom it has been masked. In clinical studies, unblinding may occur unintentionally when a patient deduces their treatment group. Unblinding that occurs before the conclusion of an experiment is a source of bias. Some degree of premature unblinding is common in blinded experiments. When a blind is imperfect, its success is judged on a spectrum with no blind (or complete failure of blinding) on one end, perfect blinding on the other, and poor or good blinding between. Thus, the common view of studies as blinded or unblinded exemplifies a false dichotomy. The success of blinding is assessed by asking study participants about information that has been masked from them (e.g., whether the participant received the drug or placebo?). In a perfectly blinded experiment, the responses should be consistent with no knowledge of the masked information. However, if unblinding has occurred, the responses will indicate the degree of unblinding. Since unblinding cannot be measured directly, but must be inferred from participants' responses, its measured value will depend on the nature of the questions asked. As a result, it is not possible to objectively measure unblinding.


id: blinded-experiment-20260505-2
file: data/en.wikipedia.org/wiki/Blinded_experiment-2.md
category: reference
title: Blinded experiment
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Blinded_experiment
date_saved: 2026-05-05T03:43:16.884871+00:00
instance: kb-cron
parent: blinded-experiment-20260505-0
summary:
==== Acupuncture ==== While the possibility of blinded trials on acupuncture is controversial, a 2003 review of 47 randomized controlled trials found no fewer than four methods of blinding patients to acupuncture treatment: 1) superficial needling of true acupuncture points, 2) use of acupuncture points which are not indicated for the condition being treated, 3) insertion of needles outside of true acupuncture points, and 4) the use of placebo needles which are designed not to penetrate the skin. The authors concluded that there was "no clear association between the type of sham intervention used and the results of the trials." A 2018 study on acupuncture, which used needles that did not penetrate the skin as a sham treatment, found that 68% of patients and 83% of acupuncturists correctly identified their group allocation. The authors concluded that blinding had failed, but that more advanced placebos may someday enable well-blinded studies in acupuncture. === In physics === It is standard practice in physics to perform blinded data analysis. Once the data analysis is complete, one may unblind the data. A prior agreement to publish the data regardless of the results of the study may be made to prevent publication bias. === In


id: blind-taste-test-20260505-0
file: data/en.wikipedia.org/wiki/Blind_taste_test-0.md
category: reference
title: Blind taste test
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Blind_taste_test
date_saved: 2026-05-05T03:43:18.183932+00:00
instance: kb-cron
parent: blind-taste-test-20260505-0
summary:
In marketing, a blind taste test is often used as a tool for companies to compare their brand to another brand. For example, the Pepsi Challenge is a famous taste test that has been run by Pepsi since 1975. Additionally, taste tests are sometimes used as a tool by companies to develop their brand or new products. Blind taste tests are ideal for goods such as food or wine (see blind wine tasting) that are consumed directly. Researchers use blind taste tests to obtain information about customers' perceptions and preferences on the goods. Blind taste test can be used to: Track views on a product over time assess changes or improvements made to a product gauge reactions to a new product == Overview == Blind taste tests require a "blind testing" meaning the people taking the blind taste test are unaware of the identity of the brand being tested, or if done at home this can be as simple as a blindfold over the person taking the test. This means that any bias, preconceived ideas about a particular brand or food, is eliminated. The people taking the test will also be unaware of any changes done to the product. In


id: case-study-20260505-0
file: data/en.wikipedia.org/wiki/Case_study-0.md
category: reference
title: Case study
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Case_study
date_saved: 2026-05-05T03:43:19.359633+00:00
instance: kb-cron
parent: case-study-20260505-0
summary:
A case study is an in-depth, detailed examination of a particular case (or cases) within a real-world context. For example, case studies in medicine may focus on an individual patient or ailment; case studies in business might cover a particular firm's strategy or a broader market; similarly, case studies in politics can range from a narrow happening over time like the operations of a specific political campaign, to an enormous undertaking like world war, or more often the policy analysis of real-world problems affecting multiple stakeholders. Generally, a case study can highlight nearly any individual, group, organization, event, belief system, or action. A case study does not necessarily have to be one observation (N=1), but may include many observations (one or multiple individuals and entities across multiple time periods, all within the same case study). Research projects involving numerous cases are frequently called cross-case research, whereas a study of a single case is called within-case research. Case study research has been extensively practiced in both the social and natural sciences. == Definition == There are multiple definitions of case studies, which may emphasize the number of observations (a small N), the method (qualitative), the thickness of the research (a comprehensive


id: case-study-20260505-1
file: data/en.wikipedia.org/wiki/Case_study-1.md
category: reference
title: Case study
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Case_study
date_saved: 2026-05-05T03:43:19.359633+00:00
instance: kb-cron
parent: case-study-20260505-0
summary:
Typical cases are cases that exemplify a stable cross-case relationship. These cases are representative of the larger population of cases, and the purpose of the study is to look within the case rather than compare it with other cases. Diverse cases are cases that have variations on the relevant X and Y variables. Due to the range of variation on the relevant variables, these cases are representative of the full population of cases. Extreme cases are cases that have an extreme value on the X or Y variable relative to other cases. Deviant cases are cases that defy existing theories and common sense. They not only have extreme values on X or Y (like extreme cases) but defy existing knowledge about causal relations. Influential cases are cases that are central to a model or theory (for example, Nazi Germany in theories of fascism and the far-right). Most similar cases are cases that are similar on all the independent variables, except the one of interest to the researcher. Most different cases are cases that are different on all the independent variables, except the one of interest to the researcher. For theoretical discovery, Jason Seawright recommends using deviant cases or extreme cases


id: case-study-20260505-2
file: data/en.wikipedia.org/wiki/Case_study-2.md
category: reference
title: Case study
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Case_study
date_saved: 2026-05-05T03:43:19.359633+00:00
instance: kb-cron
parent: case-study-20260505-0
summary:
== Limitations == Designing Social Inquiry (also called "KKV"), an influential 1994 book written by Gary King, Robert Keohane, and Sidney Verba, primarily applies lessons from regression-oriented analysis to qualitative research, arguing that the same logics of causal inference can be used in both types of research. The authors' recommendation is to increase the number of observations (a recommendation that Barbara Geddes also makes in Paradigms and Sand Castles), because few observations make it harder to estimate multiple causal effects, as well as increase the risk that there is measurement error, and that an event in a single case was caused by random error or unobservable factors. KKV sees process-tracing and qualitative research as being "unable to yield strong causal inference" because qualitative scholars would struggle with determining which of many intervening variables truly links the independent variable with a dependent variable. The primary problem is that qualitative research lacks a sufficient number of observations to properly estimate the effects of an independent variable. They write that the number of observations could be increased through various means, but that would simultaneously lead to another problem: that the number of variables would increase and thus reduce degrees of freedom. Christopher H.


id: causality-20260505-0
file: data/en.wikipedia.org/wiki/Causality-0.md
category: reference
title: Causality
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Causality
date_saved: 2026-05-05T03:43:20.686725+00:00
instance: kb-cron
parent: causality-20260505-0
summary:
Causality is an influence by which one event, process, state, or subject (i.e., a cause) contributes to the production of another event, process, state, or object (i.e., an effect) where the cause is at least partly responsible for the effect, and the effect is at least partly dependent on the cause. The cause of something may also be described as the reason behind the event or process. In general, a process can have multiple causes, which are also said to be causal factors for it, and all lie in its past. An effect can in turn be a cause of, or causal factor for, many other effects, which all lie in its future. Thus, the distinction between cause and effect either follows from or else provides the distinction between past and future. While the former viewpoint is more prevalent in physics, some writers have held that causality is metaphysically prior to notions of time and space. Causality is an abstraction that indicates how the world progresses. As such, it is a basic concept, and one might expect it to be more apt as an explanation of other concepts of progression than as something to be explained by yet more fundamental


id: causality-20260505-1
file: data/en.wikipedia.org/wiki/Causality-1.md
category: reference
title: Causality
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Causality
date_saved: 2026-05-05T03:43:20.686725+00:00
instance: kb-cron
parent: causality-20260505-0
summary:
==== Geometrical significance ==== Causality has the properties of antecedence and contiguity. These are topological, and are ingredients for space-time geometry. As developed by Alfred Robb, these properties allow the derivation of the notions of time and space. Max Jammer writes "the Einstein postulate ... opens the way to a straightforward construction of the causal topology ... of Minkowski space." Causal efficacy propagates no faster than light. Thus, the notion of causality is metaphysically prior to the notions of time and space. In practical terms, this is because use of the relation of causality is necessary for the interpretation of empirical experiments. Interpretation of experiments is needed to establish the physical and geometrical notions of time and space. ==== Volition ==== The deterministic world-view holds that the history of the universe can be exhaustively represented as a progression of events following one after the other as cause and effect. Incompatibilism holds that determinism is incompatible with free will, so if determinism is true, "free will" does not exist. Compatibilism, on the other hand, holds that determinism is compatible with, or even necessary for, free will. === Necessary and sufficient causes === Causes may sometimes be distinguished into two types: necessary


id: causality-20260505-2
file: data/en.wikipedia.org/wiki/Causality-2.md
category: reference
title: Causality
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Causality
date_saved: 2026-05-05T03:43:20.686725+00:00
instance: kb-cron
parent: causality-20260505-0
summary:
If A were a triangle, then A would have three sides. If switch S were thrown, then bulb B would light. In the first case, it would be incorrect to say that A's being a triangle caused it to have three sides, since the relationship between triangularity and three-sidedness is that of definition. The property of having three sides actually determines A's state as a triangle. Nonetheless, even when interpreted counterfactually, the first statement is true. An early version of Aristotle's "four cause" theory is described as recognizing "essential cause". In this version of the theory, that the closed polygon has three sides is said to be the "essential cause" of its being a triangle. This use of the word 'cause' is of course now far obsolete. Nevertheless, it is within the scope of ordinary language to say that it is essential to a triangle that it has three sides. A full grasp of the concept of conditionals is important to understanding the literature on causality. In everyday language, loose conditional statements are often enough made, and need to be interpreted carefully. === Questionable cause === Fallacies of questionable cause, also known as causal fallacies, non-causa pro causa (Latin for


id: causality-20260505-3
file: data/en.wikipedia.org/wiki/Causality-3.md
category: reference
title: Causality
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Causality
date_saved: 2026-05-05T03:43:20.686725+00:00
instance: kb-cron
parent: causality-20260505-0
summary:
Interpreting causation as a deterministic relation means that if A causes B, then A must always be followed by B. In this sense, war does not cause deaths, nor does smoking cause cancer or emphysema. As a result, many turn to a notion of probabilistic causation. Informally, A ("The person is a smoker") probabilistically causes B ("The person has now or will have cancer at some time in the future"), if the information that A occurred increases the likelihood of B's occurrence. Formally, P{B|A}≥ P{B} where P{B|A} is the conditional probability that B will occur given the information that A occurred, and P{B} is the probability that B will occur having no knowledge whether A did or did not occur. This intuitive condition is not adequate as a definition for probabilistic causation because of its being too general and thus not meeting our intuitive notion of cause and effect. For example, if A denotes the event "The person is a smoker," B denotes the event "The person now has or will have cancer at some time in the future" and C denotes the event "The person now has or will have emphysema some time in the future," then the following


id: causality-20260505-4
file: data/en.wikipedia.org/wiki/Causality-4.md
category: reference
title: Causality
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Causality
date_saved: 2026-05-05T03:43:20.686725+00:00
instance: kb-cron
parent: causality-20260505-0
summary:
Type 1 and type 2 represent the same statistical dependencies (i.e., X {\displaystyle X} and Z {\displaystyle Z} are independent given Y {\displaystyle Y} ) and are, therefore, indistinguishable within purely cross-sectional data. Type 3, however, can be uniquely identified, since X {\displaystyle X} and Z {\displaystyle Z} are marginally independent and all other pairs are dependent. Thus, while the skeletons (the graphs stripped of arrows) of these three triplets are identical, the directionality of the arrows is partially identifiable. The same distinction applies when X {\displaystyle X} and Z {\displaystyle Z} have common ancestors, except that one must first condition on those ancestors. Algorithms have been developed to systematically determine the skeleton of the underlying graph and, then, orient all arrows whose directionality is dictated by the conditional independencies observed. Alternative methods of structure learning search through the many possible causal structures among the variables, and remove ones which are strongly incompatible with the observed correlations. In general this leaves a set of possible causal relations, which should then be tested by analyzing time series data or, preferably, designing appropriately controlled experiments. In contrast with Bayesian Networks, path analysis (and its generalization, structural equation modeling), serve better to estimate


id: causality-20260505-5
file: data/en.wikipedia.org/wiki/Causality-5.md
category: reference
title: Causality
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Causality
date_saved: 2026-05-05T03:43:20.686725+00:00
instance: kb-cron
parent: causality-20260505-0
summary:
=== Science === For the scientific investigation of efficient causality, the cause and effect are each best conceived of as temporally transient processes. Within the conceptual frame of the scientific method, an investigator sets up several distinct and contrasting temporally transient material processes that have the structure of experiments, and records candidate material responses, normally intending to determine causality in the physical world. For instance, one may want to know whether a high intake of carrots causes humans to develop the bubonic plague. The quantity of carrot intake is a process that is varied from occasion to occasion. The occurrence or non-occurrence of subsequent bubonic plague is recorded. To establish causality, the experiment must fulfill certain criteria, only one example of which is mentioned here. For example, instances of the hypothesized cause must be set up to occur at a time when the hypothesized effect is relatively unlikely in the absence of the hypothesized cause; such unlikelihood is to be established by empirical evidence. A mere observation of a correlation is not nearly adequate to establish causality. In nearly all cases, establishment of causality relies on repetition of experiments and probabilistic reasoning. Hardly ever is causality established more firmly than


id: causality-20260505-6
file: data/en.wikipedia.org/wiki/Causality-6.md
category: reference
title: Causality
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Causality
date_saved: 2026-05-05T03:43:20.686725+00:00
instance: kb-cron
parent: causality-20260505-0
summary:
Psychologists take an empirical approach to causality, investigating how people and non-human animals detect or infer causation from sensory information, prior experience and innate knowledge. Attribution: Attribution theory is the theory concerning how people explain individual occurrences of causation. Attribution can be external (assigning causality to an outside agent or force—claiming that some outside thing motivated the event) or internal (assigning causality to factors within the person—taking personal responsibility or accountability for one's actions and claiming that the person was directly responsible for the event). Taking causation one step further, the type of attribution a person provides influences their future behavior. The intention behind the cause or the effect can be covered by the subject of action. See also accident; blame; intent; and responsibility. Causal powers Whereas David Hume argued that causes are inferred from non-causal observations, Immanuel Kant claimed that people have innate assumptions about causes. Within psychology, Patricia Cheng attempted to reconcile the Humean and Kantian views. According to her power PC theory, people filter observations of events through an intuition that causes have the power to generate (or prevent) their effects, thereby inferring specific cause-effect relations. Causation and salience Our view of causation depends on what we


id: causality-20260505-7
file: data/en.wikipedia.org/wiki/Causality-7.md
category: reference
title: Causality
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Causality
date_saved: 2026-05-05T03:43:20.686725+00:00
instance: kb-cron
parent: causality-20260505-0
summary:
is postulated, in which y i {\displaystyle y_{i}} is the ith observation of the dependent variable (hypothesized to be the caused variable), x j , i {\displaystyle x_{j,i}} for j=1,...,k is the ith observation on the jth independent variable (hypothesized to be a causative variable), and e i {\displaystyle e_{i}} is the error term for the ith observation (containing the combined effects of all other causative variables, which must be uncorrelated with the included independent variables). If there is reason to believe that none of the x j {\displaystyle x_{j}} s is caused by y, then estimates of the coefficients a j {\displaystyle a_{j}} are obtained. If the null hypothesis that a j = 0 {\displaystyle a_{j}=0} is rejected, then the alternative hypothesis that a j ≠ 0 {\displaystyle a_{j}\neq 0} and equivalently that x j {\displaystyle x_{j}} causes y cannot be rejected. On the other hand, if the null hypothesis that a j = 0 {\displaystyle a_{j}=0} cannot be rejected, then equivalently the hypothesis of no causal effect of x j {\displaystyle x_{j}} on y cannot be rejected. Here the notion of causality is one of contributory causality as discussed above: If the true value a j ≠ 0


id: causality-20260505-8
file: data/en.wikipedia.org/wiki/Causality-8.md
category: reference
title: Causality
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Causality
date_saved: 2026-05-05T03:43:20.686725+00:00
instance: kb-cron
parent: causality-20260505-0
summary:
==== History ==== In the discussion of history, events are sometimes considered as if in some way being agents that can then bring about other historical events. Thus, the combination of poor harvests, the hardships of the peasants, high taxes, lack of representation of the people, and kingly ineptitude are among the causes of the French Revolution. This is a somewhat Platonic and Hegelian view that reifies causes as ontological entities. In Aristotelian terminology, this use approximates to the case of the efficient cause. Some philosophers of history such as Arthur Danto have claimed that "explanations in history and elsewhere" describe "not simply an event—something that happens—but a change". Like many practicing historians, they treat causes as intersecting actions and sets of actions which bring about "larger changes", in Danto's words: to decide "what are the elements which persist through a change" is "rather simple" when treating an individual's "shift in attitude", but "it is considerably more complex and metaphysically challenging when we are interested in such a change as, say, the break-up of feudalism or the emergence of nationalism". Much of the historical debate about causes has focused on the relationship between communicative and other actions, between singular and


id: causality-20260505-9
file: data/en.wikipedia.org/wiki/Causality-9.md
category: reference
title: Causality
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Causality
date_saved: 2026-05-05T03:43:20.686725+00:00
instance: kb-cron
parent: causality-20260505-0
summary:
Karma is the causality principle focusing on 1) causes, 2) actions, 3) effects, where it is the mind's phenomena that guide the actions that the actor performs. Buddhism trains the actor's actions for continued and uncontrived virtuous outcomes aimed at reducing suffering. This follows the Subject–verb–object structure. The general or universal definition of pratityasamutpada (or "dependent origination" or "dependent arising" or "interdependent co-arising") is that everything arises in dependence upon multiple causes and conditions; nothing exists as a singular, independent entity. A traditional example in Buddhist texts is of three sticks standing upright and leaning against each other and supporting each other. If one stick is taken away, the other two will fall to the ground. Causality in the Chittamatrin Buddhist school approach, Asanga's (c. 400 CE) mind-only Buddhist school, asserts that objects cause consciousness in the mind's image. Because causes precede effects, which must be different entities, then subject and object are different. For this school, there are no objects which are entities external to a perceiving consciousness. The Chittamatrin and the Yogachara Svatantrika schools accept that there are no objects external to the observer's causality. This largely follows the Nikayas approach. The Vaibhashika (c. 500 CE) is an


id: causality-20260505-10
file: data/en.wikipedia.org/wiki/Causality-10.md
category: reference
title: Causality
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Causality
date_saved: 2026-05-05T03:43:20.686725+00:00
instance: kb-cron
parent: causality-20260505-0
summary:
Material cause, the material whence a thing has come or that which persists while it changes, as for example, one's mother or the bronze of a statue (see also substance theory). Formal cause, whereby a thing's dynamic form or static shape determines the thing's properties and function, as a human differs from a statue of a human or as a statue differs from a lump of bronze. Efficient cause, which imparts the first relevant movement, as a human lifts a rock or raises a statue. This is the main topic of the present article. Final cause, the criterion of completion, or the end; it may refer to an action or to an inanimate process. Examples: Socrates takes a walk after dinner for the sake of his health; earth falls to the lowest level because that is its nature. Of Aristotle's four kinds or explanatory modes, only one, the 'efficient cause' is a cause as defined in the leading paragraph of this present article. The other three explanatory modes might be rendered material composition, structure and dynamics, and, again, criterion of completion. The word that Aristotle used was αἰτία. For the present purpose, that Greek word would be better translated as


id: causality-20260505-11
file: data/en.wikipedia.org/wiki/Causality-11.md
category: reference
title: Causality
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Causality
date_saved: 2026-05-05T03:43:20.686725+00:00
instance: kb-cron
parent: causality-20260505-0
summary:
==== Middle Ages ==== In line with Aristotelian cosmology, Thomas Aquinas posed a hierarchy prioritizing Aristotle's four causes: "final > efficient > material > formal". Aquinas sought to identify the first efficient cause—now simply first cause—as everyone would agree, said Aquinas, to call it God. Later in the Middle Ages, many scholars conceded that the first cause was God, but explained that many earthly events occur within God's design or plan, and thereby scholars sought freedom to investigate the numerous secondary causes. ==== After the Middle Ages ==== For Aristotelian philosophy before Aquinas, the word cause had a broad meaning. It meant 'answer to a why question' or 'explanation', and Aristotelian scholars recognized four kinds of such answers. With the end of the Middle Ages, in many philosophical usages, the meaning of the word 'cause' narrowed. It often lost that broad meaning, and was restricted to just one of the four kinds. For authors such as Niccolò Machiavelli, in the field of political thinking, and Francis Bacon, concerning science more generally, Aristotle's moving cause was the focus of their interest. A widely used modern definition of causality in this newly narrowed sense was assumed by David Hume. He undertook an


