# kb

id: provocation-test-20260505-0
file: data/en.wikipedia.org/wiki/Provocation_test-0.md
category: reference
title: Provocation test
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Provocation_test
date_saved: 2026-05-05T09:51:31.547405+00:00
instance: kb-cron
parent: provocation-test-20260505-0
summary:
A provocation test, also called a provocation trial or provocation study, is a form of medical clinical trial whereby participants are exposed to either a substance or "thing" that is claimed to provoke a response, or to a sham substance or device that should provoke no response, or a severe exercise as in Erb's test for low serum calcium . An example of a provocation test, performed on an individual, is a skin allergy test. == See also == Blind experiment Control group == References ==


id: pseudoreplication-20260505-0
file: data/en.wikipedia.org/wiki/Pseudoreplication-0.md
category: reference
title: Pseudoreplication
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Pseudoreplication
date_saved: 2026-05-05T09:51:32.728123+00:00
instance: kb-cron
parent: pseudoreplication-20260505-0
summary:
Pseudoreplication (sometimes unit of analysis error) has many definitions. Pseudoreplication was originally defined in 1984 by Stuart H. Hurlbert as the use of inferential statistics to test for treatment effects with data from experiments where either treatments are not replicated (though samples may be) or replicates are not statistically independent. Subsequently, Millar and Anderson identified it as a special case of inadequate specification of random factors where both random and fixed factors are present. It is sometimes narrowly interpreted as an inflation of the number of samples or replicates which are not statistically independent. This definition omits the confounding of unit and treatment effects in a misspecified F-ratio. In practice, incorrect F-ratios for statistical tests of fixed effects often arise from a default F-ratio that is formed over the error rather the mixed term. Lazic defined pseudoreplication as a problem of correlated samples (e.g. from longitudinal studies) where correlation is not taken into account when computing the confidence interval for the sample mean. For the effect of serial or temporal correlation also see Markov chain central limit theorem. The problem of inadequate specification arises when treatments are assigned to units that are subsampled and the treatment F-ratio in an analysis


id: quasi-experiment-20260505-0
file: data/en.wikipedia.org/wiki/Quasi-experiment-0.md
category: reference
title: Quasi-experiment
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Quasi-experiment
date_saved: 2026-05-05T09:51:33.934268+00:00
instance: kb-cron
parent: quasi-experiment-20260505-0
summary:
A quasi-experiment is a research design used to estimate the causal impact of an intervention. This research design is aimed at assessing the difference between outcomes (e.g., reading knowledge, depressive symptoms) in a group that experienced an intervention and a group that did not. The intervention is broadly construed such that it could be designed by researchers (e.g., a reading program) or it could be an event affecting a group of people such as disaster (e.g., an earthquake). Quasi-experiments share similarities with experiments and randomized controlled trials, but specifically lack random assignment to intervention and control conditions. Instead, quasi-experimental designs typically compare groups that are either preexisting (e.g., whether someone was exposed to COVID-19) or groups that were created without random assignment (e.g., students attending schools with different reading programs). The causal analysis of quasi-experiments depends on assumptions that render non-randomness irrelevant, for example, the parallel trends assumption for difference-in-differences (DiD) method. Quasi-experiments are subject to concerns about internal validity. In other words, it may be difficult to convincingly demonstrate a causal link between the treatment and control conditions on the observed outcomes in quasi-experimental designs. This is particularly true if there are confounding variables that cannot be controlled or


id: quasi-experiment-20260505-1
file: data/en.wikipedia.org/wiki/Quasi-experiment-1.md
category: reference
title: Quasi-experiment
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Quasi-experiment
date_saved: 2026-05-05T09:51:33.934268+00:00
instance: kb-cron
parent: quasi-experiment-20260505-0
summary:
Difference in differences (pre-post with-without comparison) Event study Nonequivalent control groups design no-treatment control group designs nonequivalent dependent variables designs removed treatment group designs repeated treatment designs reversed treatment nonequivalent control groups designs cohort designs post-test only designs regression continuity designs Regression discontinuity design Case-control design time-series designs multiple time series design interrupted time series design instrumental variables Panel analysis Of all of these designs, the regression discontinuity design comes the closest to the experimental design, as the experimenter maintains control of the treatment assignment and it is known to "yield an unbiased estimate of the treatment effects". It does, however, require large numbers of study participants and precise modeling of the functional form between the assignment and the outcome variable, in order to yield the same power as a traditional experimental design. Though quasi-experiments are sometimes shunned by those who consider themselves to be experimental purists (leading Donald T. Campbell to coin the term "queasy experiments" for them), they can be useful in areas where it is not feasible or desirable to conduct an experiment or randomized control trial. Such instances include evaluating the impact of public policy changes, educational interventions or large scale health interventions. The primary drawback


id: quasi-experiment-20260505-2
file: data/en.wikipedia.org/wiki/Quasi-experiment-2.md
category: reference
title: Quasi-experiment
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Quasi-experiment
date_saved: 2026-05-05T09:51:33.934268+00:00
instance: kb-cron
parent: quasi-experiment-20260505-0
summary:
== Disadvantages == Quasi-experimental estimates of impact are subject to contamination by confounding variables. In the example above, a variation in the children's response to spanking is plausibly influenced by factors that cannot be easily measured and controlled, for example the child's intrinsic wildness or the parent's irritability. The lack of random assignment in the quasi-experimental design method may allow studies to be more feasible, but this also poses many challenges for the investigator in terms of internal validity. This deficiency in randomization makes it harder to rule out confounding variables and introduces new threats to internal validity. Because randomization is absent, some knowledge about the data can be approximated, but conclusions of causal relationships are difficult to determine due to a variety of extraneous and confounding variables that exist in a social environment. Moreover, even if these threats to internal validity are assessed, causation still cannot be fully established because the experimenter does not have total control over extraneous variables. Disadvantages also include the study groups may provide weaker evidence because of the lack of randomness. Randomness brings a lot of useful information to a study because it broadens results and therefore gives a better representation of the population


id: random-assignment-20260505-0
file: data/en.wikipedia.org/wiki/Random_assignment-0.md
category: reference
title: Random assignment
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Random_assignment
date_saved: 2026-05-05T09:51:35.110967+00:00
instance: kb-cron
parent: random-assignment-20260505-0
summary:
Random assignment or random placement is an experimental technique for assigning human participants or animal subjects to different groups in an experiment (e.g., a treatment group versus a control group) using randomization, such as by a chance procedure (e.g., flipping a coin) or a random number generator. This ensures that each participant or subject has an equal chance of being placed in any group. Random assignment of participants helps to ensure that any differences between and within the groups are not systematic at the outset of the experiment. Thus, any differences between groups recorded at the end of the experiment can be more confidently attributed to the experimental procedures or treatment. Random assignment, blinding, and controlling are key aspects of the design of experiments because they help ensure that the results are not spurious or deceptive via confounding. This is why randomized controlled trials are vital in clinical research, especially ones that can be double-blinded and placebo-controlled. Mathematically, there are distinctions between randomization, pseudorandomization, and quasirandomization, as well as between random number generators and pseudorandom number generators. How much these differences matter in experiments (such as clinical trials) is a matter of trial design and statistical rigor, which affect evidence


id: randomization-20260505-0
file: data/en.wikipedia.org/wiki/Randomization-0.md
category: reference
title: Randomization
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Randomization
date_saved: 2026-05-05T09:51:37.479553+00:00
instance: kb-cron
parent: randomization-20260505-0
summary:
Randomization is a statistical process in which a random mechanism is employed to select a sample from a population or assign subjects to different groups. The process is crucial in ensuring the random allocation of experimental units or treatment protocols, thereby minimizing selection bias and enhancing the statistical validity. It facilitates the objective comparison of treatment effects in experimental design, as it equates groups statistically by balancing both known and unknown factors at the outset of the study. In statistical terms, it underpins the principle of probabilistic equivalence among groups, allowing for the unbiased estimation of treatment effects and the generalizability of conclusions drawn from sample data to the broader population. Randomization is not haphazard; instead, a random process is a sequence of random variables describing a process whose outcomes do not follow a deterministic pattern but follow an evolution described by probability distributions. For example, a random sample of individuals from a population refers to a sample where every individual has a known probability of being sampled. This would be contrasted with nonprobability sampling, where arbitrary individuals are selected. A runs test can be used to determine whether the occurrence of a set of measured values is random. Randomization


id: randomization-20260505-1
file: data/en.wikipedia.org/wiki/Randomization-1.md
category: reference
title: Randomization
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Randomization
date_saved: 2026-05-05T09:51:37.479553+00:00
instance: kb-cron
parent: randomization-20260505-0
summary:
== Randomization in statistical analysis == Randomization is a core principle in statistical theory, whose importance was emphasized by Charles S. Peirce in "Illustrations of the Logic of Science" (1877–1878) and "A Theory of Probable Inference" (1883). Its application in statistical methodologies is multifaceted and includes critical processes such as randomized controlled experiments, survey sampling and simulations. === Randomized controlled experiment === In the realm of scientific research, particularly within clinical study designs, constraints such as limited manpower, material resources, financial backing, and time necessitate a selective approach to participant inclusion. Despite the broad spectrum of potential participants who fulfill the inclusion criteria, it is impractical to incorporate every eligible individual in the target population due to these constraints. Therefore, a representative subset of treatment groups is chosen based on the specific requirements of the research. A randomized sampling method is employed to ensure the integrity and representativeness of the study. This method ensures that all qualified subjects within the target population have an equal opportunity to be selected. Such a strategy is pivotal in mirroring the overall characteristics of the target population and in mitigating the risk of selection bias. The selected samples (or continuous non-randomly sampled samples) are


id: randomized-experiment-20260505-0
file: data/en.wikipedia.org/wiki/Randomized_experiment-0.md
category: reference
title: Randomized experiment
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Randomized_experiment
date_saved: 2026-05-05T09:51:39.917852+00:00
instance: kb-cron
parent: randomized-experiment-20260505-0
summary:
In science, randomized experiments are the experiments that allow the greatest reliability and validity of statistical estimates of treatment effects. Randomization-based inference is especially important in experimental design and in survey sampling. == Overview == In the statistical theory of design of experiments, randomization involves randomly allocating the experimental units across the treatment groups. For example, if an experiment compares a new drug against a standard drug, then the patients should be allocated to either the new drug or to the standard drug control using randomization. Randomized experimentation is not haphazard. Randomization reduces bias by equalising other factors that have not been explicitly accounted for in the experimental design (according to the law of large numbers). Randomization also produces ignorable designs, which are valuable in model-based statistical inference, especially Bayesian or likelihood-based. In the design of experiments, the simplest design for comparing treatments is the "completely randomized design". Some "restriction on randomization" can occur with blocking and experiments that have hard-to-change factors; additional restrictions on randomization can occur when a full randomization is infeasible or when it is desirable to reduce the variance of estimators of selected effects. Randomization of treatment in clinical trials pose ethical problems. In some cases,


id: regression-discontinuity-design-20260505-0
file: data/en.wikipedia.org/wiki/Regression_discontinuity_design-0.md
category: reference
title: Regression discontinuity design
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Regression_discontinuity_design
date_saved: 2026-05-05T09:51:41.067762+00:00
instance: kb-cron
parent: regression-discontinuity-design-20260505-0
summary:
Regression discontinuity designs (RDD) are a quasi-experimental pretest–posttest design that attempts to determine the causal effects of interventions by assigning a cutoff or threshold above or below which an intervention is assigned. By comparing observations lying closely on either side of the threshold, it is possible to estimate the average treatment effect in environments where random assignment to conditions is unfeasible. True causal inference using RDDs is still impossible, because the RDD cannot account for the potentially confounding effects of other variables without randomization. The RDD was originally applied by Donald Thistlethwaite and Donald Campbell (1960) to evaluate the effect of scholarship programs on student career plans. The RDD is used in disciplines like psychology, economics, political science, epidemiology, and other related disciplines. Recent comparisons of randomised controlled trials (RCTs) and RDDs have empirically demonstrated the internal validity of the design. == Example == The intuition behind the RDD is well illustrated using the evaluation of merit-based scholarships. The main problem with estimating the causal effect of such an intervention is the homogeneity of performance to the assignment of treatment (e.g., a scholarship award). Since high-performing students are more likely to be awarded the merit scholarship and continue performing well


id: regression-discontinuity-design-20260505-1
file: data/en.wikipedia.org/wiki/Regression_discontinuity_design-1.md
category: reference
title: Regression discontinuity design
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Regression_discontinuity_design
date_saved: 2026-05-05T09:51:41.067762+00:00
instance: kb-cron
parent: regression-discontinuity-design-20260505-0
summary:
== Required assumptions == Regression discontinuity design requires that all potentially relevant variables besides the treatment variable and outcome variable be continuous at the point where the treatment and outcome discontinuities occur. One sufficient, though not necessary, condition is if the treatment assignment is "as good as random" at the threshold for treatment. If this holds, then it guarantees that those who just barely received treatment are comparable to those who just barely did not receive treatment, as treatment status is effectively random. Treatment assignment at the threshold can be "as good as random" if there is randomness in the assignment variable and the agents considered (individuals, firms, etc.) cannot perfectly manipulate their treatment status. For example, suppose the treatment is passing an exam, where a grade of 50% is required. In this case, this example is a valid regression discontinuity design so long as grades are somewhat random, due either to the randomness of grading or randomness of student performance. Students must not also be able to perfectly manipulate their grade so as to determine their treatment status perfectly. Two examples include students being able to convince teachers to "mercy pass" them, or students being allowed to retake the


id: regression-discontinuity-design-20260505-2
file: data/en.wikipedia.org/wiki/Regression_discontinuity_design-2.md
category: reference
title: Regression discontinuity design
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Regression_discontinuity_design
date_saved: 2026-05-05T09:51:41.067762+00:00
instance: kb-cron
parent: regression-discontinuity-design-20260505-0
summary:
== Disadvantages == The estimated effects are only unbiased if the functional form of the relationship between the treatment and outcome is correctly modelled. The most popular caveats are non-linear relationships that are mistaken as a discontinuity. Contamination by other treatments. Suppose another treatment occurs at the same cutoff value of the same assignment variable. In that case, the measured discontinuity in the outcome variable may be partially attributed to this other treatment. For example, suppose a researcher wishes to study the impact of legal access to alcohol on mental health using a regression discontinuity design at the minimum legal drinking age. The measured impact could be confused with legal access to gambling, which may occur at the same age. == Extensions == === Fuzzy RDD === The identification of causal effects hinges on the crucial assumption that there is indeed a sharp cut-off, around which there is a discontinuity in the probability of assignment from 0 to 1. In reality, however, cutoffs are often not strictly implemented (e.g. exercised discretion for students who just fell short of passing the threshold) and the estimates will hence be biased. In contrast to the sharp regression discontinuity design, a fuzzy regression discontinuity


id: replication-statistics-20260505-0
file: data/en.wikipedia.org/wiki/Replication_(statistics)-0.md
category: reference
title: Replication (statistics)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Replication_(statistics)
date_saved: 2026-05-05T09:51:43.532403+00:00
instance: kb-cron
parent: replication-statistics-20260505-0
summary:
In engineering, science, and statistics, replication is the process of repeating a study or experiment under the same or similar conditions. It is a crucial step to test the original claim and confirm or reject the accuracy of results as well as for identifying and correcting the flaws in the original experiment. ASTM, in standard E1847, defines replication as "... the repetition of the set of all the treatment combinations to be compared in an experiment. Each of the repetitions is called a replicate." For a full factorial design, replicates are multiple experimental runs with the same factor levels. You can replicate combinations of factor levels, groups of factor level combinations, or even entire designs. For instance, consider a scenario with three factors, each having two levels, and an experiment that tests every possible combination of these levels (a full factorial design). One complete replication of this design would comprise 8 runs ( 2 3 {\displaystyle 2^{3}} ). The design can be executed once or with several replicates. There are two main types of replication in statistics. First, there is a type called “exact replication” (also called "direct replication"), which involves repeating the study as closely as possible to the


id: resentful-demoralization-20260505-0
file: data/en.wikipedia.org/wiki/Resentful_demoralization-0.md
category: reference
title: Resentful demoralization
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Resentful_demoralization
date_saved: 2026-05-05T09:51:44.708773+00:00
instance: kb-cron
parent: resentful-demoralization-20260505-0
summary:
Resentful demoralization is an issue in controlled experiments in which those in the control group become resentful of not receiving the experimental treatment. Alternatively, the experimental group could be resentful of the control group, if the experimental group perceive its treatment as inferior. They may become angry, depressed, uncooperative, or non-compliant. This may lead to significant systematic differences in the outcome of the control group, obscuring the results of the study and threatening their validity. Resentful demoralization can also make participants less likely to join future studies. If people feel treated unfairly or get upset with their treatment, they might not want to be part of research again. This can lead to fewer participants and affect the quality of future studies. So, it's important to address these feelings to keep participants engaged and ensure good research outcomes. == See also == Clinical trial Randomized controlled trial == External links == Resentful Demoralization in Encyclopedia of Statistics in Behavioral Science. (subscription required)


id: response-surface-methodology-20260505-0
file: data/en.wikipedia.org/wiki/Response_surface_methodology-0.md
category: reference
title: Response surface methodology
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Response_surface_methodology
date_saved: 2026-05-05T09:51:45.870436+00:00
instance: kb-cron
parent: response-surface-methodology-20260505-0
summary:
In statistics, response surface methodology (RSM) explores the relationships between several explanatory variables and one or more response variables. RSM is an empirical model which employs the use of mathematical and statistical techniques to relate input variables, otherwise known as factors, to the response. RSM became very useful because other methods available, such as the theoretical model, could be very cumbersome to use, time-consuming, inefficient, error-prone, and unreliable. The method was introduced by George E. P. Box and K. B. Wilson in 1951. The main idea of RSM is to use a sequence of designed experiments to obtain an optimal response. Box and Wilson suggest using a second-degree polynomial model to do this. They acknowledge that this model is only an approximation, but they use it because such a model is easy to estimate and apply, even when little is known about the process. Statistical approaches such as RSM can be employed to maximize the production of a special substance by optimization of operational factors. Of late, for formulation optimization, the RSM, using proper design of experiments (DoE), has become extensively used. In contrast to conventional methods, the interaction among process variables can be determined by statistical techniques. == Basic


id: restricted-randomization-20260505-0
file: data/en.wikipedia.org/wiki/Restricted_randomization-0.md
category: reference
title: Restricted randomization
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Restricted_randomization
date_saved: 2026-05-05T09:51:47.072793+00:00
instance: kb-cron
parent: restricted-randomization-20260505-0
summary:
In statistics, restricted randomization occurs in the design of experiments and in particular in the context of randomized experiments and randomized controlled trials. Restricted randomization allows intuitively poor allocations of treatments to experimental units to be avoided, while retaining the theoretical benefits of randomization. For example, in a clinical trial of a new proposed treatment of obesity compared to a control, an experimenter would want to avoid outcomes of the randomization in which the new treatment was allocated only to the heaviest patients. The concept was introduced by Frank Yates (1948) and William J. Youden (1972) "as a way of avoiding bad spatial patterns of treatments in designed experiments." == Example of nested data == Consider a batch process that uses 7 monitor wafers in each run. The plan further calls for measuring a response variable on each wafer at each of 9 sites. The organization of the sampling plan has a hierarchical or nested structure: the batch run is the topmost level, the second level is an individual wafer, and the third level is the site on the wafer. The total amount of data generated per batch run will be 7 · 9 = 63 observations. One approach to


id: restricted-randomization-20260505-1
file: data/en.wikipedia.org/wiki/Restricted_randomization-1.md
category: reference
title: Restricted randomization
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Restricted_randomization
date_saved: 2026-05-05T09:51:47.072793+00:00
instance: kb-cron
parent: restricted-randomization-20260505-0
summary:
==== Example: some factors hard to vary ==== Consider running the experiment under the first condition listed above, with the factor solution concentration of the plating agent (S) being hard to vary. Since this factor is hard to vary, the experimenter would like to randomize the treatment combinations so that the solution concentration factor has a minimal number of changes. In other words, the randomization of the treatment runs is restricted somewhat by the level of the solution concentration factor. As a result, the treatment combinations might be randomized such that those treatment runs corresponding to one level of the concentration (−1) are run first. Each copper strip is individually plated, meaning only one strip at a time is placed in the solution for a given treatment combination. Once the four runs at the low level of solution concentration have been completed, the solution is changed to the high level of concentration (1), and the remaining four runs of the experiment are performed (where again, each strip is individually plated). Once one complete replicate of the experiment has been completed, a second replicate is performed with a set of four copper strips processed for a given level of solution concentration


id: restricted-randomization-20260505-2
file: data/en.wikipedia.org/wiki/Restricted_randomization-2.md
category: reference
title: Restricted randomization
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Restricted_randomization
date_saved: 2026-05-05T09:51:47.072793+00:00
instance: kb-cron
parent: restricted-randomization-20260505-0
summary:
==== Example: experimental units processed individually ==== Consider running the experiment under the third scenario listed above. There is only one copper strip in the solution at one time. However, two strips, one at the low current and one at the high current, are processed one right after the other under the same temperature and concentration setting. Once two strips have been processed, the concentration is changed and the temperature is reset to another combination. Two strips are again processed, one after the other, under this temperature and concentration setting. This process is continued until all 16 copper strips have been processed. Running the experiment in this way also results in a split-plot design in which the whole-plot factors are again solution concentration and solution temperature and the subplot factor is current. In this experiment, one size experimental unit is an individual copper strip. The treatment or factor that was applied to the individual strips is current (this factor was changed each time for a different strip within the solution). The other or larger-size experimental unit is a set of two copper strips. The treatments or factors that were applied to a pair of two strips are solution concentration and


id: restricted-randomization-20260505-3
file: data/en.wikipedia.org/wiki/Restricted_randomization-3.md
category: reference
title: Restricted randomization
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Restricted_randomization
date_saved: 2026-05-05T09:51:47.072793+00:00
instance: kb-cron
parent: restricted-randomization-20260505-0
summary:
=== Strip-plot example: two steps and three factor variables === Consider the following example from the semiconductor industry. An experiment requires an implant step and an anneal step. At both the anneal and the implant steps there are three factors to test. The implant process accommodates 12 wafers in a batch, and implanting a single wafer under a specified set of conditions is not practical nor does doing so represent economical use of the implanter. The anneal furnace can handle up to 100 wafers. The settings for a two-level factorial design for the three factors in the implant step are denoted (A, B, C), and a two-level factorial design for the three factors in the anneal step are denoted (D, E, F). Also present are interaction effects between the implant factors and the anneal factors. Therefore, this experiment contains three sizes of experimental units, each of which has a unique error term for estimating the significance of effects. To put actual physical meaning to each of the experimental units in the above example, consider each combination of implant and anneal steps as an individual wafer. A batch of eight wafers goes through the implant step first. Treatment combination 3 in


id: riffle-splitter-20260505-0
file: data/en.wikipedia.org/wiki/Riffle_splitter-0.md
category: reference
title: Riffle splitter
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Riffle_splitter
date_saved: 2026-05-05T09:51:48.272622+00:00
instance: kb-cron
parent: riffle-splitter-20260505-0
summary:
The riffle splitter is a device used to divide a bulk sample of material into smaller, representative sub-samples. It can be used in laboratory settings or fieldwork. The device is usually constructed with steel sheet and should be designed to have an even number of opposing inclined chutes (the riffles), with each chute having the same width. The recommended chute width should be at least 2.5× the size of the maximum particle diameter that can be found in the lot to be split. Riffle splitters are typically used in assay and analytical laboratories to reduce the size of samples provided from other sources (crushed rock, soils, powders and so on) to a lot size that is appropriate for the next stage of analytical sample preparation. == Design == There are many different versions of the riffle splitter. However, not all can be considered correct sub-sampling devices, in that the two sub-sample halves are deemed to be representative of the original lot. The issue of correctness of a riffles split sub-sample are function of both the design and the use of the splitter. The design key items are: The splitter should have an even number of riffles so the two sub


id: round-robin-test-20260505-0
file: data/en.wikipedia.org/wiki/Round-robin_test-0.md
category: reference
title: Round-robin test
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Round-robin_test
date_saved: 2026-05-05T09:51:49.467773+00:00
instance: kb-cron
parent: round-robin-test-20260505-0
summary:
In experimental methodology, a round-robin test is an interlaboratory test (measurement, analysis, or experiment) performed independently several times. This can involve multiple independent scientists performing the test with the use of the same method in different equipment, or a variety of methods and equipment. In reality it is often a combination of the two, for example if a sample is analysed, or one (or more) of its properties is measured by different laboratories using different methods, or even just by different units of equipment of identical construction. A round-robin program is a measurement systems analysis technique which uses analysis of variance (ANOVA) random effects model to assess a measurement system. == Round-robin tests regarding occupational safety and health == Companies are obliged to determine whether hazardous substances are in the air at the workplace by using appropriate measurements. To maintain the quality parameters for analytic procedures, the quality assurance methods are to be applied that are state-of-the-art. The Institute for Occupational Safety and Health of the German Social Accident Insurance (IFA) provides proficiency testing as support and assistance for the measuring stations worldwide for the purpose of self-examination and for the external presentation of quality standards. The PT schemes are


id: royal-commission-on-animal-magnetism-20260505-0
file: data/en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism-0.md
category: reference
title: Royal Commission on Animal Magnetism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism
date_saved: 2026-05-05T09:51:50.817218+00:00
instance: kb-cron
parent: royal-commission-on-animal-magnetism-20260505-0
summary:
The Royal Commission on Animal Magnetism involved two entirely separate and independent French Royal Commissions, each appointed by Louis XVI in 1784, that were conducted simultaneously by a committee composed of four physicians from the Paris Faculty of Medicine (Faculté de médecine de Paris) and five scientists from the Royal Academy of Sciences (Académie des sciences) (the "Franklin Commission", named for Benjamin Franklin), and a second committee composed of five physicians from the Royal Society of Medicine (Société Royale de Médecine) (the "Society Commission"). Each Commission took five months to complete its investigations. The "Franklin" Report was presented to the King on 11 August 1784 – and was immediately published and very widely circulated throughout France and neighbouring countries – and the "Society" Report was presented to the King five days later on 16 August 1784. The "Franklin Commission's" investigations are notable as a very early "classic" example of a systematic controlled trial, which not only applied "sham" and "genuine" procedures to patients with "sham" and "genuine" disorders, but, significantly, was the first to use the "blindfolding" of both the investigators and their subjects. The report of the ["Franklin"] Royal Commission of 1784 ... is a masterpiece of its genre,


id: royal-commission-on-animal-magnetism-20260505-1
file: data/en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism-1.md
category: reference
title: Royal Commission on Animal Magnetism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism
date_saved: 2026-05-05T09:51:50.817218+00:00
instance: kb-cron
parent: royal-commission-on-animal-magnetism-20260505-0
summary:
From the perspective of a given phenomenon, one can look down to the entities and activities composing it. One can look up to the higher-level mechanisms of which it is a component. One can look back to the mechanisms that come before it or by which it developed. One can look forward to what comes after it. [And, finally] one can look around to see the wider context with which it operates. === Tensions within the royal family === Prior to his arrival in Paris in 1777 – with a letter of recommendation from Chancellor von Kaunitz of the Habsburg monarchy to the Austrian Ambassador to France, the Comte de Mercy-Argenteau (who, in turn, introduced Mesmer to Jean-Baptiste Le Roy (1720–1800), the Director of the Academy of Sciences) – Mesmer was already known to Marie Antoinette. At the urging of her two closest friends, Marie-Paule Angélique d’Albert de Luynes (1744–1781), "the Duchesse de Chaulnes" and Marie Thérèse Louise de Savoie-Carignan (1749–1792), "the Princess of Lamballe", both of whom "had benefited from Mesmer's treatment", Marie Antoinette had been able to arrange for both Mesmer and d'Eslon to be officially "interviewed" by (an otherwise unidentified) representative of the King on 14 March


id: royal-commission-on-animal-magnetism-20260505-2
file: data/en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism-2.md
category: reference
title: Royal Commission on Animal Magnetism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism
date_saved: 2026-05-05T09:51:50.817218+00:00
instance: kb-cron
parent: royal-commission-on-animal-magnetism-20260505-0
summary:
=== Scientific issues === In a prevailing atmosphere of "[an overall] redefinition of frontiers in the legitimacy of knowledge" – and, in relation to Mesmer's claims, a redefinition "which did not necessarily match the public popularity that they attracted" (Zanetti, 2018), p. 59) – the issue of the existence (or not) of a substantial "magnetic fluid" and/or "animal magnetism" required resolution. === Medical issues === At a time when, in relation to "healers and healing", the conglomerate of "physicians, empirics, surgeons, apothecaries, folk healers, and religious personalities all vied with each other (as well as worked together) for medical legitimacy and patients" (Broomhall, 2004, p. 5), Mesmer was not only a "foreign national", but also one that had no affiliation of any kind with any known professional medical association within France (or elsewhere in Europe); and, as a consequence, his professional conduct, his medical practice, his medico-commercial enterprises, and his therapeutic endeavours were not regulated in any way. Moreover, the efficacy of Mesmer's interventions had never been objectively tested, neither the agency nor the (pre- and post-intervention) veracity of his supposed "cures" had ever been objectively verified, and, finally, in relation to the presenting conditions of those with (supposedly) 'real'


id: royal-commission-on-animal-magnetism-20260505-3
file: data/en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism-3.md
category: reference
title: Royal Commission on Animal Magnetism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism
date_saved: 2026-05-05T09:51:50.817218+00:00
instance: kb-cron
parent: royal-commission-on-animal-magnetism-20260505-0
summary:
The second of the two Royal Commissions, usually referred to as the "Society Commission", was appointed on 5 April 1784. It was composed of five eminent physicians from the Royal Society of Medicine – the physician and one of the first members of the Royal Society, Charles-Louis-François Andry (1741–1829), the physician Claude-Antoine Caille (b. 1743), the botanist Antoine Laurent de Jussieu (1748–1836), the physician, Collège de France professor, one of the original directors of the Royal Society, and committed advocate of the therapeutic applications of electricity, Pierre Jean Claude Mauduyt de La Varenne (1732–1792), and the physician and Professor of chemistry in the Collège de France, Pierre-Isaac Poissonnier (1720–1798) – and, as Pattie remarks (1994, p. 156), "the impression given by [their] report is that the commissioners were busy practitioners who wanted to devote no more time to the project than was necessary". Although the investigations of the "Society Commission" were less thorough and less detailed than those of the "Franklin Commission" they were essentially of the same nature, and it is a matter of fact that neither Commission examined Mesmer's practices – they only examined the practices of d'Eslon. == Franz Mesmer == Franz Anton Mesmer (1734–1815), born in


id: royal-commission-on-animal-magnetism-20260505-4
file: data/en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism-4.md
category: reference
title: Royal Commission on Animal Magnetism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism
date_saved: 2026-05-05T09:51:50.817218+00:00
instance: kb-cron
parent: royal-commission-on-animal-magnetism-20260505-0
summary:
=== A concept that must not be reified === It is clear from his Mémoire (1799) that Mesmer was very aware of the human propensity – in the normal, conventional use of language (la langue de convention) – to speak of "properties" or "qualities" (i.e., these "metaphysical abstractions", illusions de la méthaphysique), as if they were "substances": in Mesmer's words, "substantivise the properties", substantisia les propriétés (Mesmer 1799, pp. 15–17). – in other words, "reification", in the manner of Whitehead's "fallacy of misplaced concreteness". Mesmer was also well aware of the extent to which, through the "distortion" caused by these "substantive words" (mot substantif) – which inappropriately "personified" (personnifia) these metaphysical abstractions (p. 16) – one is induced to believe in the actual physical existence of the "substance" itself. Given these observations, Mesmer was most emphatic in his continuous warnings that his abstract "principles" should not be "substantivised". It is significant that Mesmer (1799) describes how, once he had formulated the abstract, overarching (metaphorical) construct/concept of "animal magnetism" as the therapeutic agent (a quarter of a century earlier) – and with his hope that this newly described "principle of action" (principe de action), when considered as an agent, "could become


id: royal-commission-on-animal-magnetism-20260505-5
file: data/en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism-5.md
category: reference
title: Royal Commission on Animal Magnetism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism
date_saved: 2026-05-05T09:51:50.817218+00:00
instance: kb-cron
parent: royal-commission-on-animal-magnetism-20260505-0
summary:
=== Post-Mesmer === In late 1782, and eighteen months before the Royal Commission, d'Eslon had (acrimoniously) parted ways with Mesmer; and, despite a brief reconciliation, the relationship was terminated in late 1783. On 28 December 1783, d'Eslon wrote a letter to the Journal de Paris, which not only described the difficulties he had experienced with Mesmer, but also announced that he was opening his own (entirely independent) clinic. Following his break with Mesmer, d'Eslon not only launched his own clinical operation on his break with Mesmer, d'Eslon took all of the patients he had brought to Mesmer with him – but also began teaching his own theories and practices (i.e., rather than those of Mesmer). According to d'Eslon's own account (d'Eslon, 1784b, pp. 25–26), Mesmer had taught 300 students, 160 of whom were medical men (médecins), and d'Eslon himself had taught 160 medical men (this group included 21 members of the Paris Faculty of Medicine). Given that many of those who had privately paid Mesmer for the details of "the secret" were greatly dissatisfied, and had "[justifiably] accused [Mesmer] of having enunciated a theory which was merely a collection of obscure principles" (Binet & Féré, 1888, p. 13), it seems


id: royal-commission-on-animal-magnetism-20260505-6
file: data/en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism-6.md
category: reference
title: Royal Commission on Animal Magnetism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism
date_saved: 2026-05-05T09:51:50.817218+00:00
instance: kb-cron
parent: royal-commission-on-animal-magnetism-20260505-0
summary:
[The baquet] is a vat about six to seven feet, more or less, in diameter by eighteen inches in height. There is a double bottom in the interior of this vat, in which fragments of broken bottles, gravel, stones, and sticks of pounded sulfur and iron filings are placed. All of this is filled with water and covered up with a floor nailed into the vat. On the surface of the lid, six inches in from the rim, one makes various holes in order to allow the passage of iron rods which are arranged so that one end penetrates the bottom of the vat and the other is directed, by means of a curve, over the pit of the stomach of the patient or other affected parts of the body. Mesmer specifically stressed the primary importance of the patients' hand-holding as a factor in the "augmentation" of the force/quality of the power of the "animal magnetism". Moreover, and significantly, Mesmer (separately) acknowledged that, if it was ever to come to pass that he had a suitable "establishment" – i.e., one with sufficient space available for all the assembled patients to hold hands – he would "abolish the use of baquets"


id: royal-commission-on-animal-magnetism-20260505-7
file: data/en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism-7.md
category: reference
title: Royal Commission on Animal Magnetism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism
date_saved: 2026-05-05T09:51:50.817218+00:00
instance: kb-cron
parent: royal-commission-on-animal-magnetism-20260505-0
summary:
In his response to the Reports (d'Eslon, 1784b, pp. 21–22), d'Eslon complained that the Commissioners' emphasis on "convulsions" was not justified: among those who received group treatment during the commission's investigations (i.e., involving "50 to 60 individuals"), he wrote, there were never more than six or seven who displayed "convulsions" to any degree – and, further, of the more than 500 patients he had treated over the preceding three years, only 20 of those had manifested "convulsions" (and almost all of those had been suffering from "convulsions" before presenting for any treatment from d'Eslon). He also rejected the suggestions of any connection between the "convulsions" of epilepsy and those of the "crises", citing the cases of two of his patients, who were epileptic and "frequently had seizures at home", who never had a single "attack" during their treatment at his clinic (p. 23). Joseph Michel Antoine Servan, the one time Advocate-General to the Parlement of Grenoble, who reported (at Servan, 1784, p. 3) that, in "the Provinces" – where the various social classes were not kept apart around "the baquet", as they were in Paris – that, in relation to the concerns that the Commissioners expressed in relation to the


id: royal-commission-on-animal-magnetism-20260505-8
file: data/en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism-8.md
category: reference
title: Royal Commission on Animal Magnetism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism
date_saved: 2026-05-05T09:51:50.817218+00:00
instance: kb-cron
parent: royal-commission-on-animal-magnetism-20260505-0
summary:
=== The substantial existence of "animal magnetism" and "magnetic fluid" were investigated === Rather than being concerned with the applications, utility, and clinical efficacy of d'Eslon's "animal magnetism", the primary concern of each Commission was the significant, crucial, and exclusive question of whether or not d'Eslon's (supposed) "animal magnetic fluid" actually existed in some substantial physical way – for the simple reason that, as the two sets of Commissioners each noted in their independent reports, "Animal magnetism may well exist without being useful but it cannot be useful if it does not exist." === Mesmer's earlier refusal to have his "magnetic" interventions scrutinized === Already, in his earlier (18 September 1780) interaction with the Paris Faculty of Medicine, Mesmer had refused to have his therapeutic interventions on a set of entirely "new" patients directly scrutinized, claiming that his already-achieved "cures" were an objective matter of record. Mesmer justified his refusal as follows: Here is what I said to M. de Lassonne; however bizarre [it may seem] at first sight it is nevertheless entirely serious and very much applicable to the question. When a thief is convicted of theft he is hanged: when a murderer is convicted of murder he is


id: royal-commission-on-animal-magnetism-20260505-9
file: data/en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism-9.md
category: reference
title: Royal Commission on Animal Magnetism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism
date_saved: 2026-05-05T09:51:50.817218+00:00
instance: kb-cron
parent: royal-commission-on-animal-magnetism-20260505-0
summary:
it was [their] duty ... to confine themselves to arguments purely physical, that is, to the momentaneous [sic] effects of the fluid upon the animal frame, excluding from these effects all the illusions which might mix with them, and assuring themselves that they could proceed from no other cause than the animal magnetism." === Problems with objectively determining the precise therapeutic action of any supposed "efficacious remedy" === In support of its decision, the "Franklin Commission" produced a cogent, extended argument, consistent with the medical knowledge of the day, that is equally relevant to similar investigations in the present day: The majority of diseases have their seat in the interior part of our frame. The collective experience of a great number of centuries has made us acquainted with the symptoms, which indicate and discriminate them; the same experience has taught the method in which they are to be treated. What is the object of the efforts of the physician in this method? It is not to oppose and to subdue nature, it is to assist her in her operations. Nature, says the father of the medical science [viz., Hippocrates], cures the diseased; but sometimes she encounters obstacles, which constrain her


id: royal-commission-on-animal-magnetism-20260505-10
file: data/en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism-10.md
category: reference
title: Royal Commission on Animal Magnetism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism
date_saved: 2026-05-05T09:51:50.817218+00:00
instance: kb-cron
parent: royal-commission-on-animal-magnetism-20260505-0
summary:
They saw in the centre of a large apartment a circular box, made of oak, and about a foot or a foot and an [sic] half deep, which is called the bucket [baquet]; the lid of this box is pierced with a number of holes, in which are inserted branches of iron, elbowed and moveable. The patients are arranged in ranks about this [baquet], and each has his branch of iron, which by means of the elbow may be applied immediately to the part affected; a cord passed round their bodies connects them one with the other: sometimes a second means of communication is introduced, by the insertion of the thumb of each patient between the forefinger and thumb of the patient next him; the thumb thus inserted is pressed by the person holding it; the impression received by the left hand of the patient, communicates through his right, and thus passes through the whole circle.A piano forté is placed in one corner of the apartment, and different airs are played with various degrees of rapidity; vocal music is sometimes added to the instrumental.The persons who superintend the process, have each of them an iron rod in his hand, from


id: royal-commission-on-animal-magnetism-20260505-11
file: data/en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism-11.md
category: reference
title: Royal Commission on Animal Magnetism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism
date_saved: 2026-05-05T09:51:50.817218+00:00
instance: kb-cron
parent: royal-commission-on-animal-magnetism-20260505-0
summary:
==== Immediate publication and dissemination ==== The report was immediately published by the government printer; and at least 20,000 copies were rapidly and very widely circulated throughout France and neighbouring countries. Within four months (16 December 1784), the London publishing house of Joseph Johnson was announcing the publication of a complete English version, translated by William Godwin (i.e., Godwin, 1785), and, in between February and July 1785, four different "periodical abridgements of the Franklin report, each printed multiple times in the Atlantic coast publications" were published in the United States (Ogden, 2012, p. 167); and, in 1837, Godwin's complete translation was published, in Philadelphia, as part of a collected work. ==== Touch, imagination, and imitation ==== Clearly "recogniz[ing] that publicly endorsing the curative effects of a technique that had no demonstrable basis in the science of the late 18th century could lead to a proliferation of medical quackery" (McConkey & Perry, 2002, p. 328) and, based on their own "experiments" and "observations", the Commissioners concluded that "the true causes of the effects attributed to this new agent known by the name of animal magnetism, [and] to this fluid which is said to circulate in the body and to communicate itself


id: royal-commission-on-animal-magnetism-20260505-12
file: data/en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism-12.md
category: reference
title: Royal Commission on Animal Magnetism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism
date_saved: 2026-05-05T09:51:50.817218+00:00
instance: kb-cron
parent: royal-commission-on-animal-magnetism-20260505-0
summary:
On 4 September 1784, Bailly presented a third, brief (15 page) courtesy report to the Royal Academy of Sciences (Bailly, 1784b) on behalf of himself, Franklin, Le Roy, de Bory, and Lavoisier (i.e., those Commissioners who were also Academy members), which provided their Academy colleagues with a brief account of the commission's proceedings, the rationale behind its investigations, and the results. Noting that all of their investigations were jointly conducted with the four members of the Paris Faculty, and that all nine shared the same "interest in [discovering] the truth", they stressed that all the findings of their combined efforts were unanimous (p. 2). ==== The importance of "the Sciences" ==== Further (p. 4), given that the understanding of the Sciences – "which [collectively] are increased by [establishing] the truth" (qui s'accroissent par les vérités) – is increased by "the suppression of error": i.e., given that "error" is always "a bad leaven that ferments and, in the long run, corrupts the mass into which it has been introduced". By contrast, however, in those cases wherein the "error" has been generated by "The Empire of Science", and has spread to "the multitude" – not only to divide and agitate minds, but


id: royal-commission-on-animal-magnetism-20260505-13
file: data/en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism-13.md
category: reference
title: Royal Commission on Animal Magnetism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism
date_saved: 2026-05-05T09:51:50.817218+00:00
instance: kb-cron
parent: royal-commission-on-animal-magnetism-20260505-0
summary:
Part One (pp. 2–21), discussing the theories of the practices known as "Animal Magnetism". It commences with d'Eslon's definition of "animal magnetism"; namely that it is "the action which one man exercises on another, either through immediate contact or at a certain distance by the mere pointing of a finger or any kind of conduct", and that "this action", according to d'Eslon, "is the effect of a fluid that is distributed throughout the universe" Part Two (pp. 22–37), discussing the procedures and practices of "Animal Magnetism", as well as addressing the issues of their therapeutic efficacy (or not), and those of whether (or not) the procedures/practices should be admitted to conventional medical practice ("doivent-ils être admis en Médecine!": p. 22). The conclusions drawn (pp. 37–39) were, in brief, that they had found no evidence of d'Eslon's "magnetic fluid", that there were "no grounds for any belief in animal magnetism", that "the effects attributed to it are due to known causes", including not only the influence of "contact", "imagination", and/or "imitation", but also the influence of "the environment of the treatment room with its closed windows, fetid air, dim light, and the sight of other patients [and their responses to their


id: royal-commission-on-animal-magnetism-20260505-14
file: data/en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism-14.md
category: reference
title: Royal Commission on Animal Magnetism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism
date_saved: 2026-05-05T09:51:50.817218+00:00
instance: kb-cron
parent: royal-commission-on-animal-magnetism-20260505-0
summary:
=== Response of the Paris Faculty of Medicine === Immediately following the release of the reports of the two Commissions, the Paris Faculty of Medicine "pressure[d] its own members to renounce animal magnetism" (Crabtree, 1993, p. 32). The Faculty identified some thirty of its docteurs-régent, including François Louis Thomas d'Onglée and Charles-Louis Varnier, who "openly favored animal magnetism or were suspected of so doing". According to the contemporary account of Thomas d'Onglée (1785, passim), the thirty "magnetic physicians" were subjected to "abuse" and were presented with a declaration, of which it was demanded that they sign. Both Thomas d'Onglée and Varnier, among others, refused to sign the declaration (and were, thereby, immediately expelled). The declaration in question read: No Doctor may declare himself a partisan of animal magnetism, through writings or through practice, under penalty of being removed from the role of docteurs-régents. === D'Eslon's response === D'Eslon immediately published an attack on the commission's reports, in which he criticized their failure to investigate the longer-term effects of his treatments, and their refusal to accept his (alleged) "cures" as proof of the existence of "animal magnetism", as well as noting that, "the commissioners' recommendation that the practice of magnetism should


id: royal-commission-on-animal-magnetism-20260505-15
file: data/en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism-15.md
category: reference
title: Royal Commission on Animal Magnetism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism
date_saved: 2026-05-05T09:51:50.817218+00:00
instance: kb-cron
parent: royal-commission-on-animal-magnetism-20260505-0
summary:
The "classic" structure of the investigations undertaken by the "Franklin" Commission inspired – among many others over the ensuing years – the (1799) investigations of Chester physician John Haygarth (1740–1827) into the efficacy of Perkins' "tractors". In the process of discussing the experiments he had conducted (with medical colleagues as witnesses) with (dummy) "wooden tractors" on 7 January 1799, and with Perkins' "true metallick tractors" on 8 January 1799, Haygarth emphasized his considerable debt to the (earlier) "Franklin Commission" enterprise: It need not be remarked, how completely the trial illustrates the nature of this popular illusion, which has so wonderfully prevailed, and spread so rapidly; it resembles, in a striking manner, that of Animal Magnetism, which merited the attention of Dr. Franklin, when ambassador from America, and of other philosophers at Paris. If any person would repeat these experiments, they should be performed with due solemnity. During the process, the wonderful cures which this remedy is said to have performed ought to be particularly related. Without these indispensable aids, other trials will not prove as successful as those which are above reported. The whole effect undoubtedly depends upon the impression which can be made upon the patient's Imagination. This method


id: royal-commission-on-animal-magnetism-20260505-16
file: data/en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism-16.md
category: reference
title: Royal Commission on Animal Magnetism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Royal_Commission_on_Animal_Magnetism
date_saved: 2026-05-05T09:51:50.817218+00:00
instance: kb-cron
parent: royal-commission-on-animal-magnetism-20260505-0
summary:
The noted neurologist and psychoanalyst Sigmund Freud not only studied and wrote about "hypnosis" (e.g., Freud, 1891, and 1966), but he also actively used "hypnosis" in his clinical practice for some time. However the (à la Salpêtrière) "hypnosis" that Freud employed – quite unlike the conventional "hypnotism" of James Braid (that was induced by Braid's standard "upwards and inwards squint") – relied upon an induction process that often involved rubbing the top of a patient's head. This requirement, of course, demanded that Freud sat at the end of the therapeutic couch – in order to gain easy access to his subject's head – a practice that Freud continued to follow for his entire (post-"hypnosis") professional career. Another vestige of phreno-magnetism that demanded that Freud position himself at the patient's head was Freud's application of the "head pressure" technique that he had, in person, observed Hippolyte Bernheim use, on one of his visits to Bernheim's clinic at Nancy in 1899. Freud had discontinued this "head pressure" practice by, at least, 1904 – and, possibly, by 1900. === Mistaken identification of Esdaile's Jhar-Phoonk with d’Eslon's magnetization-by-contact === Due, to a large extent, to the (mistaken) enthusiastic promotion of Esdaile's (otherwise) valuable work


id: sample-ratio-mismatch-20260505-0
file: data/en.wikipedia.org/wiki/Sample_ratio_mismatch-0.md
category: reference
title: Sample ratio mismatch
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Sample_ratio_mismatch
date_saved: 2026-05-05T09:51:52.013933+00:00
instance: kb-cron
parent: sample-ratio-mismatch-20260505-0
summary:
In the design of experiments, a sample ratio mismatch (SRM) is a statistically significant difference between the expected and actual ratios of the sizes of treatment and control groups in an experiment. Sample ratio mismatches also known as unbalanced sampling often occur in online controlled experiments due to failures in randomization and instrumentation. Sample ratio mismatches can be detected using a chi-squared test. Using methods to detect SRM can help non-experts avoid making decisions using biased data. If the sample size is large enough, even a small discrepancy between the observed and expected group sizes can invalidate the results of an experiment. == Example == Suppose we run an A/B test in which we randomly assign 1000 users to equally sized treatment and control groups (a 50–50 split). The expected size of each group is 500. However, the actual sizes of the treatment and control groups are 600 and 400. Using Pearson's chi-squared goodness of fit test, we find a sample ratio mismatch with a p-value of 2.54×10−10. In other words, if the assignment of users were truly random, the probability that these treatment and control group sizes would occur by chance is 2.54×10−10. == References ==


id: saturated-array-20260505-0
file: data/en.wikipedia.org/wiki/Saturated_array-0.md
category: reference
title: Saturated array
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Saturated_array
date_saved: 2026-05-05T09:51:53.168008+00:00
instance: kb-cron
parent: saturated-array-20260505-0
summary:
In experiments in which additional factors are not likely to interact with any of the other factors, a saturated array can be used. In a saturated array, a controllable factor is substituted for the interaction of two or more by-products. Using a saturated array, a two-factor test matrix could be used to test three factors. Using the saturated array allows three factors to be tested in four tests rather than in eight, as would be required by a standard orthogonal array.


id: scheirer-ray-hare-test-20260505-0
file: data/en.wikipedia.org/wiki/Scheirer–Ray–Hare_test-0.md
category: reference
title: Scheirer–Ray–Hare test
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scheirer–Ray–Hare_test
date_saved: 2026-05-05T09:51:54.386214+00:00
instance: kb-cron
parent: scheirer-ray-hare-test-20260505-0
summary:
The Scheirer–Ray–Hare (SRH) test is a statistical test that can be used to examine whether a measure is affected by two or more factors. Since it does not require a normal distribution of the data, it is one of the non-parametric methods. It is an extension of the Kruskal–Wallis test, the non-parametric equivalent for one-way analysis of variance (ANOVA), to the application for more than one factor. It is thus a non-parametric alternative to multi-factorial ANOVA analyses. The test is named after James Scheirer, William Ray and Nathan Hare, who published it in 1976. == Test description == The Scheirer–Ray–Hare test is analogous to the parametric multi-factorial ANOVA of investigating the influence of two different factors on a measure for which different samples are available for the factors. As with the parametric analysis of variance, the test can be used to investigate the null hypotheses that the two factors examined in each case have no influence on the positional parameter of the samples and thus on the measure, and that there are no interactions between the two factors. A p-value less than 0.05 for one or more of these three hypotheses leads to their rejection. As with many other non-parametric


id: scientific-protocol-20260505-0
file: data/en.wikipedia.org/wiki/Scientific_protocol-0.md
category: reference
title: Scientific protocol
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scientific_protocol
date_saved: 2026-05-05T09:51:55.594780+00:00
instance: kb-cron
parent: scientific-protocol-20260505-0
summary:
In natural and social science research, a protocol is most commonly a predefined procedural method in the design and implementation of an experiment. Protocols are written whenever it is desirable to standardize a laboratory method to ensure successful replication of results by others in the same laboratory or by other laboratories. Additionally, and by extension, protocols have the advantage of facilitating the assessment of experimental results through peer review. In addition to detailed procedures, equipment, and instruments, protocols will also contain study objectives, reasoning for experimental design, reasoning for chosen sample sizes, safety precautions, and how results were calculated and reported, including statistical analysis and any rules for predefining and documenting excluded data to avoid bias. Similarly, a protocol may refer to the procedural methods of health organizations, commercial laboratories, manufacturing plants, etc. to ensure their activities (e.g., blood testing at a hospital, testing of certified reference materials at a calibration laboratory, and manufacturing of transmission gears at a facility) are consistent to a specific standard, encouraging safe use and accurate results. Finally, in the field of social science, a protocol may also refer to a "descriptive record" of observed events or a "sequence of behavior" of one or more


id: sealedenvelope-com-20260505-0
file: data/en.wikipedia.org/wiki/Sealedenvelope.com-0.md
category: reference
title: Sealedenvelope.com
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Sealedenvelope.com
date_saved: 2026-05-05T09:51:56.773434+00:00
instance: kb-cron
parent: sealedenvelope-com-20260505-0
summary:
Sealedenvelope.com is British collaboration that provides support services for clinical trials. They provide services such as randomization, allocation concealment, code-break services, and case report management through a web-based design. They also perform certain calculations such as power calculations. == References == == External links == Official website


id: self-selection-bias-20260505-0
file: data/en.wikipedia.org/wiki/Self-selection_bias-0.md
category: reference
title: Self-selection bias
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Self-selection_bias
date_saved: 2026-05-05T09:51:57.984111+00:00
instance: kb-cron
parent: self-selection-bias-20260505-0
summary:
In statistics, self-selection bias arises in any situation in which individuals select themselves into a group, causing a biased sample with nonprobability sampling. It is commonly used to describe situations where the characteristics of the people which cause them to select themselves in the group create abnormal or undesirable conditions in the group. It is closely related to the non-response bias, describing when the group of people responding has different responses than the group of people not responding. Self-selection bias is a major problem in research in sociology, psychology, economics and many other social sciences. In such fields, a poll suffering from such bias is termed a self-selected listener opinion poll or "SLOP". The term is also used in criminology to describe the process by which specific predispositions may lead an offender to choose a criminal career and lifestyle. While the effects of self-selection bias are closely related to those of selection bias, the problem arises for rather different reasons; thus there may be a purposeful intent on the part of respondents leading to self-selection bias whereas other types of selection bias may arise more inadvertently, possibly as the result of mistakes by those designing any given study. == Explanation


id: sequential-analysis-20260505-0
file: data/en.wikipedia.org/wiki/Sequential_analysis-0.md
category: reference
title: Sequential analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Sequential_analysis
date_saved: 2026-05-05T09:51:59.108535+00:00
instance: kb-cron
parent: sequential-analysis-20260505-0
summary:
In statistics, sequential analysis or sequential hypothesis testing is statistical analysis where the sample size is not fixed in advance. Instead data is evaluated as it is collected, and further sampling is stopped in accordance with a pre-defined stopping rule as soon as significant results are observed. Thus a conclusion may sometimes be reached at a much earlier stage than would be possible with more classical hypothesis testing or estimation, at consequently lower financial and/or human cost. == History == The method of sequential analysis is first attributed to Abraham Wald with Jacob Wolfowitz, W. Allen Wallis, and Milton Friedman while at Columbia University's Statistical Research Group as a tool for more efficient industrial quality control during World War II. Its value to the war effort was immediately recognised, and led to its receiving a "restricted" classification. At the same time, George Barnard led a group working on optimal stopping in Great Britain. Another early contribution to the method was made by K.J. Arrow with D. Blackwell and M.A. Girshick. A similar approach was independently developed from first principles at about the same time by Alan Turing, as part of the Banburismus technique used at Bletchley Park, to test hypotheses


id: seriousness-check-20260505-0
file: data/en.wikipedia.org/wiki/Seriousness_check-0.md
category: reference
title: Seriousness check
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Seriousness_check
date_saved: 2026-05-05T09:52:00.364244+00:00
instance: kb-cron
parent: seriousness-check-20260505-0
summary:
The seriousness check is a technique that can be used in online research (also called Internet-based research, Web-based research, Web-based experiments) to improve data quality. Nowadays, many scientific studies with human participants are conducted online and are accessible to a large diversity of participants. Nonetheless, many people just want to look at the different pages of the questionnaire, instead of giving carefully chosen answers to the questions. The seriousness check addresses this problem: In this approach the respondents are asked about the seriousness of their participation or for a probability estimate that they will complete the entire study or experiment. Thus, by using the seriousness check irrelevant data entries can be easily identified and be excluded from the data analysis. == Method == Seriousness checks can be implemented both before and after participation in the study. However, it has been shown that the seriousness check is a good predictor of dropout rates when implemented in the first page of the experiment. There are Web-based tools, e.g. WEXTOR, that implement the seriousness check by default. == Impact == Several studies have shown that performing a seriousness check at the start of a study best predicts motivation and dropout probability. It was


id: set-balancing-20260505-0
file: data/en.wikipedia.org/wiki/Set_balancing-0.md
category: reference
title: Set balancing
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Set_balancing
date_saved: 2026-05-05T09:52:01.568529+00:00
instance: kb-cron
parent: set-balancing-20260505-0
summary:
The set balancing problem in mathematics is the problem of dividing a set to two subsets that have roughly the same characteristics. It arises naturally in design of experiments. There is a group of subjects. Each subject has several features, which are considered binary. For example: each subject can be either young or old; either black or white; either tall or short; etc. The goal is to divide the subjects to two sub-groups: treatment group (T) and control group (C), such that for each feature, the number of subjects that have this feature in T is roughly equal to the number of subjects that have this feature in C. E.g., both groups should have roughly the same number of young people, the same number of black people, the same number of tall people, etc. == Matrix representation == Formally, the set balancing problem can be described as follows. m {\displaystyle m} is the number of subjects in the general population. n {\displaystyle n} is the number of potential features. The subjects are described by A {\displaystyle A} , an n × m {\displaystyle n\times m} matrix with entries in 0 , 1 {\displaystyle {0,1}} . Each column represents a subject


id: single-subject-research-20260505-0
file: data/en.wikipedia.org/wiki/Single-subject_research-0.md
category: reference
title: Single-subject research
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Single-subject_research
date_saved: 2026-05-05T09:52:05.128250+00:00
instance: kb-cron
parent: single-subject-research-20260505-0
summary:
Single-subject research is a group of research methods that are used extensively in the experimental analysis of behavior and applied behavior analysis with both human and non-human participants. This research strategy focuses on one participant and tracks their progress in the research topic over a period of time. Single-subject research allows researchers to track changes in an individual over a large stretch of time instead of observing different people at different stages. This type of research can provide critical data in several fields, specifically psychology. It is most commonly used in experimental and applied analysis of behaviors. This research has been heavily debated over the years. Some believe that this research method is not effective at all while others praise the data that can be collected from it. Principal methods in this type of research are: A-B-A-B designs, Multi-element designs, Multiple Baseline designs, Repeated acquisition designs, Brief experimental designs and Combined designs. These methods form the heart of the data collection and analytic code of behavior analysis. Behavior analysis is data driven, inductive, and disinclined to hypothetico-deductive methods. == Experimental questions == Experimental questions are decisive in determining the nature of the experimental design to be selected. There are four


id: solomon-four-group-design-20260505-0
file: data/en.wikipedia.org/wiki/Solomon_four-group_design-0.md
category: reference
title: Solomon four-group design
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Solomon_four-group_design
date_saved: 2026-05-05T09:52:06.354013+00:00
instance: kb-cron
parent: solomon-four-group-design-20260505-0
summary:
The Solomon four-group design is a research method developed by Richard Solomon in 1949. It is sometimes used in social science, psychology and medicine. It can be used if there are concerns that the treatment might be sensitized by the pre-test. In addition of the usual two groups (treatment and control), it has a second pair of groups who do not receive a pre-intervention evaluation. == Structure == The structure of the trial is shown in the table : The first two groups receive the evaluation test before and after the study, as in a normal two-group trial. The second groups receive the evaluation only after the study. The effectiveness of the treatment can be evaluated by comparisons between groups 1 and 3 and between groups 2 and 4.. In addition, the effect of the pre-treatment evaluation can be calculated by comparing the control group who received the pre-treatment evaluation with those who did not (groups 2 and 4). Various statistical treatments for the Solomon four-group design have been put forward, including Stouffer's Z and Monte Carlo. == References ==


id: sparsity-of-effects-principle-20260505-0
file: data/en.wikipedia.org/wiki/Sparsity-of-effects_principle-0.md
category: reference
title: Sparsity-of-effects principle
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Sparsity-of-effects_principle
date_saved: 2026-05-05T09:52:07.517443+00:00
instance: kb-cron
parent: sparsity-of-effects-principle-20260505-0
summary:
In the statistical analysis of the results from factorial experiments, the sparsity-of-effects principle states that a system is usually dominated by main effects and low-order interactions. Thus it is most likely that main (single factor) effects and two-factor interactions are the most significant responses in a factorial experiment. In other words, higher order interactions such as three-factor interactions are very rare. This is sometimes referred to as the hierarchical ordering principle. The sparsity-of-effects principle actually refers to the idea that only a few effects in a factorial experiment will be statistically significant. This principle is only valid on the assumption of a factor space far from a stationary point. == See also == Occam's Razor Pareto principle == References ==


id: spectrum-bias-20260505-0
file: data/en.wikipedia.org/wiki/Spectrum_bias-0.md
category: reference
title: Spectrum bias
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Spectrum_bias
date_saved: 2026-05-05T09:52:08.680162+00:00
instance: kb-cron
parent: spectrum-bias-20260505-0
summary:
In biostatistics, spectrum bias refers to the phenomenon that the performance of a diagnostic test may vary in different clinical settings because each setting has a different mix of patients. Because the performance may be dependent on the mix of patients, performance at one clinic may not be predictive of performance at another clinic. These differences are interpreted as a kind of bias. Mathematically, the spectrum bias is a sampling bias and not a traditional statistical bias; this has led some authors to refer to the phenomenon as spectrum effects, whilst others maintain it is a bias if the true performance of the test differs from that which is 'expected'. Usually the performance of a diagnostic test is measured in terms of its sensitivity and specificity and it is changes in these that are considered when referring to spectrum bias. However, other performance measures such as the likelihood ratios may also be affected by spectrum bias. Generally spectrum bias is considered to have three causes. The first is due to a change in the case-mix of those patients with the target disorder (disease) and this affects the sensitivity. The second is due to a change in the case-mix of those


id: spherical-design-20260505-0
file: data/en.wikipedia.org/wiki/Spherical_design-0.md
category: reference
title: Spherical design
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Spherical_design
date_saved: 2026-05-05T09:52:09.899202+00:00
instance: kb-cron
parent: spherical-design-20260505-0
summary:
A spherical design, part of combinatorial design theory in mathematics, is a finite set of N points on the d-dimensional unit d-sphere Sd such that the average value of any polynomial f of degree t or less on the set equals the average value of f on the whole sphere (that is, the integral of f over Sd divided by the area or measure of Sd). Such a set is often called a spherical t-design to indicate the value of t, which is a fundamental parameter. The concept of a spherical design is due to Delsarte, Goethals, and Seidel, although these objects were understood as particular examples of cubature formulas earlier. Spherical designs can be of value in approximation theory, in statistics for experimental design, in combinatorics, and in geometry. The main problem is to find examples, given d and t, that are not too large; however, such examples may be hard to come by. Spherical t-designs have also recently been appropriated in quantum mechanics in the form of quantum t-designs with various applications to quantum information theory and quantum computing. == Existence of spherical designs == The existence and structure of spherical designs on the circle were studied in


id: spillover-experiment-20260505-0
file: data/en.wikipedia.org/wiki/Spillover_(experiment)-0.md
category: reference
title: Spillover (experiment)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Spillover_(experiment)
date_saved: 2026-05-05T09:52:11.116942+00:00
instance: kb-cron
parent: spillover-experiment-20260505-0
summary:
In experiments, a spillover is an indirect effect on a subject not directly treated by the experiment. These effects are useful for policy analysis but complicate the statistical analysis of experiments. Analysis of spillover effects involves relaxing the non-interference assumption, or SUTVA (Stable Unit Treatment Value Assumption). This assumption requires that subject i's revelation of its potential outcomes depends only on that subject i's own treatment status, and is unaffected by another subject j's treatment status. In ordinary settings where the researcher seeks to estimate the average treatment effect ( A T E ^ {\displaystyle {\widehat {ATE}}} ), violation of the non-interference assumption means that traditional estimators for the ATE, such as difference-in-means, may be biased. However, there are many real-world instances where a unit's revelation of potential outcomes depend on another unit's treatment assignment, and analyzing these effects may be just as important as analyzing the direct effect of treatment. One solution to this problem is to redefine the causal estimand of interest by redefining a subject's potential outcomes in terms of one's own treatment status and related subjects' treatment status. The researcher can then analyze various estimands of interest separately. One important assumption here is that this process


id: spillover-experiment-20260505-1
file: data/en.wikipedia.org/wiki/Spillover_(experiment)-1.md
category: reference
title: Spillover (experiment)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Spillover_(experiment)
date_saved: 2026-05-05T09:52:11.116942+00:00
instance: kb-cron
parent: spillover-experiment-20260505-0
summary:
=== Using randomization inference for hypothesis testing === In experimental settings where treatment is randomized, we can use randomization inference to test for the existence of spillover effects. The key advantage of this approach is that randomization inference is finite-sample valid, without requiring correct model specification or normal asymptotics. To be specific, consider the aforementioned example experiment in college dorm rooms, and suppose we want to test: H 0 : Y 0 , 1 = Y 0 , 0 {\displaystyle H_{0}:Y_{0,1}=Y_{0,0}} This hypothesis posits that there is no spillover effect on students who don't receive the information (i.e., students who are in control in the experiment). Rejecting this hypothesis implies that even when students don't receive the information message directly, they still may receive it indirectly from treated roommates; hence, there is a spillover effect. To test a hypothesis like H 0 {\displaystyle H_{0}} we can apply a conditional Fisher randomization test. Let R i j = 1 {\displaystyle R_{ij}=1} be an indicator denoting that students i , j {\displaystyle i,j} are roommates, where we assumed for simplicity that each student has exactly one roommate. Suppose this is a completely randomized design and let D i {\displaystyle D_{i}} denote the


id: spillover-experiment-20260505-2
file: data/en.wikipedia.org/wiki/Spillover_(experiment)-2.md
category: reference
title: Spillover (experiment)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Spillover_(experiment)
date_saved: 2026-05-05T09:52:11.116942+00:00
instance: kb-cron
parent: spillover-experiment-20260505-0
summary:
I k P I k ′ = [ π 1 ( d k ) π 12 ( d k ) ⋯ π 1 N ( d k ) π 21 ( d k ) π 2 ( d k ) ⋯ π 2 N ( d k ) ⋮ ⋮ ⋱ π N 1 ( d k ) π N 2 ( d k ) π N ( d k ) ] {\displaystyle \mathbf {I} _{k}\mathbf {P} \mathbf {I} _{k}^{\prime }=\left[{\begin{array}{cccc}{\pi _{1}(d_{k})}&\pi _{12}(d_{k})&\cdots &\pi _{1N}(d_{k})\\\pi _{21}(d_{k})&\pi _{2}(d_{k})&\cdots &\pi _{2N}(d_{k})\\\vdots &\vdots &\ddots &\\\pi _{N1}(d_{k})&\pi _{N2}(d_{k})&{}&\pi _{N}(d_{k})\end{array}}\right]} In a similar fashion, the joint probability of exposure of i being in exposure condition d k {\displaystyle d_{k}} and j being in a different exposure condition d l {\displaystyle d_{l}} can be obtained by calculating I k P I l ′ {\displaystyle \mathbf {I} _{k}\mathbf {P} \mathbf {I} _{l}^{\prime }} : I k P I l ′ = [ 0 π 12 ( d k , d l ) … π 1 N ( d k , d l ) π 21 ( d k , d l ) 0 … π 2 N ( d k , d l ) ⋮ ⋮ ⋱


id: standard-treatment-20260505-0
file: data/en.wikipedia.org/wiki/Standard_treatment-0.md
category: reference
title: Standard treatment
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Standard_treatment
date_saved: 2026-05-05T09:52:12.282392+00:00
instance: kb-cron
parent: standard-treatment-20260505-0
summary:
The standard treatment, also known as the standard of care, is the medical treatment that is normally provided to people with a given condition. In many scientific studies, the control group receives the standard treatment rather than a placebo while a treatment group receives the experimental treatment. After the clinical trial, researchers compare the outcomes of the two groups to see if the experimental treatment is better than, as good as or not as beneficial as the standard treatment. == Active (positive) concurrent control == In an active control or positive control trial, subjects are randomly assigned to the test treatment or to an active control treatment. Such clinical trials are usually double-blind, but this is not always possible; many oncology trials, for example, are considered difficult or impossible to blind because of different regimens, different routes of administration, and different toxicities. Active control trials can have two distinct objectives with respect to showing efficacy: (1) to show efficacy of the test treatment by showing it is as good as a known effective treatment or (2) to show efficacy by showing superiority of the test treatment to the active control. They may also be used with the primary objective of


id: statistical-hypothesis-test-20260505-0
file: data/en.wikipedia.org/wiki/Statistical_hypothesis_test-0.md
category: reference
title: Statistical hypothesis test
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Statistical_hypothesis_test
date_saved: 2026-05-05T09:52:13.457599+00:00
instance: kb-cron
parent: statistical-hypothesis-test-20260505-0
summary:
A statistical hypothesis test is a method of statistical inference used to decide whether the data provide sufficient evidence to reject a particular hypothesis. A statistical hypothesis test typically involves a calculation of a test statistic. Then a decision is made, either by comparing the test statistic to a critical value or equivalently by evaluating a p-value computed from the test statistic. Roughly 100 specialized statistical tests are in use and noteworthy. == History == While hypothesis testing was popularized early in the 20th century, early forms were used in the 1700s. The first use is credited to John Arbuthnot (1710), followed by Pierre-Simon Laplace (1770s), in analyzing the human sex ratio at birth; see § Human sex ratio. === Choice of null hypothesis === Paul Meehl has argued that the epistemological importance of the choice of null hypothesis has gone largely unacknowledged. When the null hypothesis is predicted by theory, a more precise experiment will be a more severe test of the underlying theory. When the null hypothesis defaults to "no difference" or "no effect", a more precise experiment is a less severe test of the theory that motivated performing the experiment. An examination of the origins of the


id: statistical-hypothesis-test-20260505-1
file: data/en.wikipedia.org/wiki/Statistical_hypothesis_test-1.md
category: reference
title: Statistical hypothesis test
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Statistical_hypothesis_test
date_saved: 2026-05-05T09:52:13.457599+00:00
instance: kb-cron
parent: statistical-hypothesis-test-20260505-0
summary:
=== Null hypothesis significance testing (NHST) === The modern version of hypothesis testing is generally called the null hypothesis significance testing (NHST) and is a hybrid of the Fisher approach with the Neyman-Pearson approach. In 2000, Raymond S. Nickerson wrote an article stating that NHST was (at the time) "arguably the most widely used method of analysis of data collected in psychological experiments and has been so for about 70 years" and that it was at the same time "very controversial". This fusion resulted from confusion by writers of statistical textbooks (as predicted by Fisher) beginning in the 1940s (but signal detection, for example, still uses the Neyman/Pearson formulation). Great conceptual differences and many caveats in addition to those mentioned above were ignored. Neyman and Pearson provided the stronger terminology, the more rigorous mathematics and the more consistent philosophy, but the subject taught today in introductory statistics has more similarities with Fisher's method than theirs. Sometime around 1940, authors of statistical text books began combining the two approaches by using the p-value in place of the test statistic (or data) to test against the Neyman–Pearson "significance level". == Philosophy == Hypothesis testing and philosophy intersect. Inferential statistics, which includes hypothesis


id: statistical-hypothesis-test-20260505-2
file: data/en.wikipedia.org/wiki/Statistical_hypothesis_test-2.md
category: reference
title: Statistical hypothesis test
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Statistical_hypothesis_test
date_saved: 2026-05-05T09:52:13.457599+00:00
instance: kb-cron
parent: statistical-hypothesis-test-20260505-0
summary:
"The Geiger-counter reading is 10. The limit is 9. Check the suitcase." "The Geiger-counter reading is high; 97% of safe suitcases have lower readings. The limit is 95%. Check the suitcase." The former report is adequate, the latter gives a more detailed explanation of the data and the reason why the suitcase is being checked. Not rejecting the null hypothesis does not mean the null hypothesis is "accepted" per se (though Neyman and Pearson used that word in their original writings; see the Interpretation section). The processes described here are perfectly adequate for computation. They seriously neglect the design of experiments considerations. It is particularly critical that appropriate sample sizes be estimated before conducting the experiment. The phrase "test of significance" was coined by statistician Ronald Fisher. === Interpretation === When the null hypothesis is true and statistical assumptions are met, the probability that the p-value will be less than or equal to the significance level α {\displaystyle \alpha } is at most α {\displaystyle \alpha } . This ensures that the hypothesis test maintains its specified false positive rate (provided that statistical assumptions are met). The p-value is the probability that a test statistic which is at least as


id: statistical-hypothesis-test-20260505-3
file: data/en.wikipedia.org/wiki/Statistical_hypothesis_test-3.md
category: reference
title: Statistical hypothesis test
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Statistical_hypothesis_test
date_saved: 2026-05-05T09:52:13.457599+00:00
instance: kb-cron
parent: statistical-hypothesis-test-20260505-0
summary:
The following definitions are mainly based on the exposition in the book by Lehmann and Romano: Statistical hypothesis: A statement about the parameters describing a population (not a sample). Test statistic: A value calculated from a sample without any unknown parameters, often to summarize the sample for comparison purposes. Simple hypothesis: Any hypothesis which specifies the population distribution completely. Composite hypothesis: Any hypothesis which does not specify the population distribution completely. Null hypothesis (H0) Positive data: Data that enable the investigator to reject a null hypothesis. Alternative hypothesis (H1) Critical values of a statistical test are the boundaries of the acceptance region of the test. The acceptance region is the set of values of the test statistic for which the null hypothesis is not rejected. Depending on the shape of the acceptance region, there can be one or more than one critical value. Region of rejection / Critical region: The set of values of the test statistic for which the null hypothesis is rejected. Power of a test (1 − β) Size: For simple hypotheses, this is the test's probability of incorrectly rejecting the null hypothesis. The false positive rate. For composite hypotheses this is the supremum of the probability


id: statistical-hypothesis-test-20260505-4
file: data/en.wikipedia.org/wiki/Statistical_hypothesis_test-4.md
category: reference
title: Statistical hypothesis test
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Statistical_hypothesis_test
date_saved: 2026-05-05T09:52:13.457599+00:00
instance: kb-cron
parent: statistical-hypothesis-test-20260505-0
summary:
=== Courtroom trial === A statistical test procedure is comparable to a criminal trial; a defendant is considered not guilty as long as his or her guilt is not proven. The prosecutor tries to prove the guilt of the defendant. Only when there is enough evidence for the prosecution is the defendant convicted. In the start of the procedure, there are two hypotheses H 0 {\displaystyle H_{0}} : "the defendant is not guilty", and H 1 {\displaystyle H_{1}} : "the defendant is guilty". The first one, H 0 {\displaystyle H_{0}} , is called the null hypothesis. The second one, H 1 {\displaystyle H_{1}} , is called the alternative hypothesis. It is the alternative hypothesis that one hopes to support. The hypothesis of innocence is rejected only when an error is very unlikely, because one does not want to convict an innocent defendant. Such an error is called error of the first kind (i.e., the conviction of an innocent person), and the occurrence of this error is controlled to be rare. As a consequence of this asymmetric behaviour, an error of the second kind (acquitting a person who committed the crime), is more common. A criminal trial can be regarded as


id: statistical-hypothesis-test-20260505-5
file: data/en.wikipedia.org/wiki/Statistical_hypothesis_test-5.md
category: reference
title: Statistical hypothesis test
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Statistical_hypothesis_test
date_saved: 2026-05-05T09:52:13.457599+00:00
instance: kb-cron
parent: statistical-hypothesis-test-20260505-0
summary:
== Variations and sub-classes == Statistical hypothesis testing is a key technique of both frequentist inference and Bayesian inference, although the two types of inference have notable differences. Statistical hypothesis tests define a procedure that controls (fixes) the probability of incorrectly deciding that a default position (null hypothesis) is incorrect. The procedure is based on how likely it would be for a set of observations to occur if the null hypothesis were true. This probability of making an incorrect decision is not the probability that the null hypothesis is true, nor whether any specific alternative hypothesis is true. This contrasts with other possible techniques of decision theory in which the null and alternative hypothesis are treated on a more equal basis. One naïve Bayesian approach to hypothesis testing is to base decisions on the posterior probability, but this fails when comparing point and continuous hypotheses. Other approaches to decision making, such as Bayesian decision theory, attempt to balance the consequences of incorrect decisions across all possibilities, rather than concentrating on a single null hypothesis. A number of other approaches to reaching a decision based on data are available via decision theory and optimal decisions, some of which have desirable properties.


id: statistical-hypothesis-test-20260505-6
file: data/en.wikipedia.org/wiki/Statistical_hypothesis_test-6.md
category: reference
title: Statistical hypothesis test
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Statistical_hypothesis_test
date_saved: 2026-05-05T09:52:13.457599+00:00
instance: kb-cron
parent: statistical-hypothesis-test-20260505-0
summary:
The interpretation of a p-value is dependent upon stopping rule and definition of multiple comparison. The former often changes during the course of a study and the latter is unavoidably ambiguous. (i.e. "p values depend on both the (data) observed and on the other possible (data) that might have been observed but weren't"). Confusion resulting (in part) from combining the methods of Fisher and Neyman–Pearson which are conceptually distinct. Emphasis on statistical significance to the exclusion of estimation and confirmation by repeated experiments. Rigidly requiring statistical significance as a criterion for publication, resulting in publication bias. Most of the criticism is indirect. Rather than being wrong, statistical hypothesis testing is misunderstood, overused and misused. When used to detect whether a difference exists between groups, a paradox arises. As improvements are made to experimental design (e.g. increased precision of measurement and sample size), the test becomes more lenient. Unless one accepts the absurd assumption that all sources of noise in the data cancel out completely, the chance of finding statistical significance in either direction approaches 100%. However, this absurd assumption that the mean difference between two groups cannot be zero implies that the data cannot be independent and identically distributed (i.i.d.)


id: statistical-hypothesis-test-20260505-7
file: data/en.wikipedia.org/wiki/Statistical_hypothesis_test-7.md
category: reference
title: Statistical hypothesis test
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Statistical_hypothesis_test
date_saved: 2026-05-05T09:52:13.457599+00:00
instance: kb-cron
parent: statistical-hypothesis-test-20260505-0
summary:
A unifying position of critics is that statistics should not lead to an accept-reject conclusion or decision, but to an estimated value with an interval estimate; this data-analysis philosophy is broadly referred to as estimation statistics. Estimation statistics can be accomplished with either frequentist or Bayesian methods. Critics of significance testing have advocated basing inference less on p-values and more on confidence intervals for effect sizes for importance, prediction intervals for confidence, replications and extensions for replicability, meta-analyses for generality :. But none of these suggested alternatives inherently produces a decision. Lehmann said that hypothesis testing theory can be presented in terms of conclusions/decisions, probabilities, or confidence intervals: "The distinction between the ... approaches is largely one of reporting and interpretation." Bayesian inference is one proposed alternative to significance testing. (Nickerson cited 10 sources suggesting it, including Rozeboom (1960)). For example, Bayesian parameter estimation can provide rich information about the data from which researchers can draw inferences, while using uncertain priors that exert only minimal influence on the results when enough data is available. Psychologist John K. Kruschke has suggested Bayesian estimation as an alternative for the t-test and has also contrasted Bayesian estimation for assessing null values with Bayesian


id: steiner-system-20260505-0
file: data/en.wikipedia.org/wiki/Steiner_system-0.md
category: reference
title: Steiner system
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Steiner_system
date_saved: 2026-05-05T09:52:14.665238+00:00
instance: kb-cron
parent: steiner-system-20260505-0
summary:
In combinatorial mathematics, a Steiner system (named after Jakob Steiner) is a type of block design, specifically a t-design with λ = 1 and t = 2 or (recently) t ≥ 2. A Steiner system with parameters t, k, n, written S(t,k,n), is an n-element set S together with a set of k-element subsets of S (called blocks) with the property that each t-element subset of S is contained in exactly one block. In an alternative notation for block designs, an S(t,k,n) would be a t-(n,k,1) design. This definition is relatively new. The classical definition of Steiner systems also required that k = t + 1. An S(2,3,n) was (and still is) called a Steiner triple (or triad) system, while an S(3,4,n) is called a Steiner quadruple system, and so on. With the generalization of the definition, this naming system is no longer strictly adhered to. Long-standing problems in design theory were whether there exist any nontrivial Steiner systems (nontrivial meaning t < k < n) with t ≥ 6; also whether infinitely many have t = 4 or 5. Both existences were proved by Peter Keevash in 2014. His proof is non-constructive and, as of 2019, no actual Steiner


id: steiner-system-20260505-1
file: data/en.wikipedia.org/wiki/Steiner_system-1.md
category: reference
title: Steiner system
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Steiner_system
date_saved: 2026-05-05T09:52:14.665238+00:00
instance: kb-cron
parent: steiner-system-20260505-0
summary:
for girls labeled A to O, and constructed each subsequent week's solution from its immediate predecessor by changing A to B, B to C, ... L to M and M back to A, all while leaving N and O unchanged. The Week 13 solution, upon undergoing that relabeling, returns to the Week 1 solution. Denniston reported in his paper that the search he employed took 7 hours on an Elliott 4130 computer at the University of Leicester, and he immediately ended the search on finding the solution above, not looking to establish uniqueness. The number of non-isomorphic solutions to Sylvester's problem remains unknown as of 2021. == Properties == It is clear from the definition of S(t, k, n) that 1 < t < k < n {\displaystyle 1<t<k<n} . (Equalities, while technically possible, lead to trivial systems.) If S(t, k, n) exists, then taking all blocks containing a specific element and discarding that element gives a derived system S(t−1, k−1, n−1). Therefore, the existence of S(t−1, k−1, n−1) is a necessary condition for the existence of S(t, k, n). The number of t-element subsets in S is ( n t ) {\displaystyle {\tbinom {n}{t}}} , while the number of


id: steiner-system-20260505-2
file: data/en.wikipedia.org/wiki/Steiner_system-2.md
category: reference
title: Steiner system
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Steiner_system
date_saved: 2026-05-05T09:52:14.665238+00:00
instance: kb-cron
parent: steiner-system-20260505-0
summary:
a "block" (it contains ∞ together with the 5 nonzero squares in F11). From this block, we obtain the other blocks of the S(5,6,12) system by repeatedly applying the linear fractional transformations: z ′ = f ( z ) = a z + b c z + d , {\displaystyle z'=f(z)={\frac {az+b}{cz+d}},} where a,b,c,d are in F11 and ad − bc = 1. With the usual conventions of defining f (−d/c) = ∞ and f (∞) = a/c, these functions map the set S onto itself. In geometric language, they are projectivities of the projective line. They form a group under composition which is the projective special linear group PSL(2,11) of order 660. There are exactly five elements of this group that leave the starting block fixed setwise, namely those such that b=c=0 and ad=1 so that f(z) = a2 z. So there will be 660/5 = 132 images of that block. As a consequence of the multiply transitive property of this group acting on this set, any subset of five elements of S will appear in exactly one of these 132 images of size six. === Kitten construction === An alternative construction of W12 is obtained by use of


id: steiner-system-20260505-3
file: data/en.wikipedia.org/wiki/Steiner_system-3.md
category: reference
title: Steiner system
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Steiner_system
date_saved: 2026-05-05T09:52:14.665238+00:00
instance: kb-cron
parent: steiner-system-20260505-0
summary:
where a,b,c,d are in F23 and ad − bc = 1. With the usual conventions of defining f (−d/c) = ∞ and f (∞) = a/c, these functions map the set S onto itself. In geometric language, they are projectivities of the projective line. They form a group under composition which is the projective special linear group PSL(2,23) of order 6072. There are exactly 8 elements of this group that leave the initial block fixed setwise. So there will be 6072/8 = 759 images of that block. These form the octads of S(5,8,24). === Construction from the Miracle Octad Generator === The Miracle Octad Generator (MOG) is a tool to generate octads, such as those containing specified subsets. It consists of a 4x6 array with certain weights assigned to the rows. In particular, an 8-subset should obey three rules in order to be an octad of S(5,8,24). First, each of the 6 columns should have the same parity, that is, they should all have an odd number of cells or they should all have an even number of cells. Second, the top row should have the same parity as each of the columns. Third, the rows are respectively multiplied by


id: stepped-wedge-trial-20260505-0
file: data/en.wikipedia.org/wiki/Stepped-wedge_trial-0.md
category: reference
title: Stepped-wedge trial
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Stepped-wedge_trial
date_saved: 2026-05-05T09:52:15.845060+00:00
instance: kb-cron
parent: stepped-wedge-trial-20260505-0
summary:
In medicine, a stepped-wedge trial (or SWT) is a type of randomised controlled trial (RCT). An RCT is a scientific experiment that is designed to reduce bias when testing a new medical treatment, a social intervention, or another testable hypothesis. In a traditional RCT, the researcher randomly divides the experiment participants into two groups at the same time: One group receives the treatment (the "treatment group") The other group does not get the treatment (the "control group"). In a SWT, a logistic constraint typically prevents the simultaneous treatment of some participants, and instead, all or most participants receive the treatment in waves or "steps". For instance, a researcher wants to measure whether teaching college students how to make several meals increased their propensity to cook at home instead of eating out. In a traditional RCT, a sample of students would be selected and some would be trained on how to cook these meals, whereas the others would not. Both groups would be monitored to see how frequently they ate out. In the end, the number of times the treatment group ate out would be compared to the number of times the control group ate out, most likely with a t-test


id: stepped-wedge-trial-20260505-1
file: data/en.wikipedia.org/wiki/Stepped-wedge_trial-1.md
category: reference
title: Stepped-wedge trial
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Stepped-wedge_trial
date_saved: 2026-05-05T09:52:15.845060+00:00
instance: kb-cron
parent: stepped-wedge-trial-20260505-0
summary:
== Model == While there are several other potential methods for modeling outcomes in an SWT, the work of Hussey and Hughes "first described methods to determine statistical power available when using a stepped wedge design." What follows is their design. Suppose there are N {\displaystyle N} samples divided into C {\displaystyle C} clusters. At each time point t = 1 , … , T {\displaystyle t=1,\ldots ,T} , preferably equally spaced in actual time, some number of clusters are treated. Let Z c t {\displaystyle Z_{ct}} be 1 {\displaystyle 1} if cluster c {\displaystyle c} has been treated at time t {\displaystyle t} and 0 {\displaystyle 0} otherwise. In particular, note that if Z c t = 1 {\displaystyle Z_{ct}=1} then Z c , t + 1 = 1 {\displaystyle Z_{c,t+1}=1} . For each participant i {\displaystyle i} in cluster c {\displaystyle c} , measure the outcome to be studied y i c t {\displaystyle y_{ict}} at time t {\displaystyle t} . Note that the notation allows for clustering by including c {\displaystyle c} in the subscript of y i c t {\displaystyle y_{ict}} , α c {\displaystyle \alpha _{c}} , Z c t {\displaystyle Z_{ct}} , and ϵ


id: stepped-wedge-trial-20260505-2
file: data/en.wikipedia.org/wiki/Stepped-wedge_trial-2.md
category: reference
title: Stepped-wedge trial
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Stepped-wedge_trial
date_saved: 2026-05-05T09:52:15.845060+00:00
instance: kb-cron
parent: stepped-wedge-trial-20260505-0
summary:
First, since in SWTs the study period lasts longer and all the subjects eventually receive the treatment, costs may increase significantly. Because the design can be expensive, SWTs may not be the optimal solution when measurement precision and outcome autocorrelation are high. Moreover, since everyone is eventually treated, SWTs do not facilitate downstream analysis. Secondly, in an SWT, more clusters are exposed to the intervention at later than earlier time periods. As such, it is possible that an underlying temporal trend may confound the intervention effect, and so the confounding effect of time must be accounted for in both pre-trial power calculations and post-trial analysis. Specifically, in post-trial analysis, the use of generalized linear mixed models or generalized estimating equations is recommended. Finally, the design and analysis of stepped-wedge trials is therefore more complex than for other types of randomized trials. Previous systematic reviews highlighted the poor reporting of sample size calculations and a lack of consistency in the analysis of such trials. Hussey and Hughes were the first authors to suggest a structure and formula for estimating power in stepped-wedge studies in which data was collected at each and every step. This has now been expanded for designs in


id: stock-sampling-20260505-0
file: data/en.wikipedia.org/wiki/Stock_sampling-0.md
category: reference
title: Stock sampling
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Stock_sampling
date_saved: 2026-05-05T09:52:17.002391+00:00
instance: kb-cron
parent: stock-sampling-20260505-0
summary:
Stock sampling is sampling people in a certain state at the time of the survey. This is in contrast to flow sampling, where the relationship of interest deals with duration or survival analysis. In stock sampling, rather than focusing on transitions within a certain time interval, we only have observations at a certain point in time. This can lead to both left and right censoring. Imposing the same model on data that have been generated under the two different sampling regimes can lead to research reaching fundamentally different conclusions if the joint distribution across the flow and stock samples differ sufficiently. Stock sampling essentially leads to a sample selection problem. This selection issue is akin to the truncated regression model where we face selection on the basis of a binary response variable, but the problem has been referred to as length-biased sampling in this specific context. Consider, for example, the figure below that plots some duration data. If a researcher would revert to stock sampling and only sample and survey individuals at the survey dates (i.e. the survey data, 12 months after the survey date, etc.), there is a high likelihood the short duration spells will be omitted from the


id: subgroup-analysis-20260505-0
file: data/en.wikipedia.org/wiki/Subgroup_analysis-0.md
category: reference
title: Subgroup analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Subgroup_analysis
date_saved: 2026-05-05T09:52:18.188918+00:00
instance: kb-cron
parent: subgroup-analysis-20260505-0
summary:
Subgroup analysis refers to repeating the analysis of a study within subgroups of subjects defined by a subgrouping variable. For example: smoking status defining two subgroups: smokers and non-smokers. == See also == Post hoc analysis == References ==


id: surrogate-model-20260505-0
file: data/en.wikipedia.org/wiki/Surrogate_model-0.md
category: reference
title: Surrogate model
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Surrogate_model
date_saved: 2026-05-05T09:52:19.376341+00:00
instance: kb-cron
parent: surrogate-model-20260505-0
summary:
A surrogate model is an engineering method used when an outcome of interest cannot be easily measured or computed, so an approximate mathematical model of the outcome is used instead. Most engineering design problems require experiments and/or simulations to evaluate design objective and constraint functions as a function of design variables. For example, in order to find the optimal airfoil shape for an aircraft wing, an engineer simulates the airflow around the wing for different shape variables (e.g., length, curvature, material, etc.). For many real-world problems, however, a single simulation can take many minutes, hours, or even days to complete. As a result, routine tasks such as design optimization, design space exploration, sensitivity analysis and "what-if" analysis become impossible since they require thousands or even millions of simulation evaluations. One way of alleviating this burden is by constructing approximation models, known as surrogate models, metamodels or emulators, that mimic the behavior of the simulation model as closely as possible while being computationally cheaper to evaluate. Surrogate models are constructed using a data-driven, bottom-up approach. The exact, inner working of the simulation code is not assumed to be known (or even understood), relying solely on the input-output behavior. A model is


id: surrogate-model-20260505-1
file: data/en.wikipedia.org/wiki/Surrogate_model-1.md
category: reference
title: Surrogate model
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Surrogate_model
date_saved: 2026-05-05T09:52:19.376341+00:00
instance: kb-cron
parent: surrogate-model-20260505-0
summary:
== Surrogate-Assisted Evolutionary Algorithms (SAEAs) == SAEAs are an advanced class of optimization techniques that integrate evolutionary algorithms (EAs) with surrogate models. In traditional EAs, evaluating the fitness of candidate solutions often requires computationally expensive simulations or experiments. SAEAs address this challenge by building a surrogate model, which is a computationally inexpensive approximation of the objective function or constraint functions. The surrogate model serves as a substitute for the actual evaluation process during the evolutionary search. It allows the algorithm to quickly estimate the fitness of new candidate solutions, thereby reducing the number of expensive evaluations needed. This significantly speeds up the optimization process, especially in cases where the objective function evaluations are time-consuming or resource-intensive. SAEAs typically involve three main steps: (1) building the surrogate model using a set of initial sampled data points, (2) performing the evolutionary search using the surrogate model to guide the selection, crossover, and mutation operations, and (3) periodically updating the surrogate model with new data points generated during the evolutionary process to improve its accuracy. By balancing exploration (searching new areas in the solution space) and exploitation (refining known promising areas), SAEAs can efficiently find high-quality solutions to complex optimization problems. They have


id: synthetic-control-method-20260505-0
file: data/en.wikipedia.org/wiki/Synthetic_control_method-0.md
category: reference
title: Synthetic control method
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Synthetic_control_method
date_saved: 2026-05-05T09:52:20.540120+00:00
instance: kb-cron
parent: synthetic-control-method-20260505-0
summary:
The synthetic control method is an econometric method used to evaluate the effect of large-scale interventions. It was proposed in a series of articles by Alberto Abadie and his coauthors. A synthetic control is a weighted average of several units (such as regions or companies) combined to recreate the trajectory that the outcome of a treated unit would have followed in the absence of the intervention. The weights are selected in a data-driven manner to ensure that the resulting synthetic control closely resembles the treated unit in terms of key predictors of the outcome variable. Unlike difference in differences approaches, this method can account for the effects of confounders changing over time, by weighting the control group to better match the treatment group before the intervention. Another advantage of the synthetic control method is that it allows researchers to systematically select comparison groups. It has been applied to the fields of economics, political science, health policy, criminology, and others. The synthetic control method combines elements from matching and difference-in-differences techniques. Difference-in-differences methods are often-used policy evaluation tools that estimate the effect of an intervention at an aggregate level (e.g. state, country, age group etc.) by averaging over a set of


id: taguchi-methods-20260505-0
file: data/en.wikipedia.org/wiki/Taguchi_methods-0.md
category: reference
title: Taguchi methods
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Taguchi_methods
date_saved: 2026-05-05T09:52:21.761240+00:00
instance: kb-cron
parent: taguchi-methods-20260505-0
summary:
Taguchi methods (Japanese: タグチメソッド) are statistical methods, sometimes called robust design methods, developed by Genichi Taguchi to improve the quality of manufactured goods, and more recently also applied to engineering, biotechnology, marketing and advertising. Professional statisticians have welcomed the goals and improvements brought about by Taguchi methods, particularly by Taguchi's development of designs for studying variation, but have criticized the inefficiency of some of Taguchi's proposals. Taguchi's work includes three principal contributions to statistics: A specific loss function The philosophy of off-line quality control; and Innovations in the design of experiments. == Loss functions == === Loss functions in the statistical theory === Traditionally, statistical methods have relied on mean-unbiased estimators of treatment effects: Under the conditions of the Gauss–Markov theorem, least squares estimators have minimum variance among all mean-unbiased linear estimators. The emphasis on comparisons of means also draws (limiting) comfort from the law of large numbers, according to which the sample means converge to the true mean. Fisher's textbook on the design of experiments emphasized comparisons of treatment means. However, loss functions were avoided by Ronald A. Fisher. === Taguchi's use of loss functions === Taguchi knew statistical theory mainly from the followers of Ronald A. Fisher, who


id: taguchi-methods-20260505-1
file: data/en.wikipedia.org/wiki/Taguchi_methods-1.md
category: reference
title: Taguchi methods
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Taguchi_methods
date_saved: 2026-05-05T09:52:21.761240+00:00
instance: kb-cron
parent: taguchi-methods-20260505-0
summary:
== Design of experiments == Taguchi developed his experimental theories independently. Taguchi read works following R. A. Fisher only in 1954. === Outer arrays === Taguchi's designs aimed to allow greater understanding of variation than did many of the traditional designs from the analysis of variance (following Fisher). Taguchi contended that conventional sampling is inadequate here as there is no way of obtaining a random sample of future conditions. In Fisher's design of experiments and analysis of variance, experiments aim to reduce the influence of nuisance factors to allow comparisons of the mean treatment-effects. Variation becomes even more central in Taguchi's thinking. Taguchi proposed extending each experiment with an "outer array" (possibly an orthogonal array); the "outer array" should simulate the random environment in which the product would function. This is an example of judgmental sampling. Many quality specialists have been using "outer arrays". Later innovations in outer arrays resulted in "compounded noise." This involves combining a few noise factors to create two levels in the outer array: First, noise factors that drive output lower, and second, noise factors that drive output higher. "Compounded noise" simulates the extremes of noise variation but uses fewer experimental runs than would previous Taguchi


id: treatment-and-control-groups-20260505-0
file: data/en.wikipedia.org/wiki/Treatment_and_control_groups-0.md
category: reference
title: Treatment and control groups
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Treatment_and_control_groups
date_saved: 2026-05-05T09:52:24.158140+00:00
instance: kb-cron
parent: treatment-and-control-groups-20260505-0
summary:
In the design of experiments, hypotheses are applied to experimental units in a treatment group. In comparative experiments, members of a control group receive a standard treatment, a placebo, or no treatment at all. There may be more than one treatment group, more than one control group, or both. A placebo control group can be used to support a double-blind study, in which some subjects are given an ineffective treatment (in medical studies typically a sugar pill) to minimize differences in the experiences of subjects in the different groups; this is done in a way that ensures no participant in the experiment (subject or experimenter) knows to which group each subject belongs. In such cases, a third, non-treatment control group can be used to measure the placebo effect directly, as the difference between the responses of placebo subjects and untreated subjects, perhaps paired by age group or other factors (such as being twins). For the conclusions drawn from the results of an experiment to have validity, it is essential that the items or patients assigned to treatment and control groups be representative of the same population. In some experiments, such as many in agriculture or psychology, this can be achieved


id: type-i-and-type-ii-errors-20260505-0
file: data/en.wikipedia.org/wiki/Type_I_and_type_II_errors-0.md
category: reference
title: Type I and type II errors
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Type_I_and_type_II_errors
date_saved: 2026-05-05T09:52:25.345633+00:00
instance: kb-cron
parent: type-i-and-type-ii-errors-20260505-0
summary:
Type I error, or a false positive, is the incorrect rejection of a true null hypothesis in statistical hypothesis testing. A type II error, or a false negative, is the incorrect failure to reject a false null hypothesis. An analysis commits a Type I error when some baseline assumption is incorrectly rejected because of new, misleading information. Meanwhile, a Type II error is made when such an assumption is maintained, due to flawed or insufficent data, when better measurements would have shown it to be untrue. For example, in the context of medical testing, if we consider the null hypothesis to be "This patient does not have the disease," a diagnosis that the disease is present when it is not is a Type I error, while a diagnosis that the patient does not have the disease when it is present would be a Type II error. The manner in which a null hypothesis frames contextually default expectations influences the specific ways in which type I errors and type II errors manifest, and this varies by context and application. Generally the risk of such errors cannot be entirely eliminated, only traded-off between the two types, by for example changing the significance


id: type-i-and-type-ii-errors-20260505-1
file: data/en.wikipedia.org/wiki/Type_I_and_type_II_errors-1.md
category: reference
title: Type I and type II errors
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Type_I_and_type_II_errors
date_saved: 2026-05-05T09:52:25.345633+00:00
instance: kb-cron
parent: type-i-and-type-ii-errors-20260505-0
summary:
=== The quality of hypothesis test === The same idea can be expressed in terms of the rate of correct results and therefore used to minimize error rates and improve the quality of hypothesis test. To reduce the probability of committing a type I error, making the alpha value more stringent is both simple and efficient. For example, setting the alpha value at 0.01, instead of 0.05. To decrease the probability of committing a type II error, which is closely associated with analyses' power, either increasing the test's sample size or relaxing the alpha level, ex. setting the alpha level to 0.1 instead of 0.05, could increase the analyses' power. A test statistic is robust if the type I error rate is controlled. Varying different threshold (cut-off) values could also be used to make the test either more specific or more sensitive, which in turn elevates the test quality. For example, imagine a medical test, in which an experimenter might measure the concentration of a certain protein in the blood sample. The experimenter could adjust the threshold (black vertical line in the figure) and people would be diagnosed as having diseases if any number is detected above this certain threshold.


id: type-i-and-type-ii-errors-20260505-2
file: data/en.wikipedia.org/wiki/Type_I_and_type_II_errors-2.md
category: reference
title: Type I and type II errors
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Type_I_and_type_II_errors
date_saved: 2026-05-05T09:52:25.345633+00:00
instance: kb-cron
parent: type-i-and-type-ii-errors-20260505-0
summary:
in testing hypotheses two considerations must be kept in view, we must be able to reduce the chance of rejecting a true hypothesis to as low a value as desired; the test must be so devised that it will reject the hypothesis tested when it is likely to be false. In 1933, they observed that these "problems are rarely presented in such a form that we can discriminate with certainty between the true and false hypothesis". They also noted that, in deciding whether to fail to reject, or reject a particular hypothesis amongst a "set of alternative hypotheses", H1, H2..., it was easy to make an error, [and] these errors will be of two kinds: In all of the papers co-written by Neyman and Pearson the expression H0 always signifies "the hypothesis to be tested". In the same paper they call these two sources of error, errors of type I and errors of type II respectively. == Related terms == === Null hypothesis === It is standard practice for statisticians to conduct tests in order to determine whether or not a "speculative hypothesis" concerning the observed phenomena of the world (or its inhabitants) can be supported. The results of such


id: type-i-and-type-ii-errors-20260505-3
file: data/en.wikipedia.org/wiki/Type_I_and_type_II_errors-3.md
category: reference
title: Type I and type II errors
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Type_I_and_type_II_errors
date_saved: 2026-05-05T09:52:25.345633+00:00
instance: kb-cron
parent: type-i-and-type-ii-errors-20260505-0
summary:
Hypothesis: "The newborns have phenylketonuria and hypothyroidism". Null hypothesis (H0): "The newborns do not have phenylketonuria and hypothyroidism". Type I error (false positive): The true fact is that the newborns do not have phenylketonuria and hypothyroidism but we consider they have the disorders according to the data. Type II error (false negative): The true fact is that the newborns have phenylketonuria and hypothyroidism but we consider they do not have the disorders according to the data. Although they display a high rate of false positives, the screening tests are considered valuable because they greatly increase the likelihood of detecting these disorders at a far earlier stage. The simple blood tests used to screen possible blood donors for HIV and hepatitis have a significant rate of false positives; however, physicians use much more expensive and far more precise tests to determine whether a person is actually infected with either of these viruses. Perhaps the most widely discussed false positives in medical screening come from the breast cancer screening procedure mammography. The US rate of false positive mammograms is up to 15%, the highest in world. One consequence of the high false positive rate in the US is that, in any 10-year


id: type-i-and-type-ii-errors-20260505-4
file: data/en.wikipedia.org/wiki/Type_I_and_type_II_errors-4.md
category: reference
title: Type I and type II errors
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Type_I_and_type_II_errors
date_saved: 2026-05-05T09:52:25.345633+00:00
instance: kb-cron
parent: type-i-and-type-ii-errors-20260505-0
summary:
Substantial scholarship dating back centuries discusses the grave consequences of miscarriages of justice in criminal proceedings, not only for the defendant, but for the perception of fairness of the entire judicial system and trust from community stakeholders that allegations of criminal activity will be heard seriously but also fairly. English jurist William Blackstone invented Blackstone's ratio of 10:1 to describe the concept that a fair system might let ten guilty defendants walk free rather than let more than one innocent person be jailed. In recent years, legal scholarship and mainstream jurisprudence has adopted the Type I and Type II error taxonomy to have a more rigorous vocabulary with which to discuss judicial mistakes and wrongful convictions. The U.S. Supreme Court used the Type I vs. Type II taxonomy in its discussion of errors in Ballew v. Georgia and judges and law professors increasingly use this dichotomous designation rather than the more casual "wrongly convicted" or "erroneously acquitted," which were terms used often in earlier scholarship. Recent scholarship and judicial concern often centers on the size and unanimity of juries as safeguards against Type I error (incorrectly convicting the innocent defendant). Small juries of less than twelve have been criticized by


id: up-and-down-design-20260505-0
file: data/en.wikipedia.org/wiki/Up-and-down_design-0.md
category: reference
title: Up-and-down design
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Up-and-down_design
date_saved: 2026-05-05T09:52:26.719636+00:00
instance: kb-cron
parent: up-and-down-design-20260505-0
summary:
Up-and-down designs (UDDs) are a family of statistical experiment designs used in dose-finding experiments in science, engineering, and medical research. Dose-finding experiments have binary responses: each individual outcome can be described as one of two possible values, such as success vs. failure or toxic vs. non-toxic. Mathematically the binary responses are coded as 1 and 0. The goal of dose-finding experiments is to estimate the strength of treatment (i.e., the "dose") that would trigger the "1" response a pre-specified proportion of the time. This dose can be envisioned as a percentile of the distribution of response thresholds. An example where dose-finding is used is in an experiment to estimate the LD50 of some toxic chemical with respect to mice. Dose-finding designs are sequential and response-adaptive: the dose at a given point in the experiment depends upon previous outcomes, rather than be fixed a priori. Dose-finding designs are generally more efficient for this task than fixed designs, but their properties are harder to analyze, and some require specialized design software. UDDs use a discrete set of doses rather than vary the dose continuously. They are relatively simple to implement, and are also among the best understood dose-finding designs. Despite this simplicity,


id: up-and-down-design-20260505-1
file: data/en.wikipedia.org/wiki/Up-and-down_design-1.md
category: reference
title: Up-and-down design
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Up-and-down_design
date_saved: 2026-05-05T09:52:26.719636+00:00
instance: kb-cron
parent: up-and-down-design-20260505-0
summary:
=== Transition probability matrix === Given that a subject receives dose d m {\displaystyle d_{m}} , denote the probability that the next subject receives dose d m − 1 , d m {\displaystyle d_{m-1},d_{m}} , or d m + 1 {\displaystyle d_{m+1}} , as p m , m − 1 , p m m {\displaystyle p_{m,m-1},p_{mm}} or p m , m + 1 {\displaystyle p_{m,m+1}} , respectively. These transition probabilities obey the constraints p m , m − 1 + p m m + p m , m + 1 = 1 {\displaystyle p_{m,m-1}+p_{mm}+p_{m,m+1}=1} and the boundary conditions p 1 , 0 = p M , M + 1 = 0 {\displaystyle p_{1,0}=p_{M,M+1}=0} . Each specific set of UDD rules enables the symbolic calculation of these probabilities, usually as a function of F ( x ) {\displaystyle F(x)} . Assuming that transition probabilities are fixed in time, depending only upon the current allocation and its outcome, i.e., upon ( X i , Y i ) {\displaystyle \left(X_{i},Y_{i}\right)} and through them upon F ( x ) {\displaystyle F(x)} (and possibly on a set of fixed parameters). The probabilities are then best represented via a tri-diagonal transition probability matrix (TPM) P {\displaystyle


id: up-and-down-design-20260505-2
file: data/en.wikipedia.org/wiki/Up-and-down_design-2.md
category: reference
title: Up-and-down design
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Up-and-down_design
date_saved: 2026-05-05T09:52:26.719636+00:00
instance: kb-cron
parent: up-and-down-design-20260505-0
summary:
X i + 1 = d m + 1 if Y i = 0 & 'heads' ; d m − 1 if Y _ i = 1 ; d m if Y i = 0 & 'tails' . {\displaystyle X_{i+1}={\begin{array}{ll}d_{m+1}&{\textrm {if}}\ \ Y_{i}=0\ \ \&\ \ {\textrm {'heads'}};\\d_{m-1}&{\textrm {if}}\ \ Y\_i=1;\\d_{m}&{\textrm {if}}\ \ Y_{i}=0\ \ \&\ \ {\textrm {'tails'}}.\\\end{array}}} The heads probability b {\displaystyle b} can take any value in [ 0 , 1 ] {\displaystyle [0,1]} . The balance point is b ( 1 − F ∗ ) = F ∗ F ∗ = b 1 + b ∈ [ 0 , 0.5 ] . {\displaystyle {\begin{array}{rcl}b\left(1-F^{*}\right)&=&F^{*}\\F^{*}&=&{\frac {b}{1+b}}\in [0,0.5].\end{array}}} The BCD balance point can made identical to a target rate F − 1 ( Γ ) {\displaystyle F^{-1}(\Gamma )} by setting the heads probability to b = Γ / ( 1 − Γ ) {\displaystyle b=\Gamma /(1-\Gamma )} . For example, for Γ = 0.3 {\displaystyle \Gamma =0.3} set b = 3 / 7 {\displaystyle b=3/7} . Setting b = 1 {\displaystyle b=1} makes this design identical to the classical UDD, and inverting the rules by imposing the coin toss upon positive rather than negative outcomes, produces above-median balance


id: up-and-down-design-20260505-3
file: data/en.wikipedia.org/wiki/Up-and-down_design-3.md
category: reference
title: Up-and-down design
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Up-and-down_design
date_saved: 2026-05-05T09:52:26.719636+00:00
instance: kb-cron
parent: up-and-down-design-20260505-0
summary:
Every dose escalation requires k {\displaystyle k} non-toxicities observed on consecutive data points, all at the current dose, while de-escalation only requires a single toxicity. It closely resembles GUD ( s , 0 , 1 ) {\displaystyle _{(s,0,1)}} described above, and indeed shares the same balance point. The difference is that k {\displaystyle k} -in-a-row can bail out of a dose level upon the first toxicity, whereas its group UDD sibling might treat the entire cohort at once, and therefore might see more than one toxicity before descending. The method used in sensory studies is actually the mirror-image of the one defined above, with k {\displaystyle k} successive responses required for a de-escalation and only one non-response for escalation, yielding F ∗ ≈ 0.707 , 0.794 , 0.841 , … {\displaystyle F^{*}\approx 0.707,0.794,0.841,\ldots } for k = 2 , 3 , 4 , … {\displaystyle k=2,3,4,\ldots } . k {\displaystyle k} -in-a-row generates a k {\displaystyle k} -th order random walk because knowledge of the last k {\displaystyle k} responses might be needed. It can be represented as a first-order chain with M k {\displaystyle Mk} states, or as a Markov chain with M {\displaystyle M} levels, each having k


id: vaccine-trial-20260505-0
file: data/en.wikipedia.org/wiki/Vaccine_trial-0.md
category: reference
title: Vaccine trial
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Vaccine_trial
date_saved: 2026-05-05T09:52:27.963512+00:00
instance: kb-cron
parent: vaccine-trial-20260505-0
summary:
A vaccine trial is a clinical trial that aims at establishing the safety and efficacy of a vaccine prior to it being licensed. A vaccine candidate drug is first identified through preclinical evaluations that could involve high throughput screening and selecting the proper antigen to invoke an immune response. Some vaccine trials may take months or years to complete, depending on the time required for the subjects to react to the vaccine and develop the required antibodies. == Preclinical stage == Preclinical development stages are necessary to determine the immunogenicity potential and safety profile for a vaccine candidate. This is also the stage in which the drug candidate may be first tested in laboratory animals prior to moving to the Phase I trials. Vaccines such as the oral polio vaccine have been first tested for adverse effects and immunogenicity in monkeys as well as non-human primates and lab mice. Scientific advances since the 1980s have helped to use transgenic animals as a part of vaccine preclinical protocol in hopes to more accurately determine drug reactions in humans. Understanding vaccine safety and the immunological response to the vaccine, such as toxicity, are necessary components of the preclinical stage. Other drug trials


id: variable-and-attribute-research-20260505-0
file: data/en.wikipedia.org/wiki/Variable_and_attribute_(research)-0.md
category: reference
title: Variable and attribute (research)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Variable_and_attribute_(research)
date_saved: 2026-05-05T09:52:29.092443+00:00
instance: kb-cron
parent: variable-and-attribute-research-20260505-0
summary:
In science and research, an attribute is a quality of an object (person, thing, etc.). Attributes are closely related to variables. A variable is a logical set of attributes. Variables can "vary" – for example, be high or low. How high, or how low, is determined by the value of the attribute (and in fact, an attribute could be just the word "low" or "high"). (For example see: Binary option) While an attribute is often intuitive, the variable is the operationalized way in which the attribute is represented for further data processing. In data processing data are often represented by a combination of items (objects organized in rows), and multiple variables (organized in columns). Values of each variable statistically "vary" (or are distributed) across the variable's domain. A domain is a set of all possible values that a variable is allowed to have. The values are ordered in a logical way and must be defined for each variable. Domains can be bigger or smaller. The smallest possible domains have those variables that can only have two values, also called binary (or dichotomous) variables. Bigger domains have non-dichotomous variables and the ones with a higher level of measurement. (See also domain


id: wait-list-control-group-20260505-0
file: data/en.wikipedia.org/wiki/Wait_list_control_group-0.md
category: reference
title: Wait list control group
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Wait_list_control_group
date_saved: 2026-05-05T09:52:30.304780+00:00
instance: kb-cron
parent: wait-list-control-group-20260505-0
summary:
A wait list control group, also called a wait list comparison, is a group of participants included in an outcome study that is assigned to a waiting list and receives intervention after the active treatment group. This control group serves as an untreated comparison group during the study, but eventually goes on to receive treatment at a later date. Wait list control groups are often used when it would be unethical to deny participants access to treatment, provided the wait is still shorter than that for routine services. == See also == Experimental design Scientific control Treatment and control groups == References ==


id: weighing-matrix-20260505-0
file: data/en.wikipedia.org/wiki/Weighing_matrix-0.md
category: reference
title: Weighing matrix
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Weighing_matrix
date_saved: 2026-05-05T09:52:31.632857+00:00
instance: kb-cron
parent: weighing-matrix-20260505-0
summary:
In mathematics, a weighing matrix of order n {\displaystyle n} and weight w {\displaystyle w} is a matrix W {\displaystyle W} with entries from the set { 0 , 1 , − 1 } {\displaystyle \{0,1,-1\}} such that W W T = w I n , {\displaystyle WW^{\mathsf {T}}=wI_{n},} where W T {\displaystyle W^{\mathsf {T}}} is the transpose of W {\displaystyle W} and I n {\displaystyle I_{n}} is the identity matrix of order n {\displaystyle n} . The weight w {\displaystyle w} is also called the degree of the matrix. For convenience, a weighing matrix of order n {\displaystyle n} and weight w {\displaystyle w} may be denoted by W ( n , w ) {\displaystyle W(n,w)} . Weighing matrices are so called because of their use in optimally measuring the individual weights of multiple objects. When the weighing device is a balance scale, the statistical variance of the measurement can be minimized by weighing multiple objects at once, including some objects in the opposite pan of the scale where they subtract from the measurement. == Properties == Some properties are immediate from the definition. If W {\displaystyle W} is a W ( n , w ) {\displaystyle W(n,w)} ,


id: weighing-matrix-20260505-1
file: data/en.wikipedia.org/wiki/Weighing_matrix-1.md
category: reference
title: Weighing matrix
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Weighing_matrix
date_saved: 2026-05-05T09:52:31.632857+00:00
instance: kb-cron
parent: weighing-matrix-20260505-0
summary:
This constraint is also equivalent to the rows of X {\displaystyle X} being orthogonal and each row having exactly s i {\displaystyle s_{i}} occurrences of x i {\displaystyle x_{i}} . An orthogonal design can be denoted as O D ( n ; s 1 , … , s u ) {\displaystyle \mathrm {OD} (n;s_{1},\dots ,s_{u})} . An orthogonal design of one variable is a weighing matrix, and so the two fields of study are connected. Because of this connection, new orthogonal designs can be discovered by way of weighing matrices. == Examples == Note that when weighing matrices are displayed, the symbol − {\displaystyle -} is used to represent −1. Here are some examples: This is a W ( 2 , 2 ) {\displaystyle W(2,2)} : ( 1 1 1 − ) {\displaystyle {\begin{pmatrix}1&1\\1&-\end{pmatrix}}} This is a W ( 4 , 3 ) {\displaystyle W(4,3)} : ( 1 1 1 0 1 − 0 1 1 0 − − 0 1 − 1 ) {\displaystyle {\begin{pmatrix}1&1&1&0\\1&-&0&1\\1&0&-&-\\0&1&-&1\end{pmatrix}}} This is a W ( 7 , 4 ) {\displaystyle W(7,4)} : ( 1 1 1 1 0 0 0 1 − 0 0 1 1 0 1 0 − 0 − 0 1


id: weighing-matrix-20260505-2
file: data/en.wikipedia.org/wiki/Weighing_matrix-2.md
category: reference
title: Weighing matrix
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Weighing_matrix
date_saved: 2026-05-05T09:52:31.632857+00:00
instance: kb-cron
parent: weighing-matrix-20260505-0
summary:
If n ≡ 2 ( mod 4 ) {\displaystyle n\equiv 2{\pmod {4}}} then there exists a W ( n , w ) {\displaystyle W(n,w)} if and only if w < n − 1 {\displaystyle w<n-1} is the sum of two integer squares. If n ≡ 0 ( mod 4 ) {\displaystyle n\equiv 0{\pmod {4}}} then there exists a W ( n , w ) {\displaystyle W(n,w)} for each w < n {\displaystyle w<n} . If n ≡ 4 ( mod 8 ) {\displaystyle n\equiv 4{\pmod {8}}} then there exists an orthogonal design O D ( n ; 1 , 1 ) {\displaystyle \mathrm {OD} (n;1,1)} for all k < n {\displaystyle k<n} where k {\displaystyle k} is the sum of three integer squares. If n ≡ 0 ( mod 8 ) {\displaystyle n\equiv 0{\pmod {8}}} then there exists an orthogonal design O D ( n ; 1 , k ) {\displaystyle \mathrm {OD} (n;1,k)} for all k < n {\displaystyle k<n} . If n ≡ 2 ( mod 4 ) {\displaystyle n\equiv 2{\pmod {4}}} then there exists an orthogonal design O D ( n ; 1 , k ) {\displaystyle \mathrm {OD} (n;1,k)} for all k < n − 1


id: wike-s-law-of-low-odd-primes-20260505-0
file: data/en.wikipedia.org/wiki/Wike's_law_of_low_odd_primes-0.md
category: reference
title: Wike's law of low odd primes
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Wike's_law_of_low_odd_primes
date_saved: 2026-05-05T09:52:32.763388+00:00
instance: kb-cron
parent: wike-s-law-of-low-odd-primes-20260505-0
summary:
Wike's law of low odd primes is a methodological principle to help design experiments in psychology. It is: "If the number of experimental treatments is a low odd prime number, then the experimental design is unbalanced and partially confounded" (Wike, 1973, pp. 192–193). This law was stated by Edwin Wike in a humorous article in which he also admits that the association of his name with the law is an example of Stigler's law of eponymy. The lowest odd prime number is three. Wike illustrates how this yields an unbalanced design with an invented study in which researchers investigated the effects of water beds on sexual satisfaction. The fictitious researchers randomly assigned couples to three groups: those having sex on a conventional bed, those having sex on a water bed, and those having sex on a water bed having also taken a sea sickness pill. Wike pointed out that any differences in sexual satisfaction among the three groups could be due to the water bed or to the sea sickness pill. It requires a fourth group, couples taking the pill and using a conventional bed, to balance the design and to allow the researchers to attribute any differences in sexual


id: wizard-of-oz-experiment-20260505-0
file: data/en.wikipedia.org/wiki/Wizard_of_Oz_experiment-0.md
category: reference
title: Wizard of Oz experiment
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Wizard_of_Oz_experiment
date_saved: 2026-05-05T09:52:33.981596+00:00
instance: kb-cron
parent: wizard-of-oz-experiment-20260505-0
summary:
In the field of human–computer interaction, a Wizard of Oz experiment is a research experiment in which subjects interact with a computer system that subjects believe to be autonomous, but that is actually operated or partially operated by an unseen human being. == Concept == The phrase Wizard of Oz (originally OZ Paradigm) has come into common usage in the fields of experimental psychology, human factors, ergonomics, linguistics, and usability engineering to describe a specific type of testing or iterative design. In such an experiment, a laboratory experimenter (the "wizard") simulates the behavior of a theoretical intelligent computer application, often by going into another room and intercepting all communications between participant and system. Sometimes this is done without the participant's prior knowledge, to manage the participant's expectations and encourage natural behaviors, while at other times the participant is aware. For example, a test participant may think that he is communicating with a computer using a speech interface, when the wizard is actually covertly entering the participant's words into the computer, enabling them to be processed as a text stream, rather than as an audio stream. The missing system functionality that the wizard provides may be implemented in later versions of


id: wizard-of-oz-experiment-20260505-1
file: data/en.wikipedia.org/wiki/Wizard_of_Oz_experiment-1.md
category: reference
title: Wizard of Oz experiment
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Wizard_of_Oz_experiment
date_saved: 2026-05-05T09:52:33.981596+00:00
instance: kb-cron
parent: wizard-of-oz-experiment-20260505-0
summary:
== Significance == The Wizard of OZ method is very powerful. Originally, Kelley created a simple keyboard-input natural language recognition system that far exceeded the recognition rates of any of the far more complex systems of the day. Contemporary computer scientists and linguists thought that, in order for a computer to be able to "understand" natural language enough to be able to assist in useful tasks, the software would have to be attached to a formidable "dictionary" having a large number of categories for each word. The categories would enable a very complex parsing algorithm to unravel the ambiguities inherent in naturally produced language. The daunting task of creating such a dictionary led many to believe that computers simply would never truly "understand" language until they could be "raised" and "experience life" as humans, since humans seem to apply a life's worth of experiences to the interpretation of language. The key enabling factor for the first use of the OZ method was that the system was designed to work in a single context (calendar-keeping), which greatly constrained user input so greatly that a simple language processing model could meet the goals of the application. The processing model was a two-pass


id: yates-analysis-20260505-0
file: data/en.wikipedia.org/wiki/Yates_analysis-0.md
category: reference
title: Yates analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Yates_analysis
date_saved: 2026-05-05T09:52:35.174477+00:00
instance: kb-cron
parent: yates-analysis-20260505-0
summary:
In statistics, a Yates analysis is an approach to analyzing data obtained from a designed experiment where a factorial design has been used. Full- and fractional-factorial designs are common in designed experiments for engineering and scientific applications. In these designs, each factor is assigned two levels, typically called the low and high levels and referred to as "-" and "+". For computational purposes the factors are scaled so that the low level is assigned a value of -1 and the high level is assigned a value of +1. A full factorial design contains all possible combinations of low/high levels for all the factors. A fractional factorial design contains a carefully chosen subset of these combinations. The criterion for choosing the subsets is discussed in detail in the fractional factorial designs article. Formalized by Frank Yates, a Yates analysis exploits the special structure of these designs to generate least squares estimates for factor effects for all factors and all relevant interactions. The Yates analysis can be used to answer the following questions: What is the ranked list of factors? What is the goodness-of-fit (as measured by the residual standard deviation) for the various models? The mathematical details of the Yates analysis


id: yates-analysis-20260505-1
file: data/en.wikipedia.org/wiki/Yates_analysis-1.md
category: reference
title: Yates analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Yates_analysis
date_saved: 2026-05-05T09:52:35.174477+00:00
instance: kb-cron
parent: yates-analysis-20260505-0
summary:
where e is the estimated factor effect and se is the standard deviation of the estimated factor effect. The residual standard deviation that results from the model with the single term only. That is, the residual standard deviation from the model response = constant + 0.5 X i {\displaystyle {\textrm {response}}={\textrm {constant}}+0.5X_{i}} where Xi is the estimate of the ith factor or interaction effect. The cumulative residual standard deviation that results from the model using the current term plus all terms preceding that term. That is, response = constant + 0.5 ( a l l e f f e c t e s t i m a t e s d o w n t o a n d i n c l u d i n g t h e e f f e c t o f i n t e r e s t ) {\displaystyle {\textrm {response}}={\textrm {constant}}+0.5\mathrm {(all\ effect\ estimates\ down\ to\ and\ including\ the\ effect\ of\ interest)} } This consists of a monotonically decreasing set of residual standard deviations (indicating a better fit as the number of terms in the model increases). The first cumulative residual standard deviation is for the model response =


id: yates-analysis-20260505-2
file: data/en.wikipedia.org/wiki/Yates_analysis-2.md
category: reference
title: Yates analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Yates_analysis
date_saved: 2026-05-05T09:52:35.174477+00:00
instance: kb-cron
parent: yates-analysis-20260505-0
summary:
== Model selection and validation == From the above Yates output, one can define the potential models from the Yates analysis. An important component of a Yates analysis is selecting the best model from the available potential models. The above step lists all the potential models. From this list, we want to select the most appropriate model. This requires balancing the following two goals. We want the model to include all important factors. We want the model to be parsimonious. That is, the model should be as simple as possible. In short, we want our model to include all the important factors and interactions and to omit the unimportant factors and interactions. Note that the residual standard deviation alone is insufficient for determining the most appropriate model as it will always be decreased by adding additional factors. Instead, seven criteria are utilized to define important factors. These seven criteria are not all equally important, nor will they yield identical subsets, in which case a consensus subset or a weighted consensus subset must be extracted. In practice, some of these criteria may not apply in all situations, and some analysts may have additional criteria. These criteria are given as useful guidelines.


id: yoked-control-design-20260505-0
file: data/en.wikipedia.org/wiki/Yoked_control_design-0.md
category: reference
title: Yoked control design
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Yoked_control_design
date_saved: 2026-05-05T09:52:36.352286+00:00
instance: kb-cron
parent: yoked-control-design-20260505-0
summary:
A yoked control design is a research design used in experiments in which matched research subjects are yoked (joined together) by receiving the same stimuli or conditions. In operant conditioning the yoked subject receives the same treatment in terms of reinforcement or punishment. Yoked control designs are used in a variety of scientific disciplines, including learning sciences, social psychology, and psychophysiology. == Example == In his research into the name-letter effect, Belgian experimental psychologist Jozef Nuttin created a yoked control design in which two subjects evaluated the same letters, separately. Some of the letters belonged to one subject's name, and some of the letters belonged to the other subject's name, while some were random. Crucially, in the experimental design subjects are not aware that they are choosing letters from their name. In this design any difference in preference between subjects would have to be based on whether the letter occurred in their name. For example, for the fictitious pair Irma Maes and Jef Jacobs the first stimulus was A and U: the last letter in Irma's first name and a letter not in her name. Both subjects had to circle the letter they preferred. The next stimulus was M and


id: zelen-s-design-20260505-0
file: data/en.wikipedia.org/wiki/Zelen's_design-0.md
category: reference
title: Zelen's design
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Zelen's_design
date_saved: 2026-05-05T09:52:37.475463+00:00
instance: kb-cron
parent: zelen-s-design-20260505-0
summary:
Zelen's design is an experimental design for randomized clinical trials proposed by Harvard School of Public Health statistician Marvin Zelen (1927-2014). In this design, patients are randomized to either the treatment or control group before giving informed consent. Because the group to which a given patient is assigned is known, consent can be sought conditionally. == Overview == In this design, those patients receiving standard care need not be consented for participation in the study other than possibly for privacy issues. On the other hand, those patients randomized to the experimental group are consented as usual, except that they are consenting to the certainty of receiving the experimental treatment only; alternatively these patients can decline and receive the standard treatment instead. In comparison, the current predominant design is for consent to be solicited prior to randomization. That is, eligible patients are asked if they would agree to participate in the clinical trial as a whole. This entails agreeing to receiving the experimental treatment as a possibility, receiving the control treatment as a possibility, and the uncertainty involved in not knowing. == Statistical and epidemiological issues == There are a number of advantages conferred by the post-randomization consent design. Clinicians are


id: list-of-metascience-research-centers-and-organisations-20260505-0
file: data/en.wikipedia.org/wiki/List_of_metascience_research_centers_and_organisations-0.md
category: reference
title: List of metascience research centers and organisations
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/List_of_metascience_research_centers_and_organisations
date_saved: 2026-05-05T09:52:39.032836+00:00
instance: kb-cron
parent: list-of-metascience-research-centers-and-organisations-20260505-0
summary:
Association for Interdisciplinary Meta-Research & Open Science (AIMOS) Brazilian Metascience Research Group (BMRG) Centre for Journalology Center for Open Science Center for Scientific Integrity Cochrane Collaboration European Network for Innovation and Knowledge Evidence-Based Research Network (EBRNetwork) Interdisciplinary Meta-Research Group at The University of Melbourne, Australia Meta-Research Center at Tilburg University Meta-Research Innovation Center at Stanford (METRICS) NYU Meta-Research Collaborative Projet MiRoR Methods in Research on Research QUEST Center for Responsible Research of the Berlin Institute of Health UCMeta at the University of Canterbury Severance Underwood Meta-Research Center at Yonsei University


id: center-for-open-science-20260505-0
file: data/en.wikipedia.org/wiki/Center_for_Open_Science-0.md
category: reference
title: Center for Open Science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Center_for_Open_Science
date_saved: 2026-05-05T09:52:41.470257+00:00
instance: kb-cron
parent: center-for-open-science-20260505-0
summary:
The Center for Open Science is a non-profit technology organization based in Charlottesville, Virginia with a mission to "increase the openness, integrity, and reproducibility of scientific research." Brian Nosek and Jeffrey Spies founded the organization in January 2013, funded mainly by the Laura and John Arnold Foundation and others. The organization began with work in reproducibility of psychology research, with the large-scale initiative Reproducibility Project: Psychology. A second reproducibility project for cancer biology research has also been started through a partnership with Science Exchange. In March 2017, the Center published a detailed strategic plan. Brian Nosek posted a letter outlining the history of the Center and future directions. In 2020, the Center received a grant from Fast Grants to promote the publication of COVID-19 research on the platform. In 2021, the Center for Open Science was honored with the Einstein Foundation Award for Promoting Quality in Research in the institutional category for their contribution to fostering research integrity and to improving transparency and accessibility. == Open Science Framework == === Reproducibility project === The Open Science Framework (OSF) is an open source software project that facilitates open collaboration in science research. The framework was initially used to work on a


id: meta-research-center-at-tilburg-university-20260505-0
file: data/en.wikipedia.org/wiki/Meta-Research_Center_at_Tilburg_University-0.md
category: reference
title: Meta-Research Center at Tilburg University
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Meta-Research_Center_at_Tilburg_University
date_saved: 2026-05-05T09:52:42.734220+00:00
instance: kb-cron
parent: meta-research-center-at-tilburg-university-20260505-0
summary:
The Meta-Research Center at Tilburg University is a metascience research center within the School of Social and Behavioral Sciences at the Dutch Tilburg University. They were profiled in a September 2018 article in Science. == Research == Meta-research aims to improve reproducibility by studying how science is practiced and published and developing better ways for the scientific community to operate. The research institute has published a large statistical meta-analysis of studies on the effect of Stereotype threat on girls' mathematics performance. They also use methods for estimating publication bias. The research institute has developed an R based software tool called Statcheck that can detect incorrect statistical methods used in research articles. A large amount of statistical errors were detected in a sample of 50 000 psychology research articles. The use of it was perceived negatively by some of the researchers. The data mining practices of the research center have been in conflict with the policies of scientific publisher Elsevier. A scientific misconduct case in the field of social psychology at Tilburg University has been a contributing factor in establishing the research center. == Advocacy == The research center makes recommendations for other researchers about how to avoid publication bias and


id: meta-research-innovation-center-at-stanford-20260505-0
file: data/en.wikipedia.org/wiki/Meta-Research_Innovation_Center_at_Stanford-0.md
category: reference
title: Meta-Research Innovation Center at Stanford
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Meta-Research_Innovation_Center_at_Stanford
date_saved: 2026-05-05T09:52:43.947789+00:00
instance: kb-cron
parent: meta-research-innovation-center-at-stanford-20260505-0
summary:
The Meta-Research Innovation Center at Stanford (METRICS) is a research center within the Stanford School of Medicine that aims to improve reproducibility by studying how science is practiced and published and developing better ways for the scientific community to operate. It is headed by John Ioannidis and Steven Goodman. Laura and John Arnold Foundation provided the initial founding of the center, which launched in 2014. Ioannidis' past work that led to the creation of METRICS has been covered twice in The New York Times. == See also == Cochrane Collaboration List of metascience research centers and organisations Meta-research Scientometrics == References == == External links == Official website


id: peer-review-20260505-0
file: data/en.wikipedia.org/wiki/Peer_review-0.md
category: reference
title: Peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Peer_review
date_saved: 2026-05-05T09:52:45.428893+00:00
instance: kb-cron
parent: peer-review-20260505-0
summary:
Peer review is the evaluation of work by one or more people with similar competencies as the producers of the work (peers). It functions as a form of self-regulation by qualified members of a profession within the relevant field. Peer review methods are used to maintain quality standards, improve performance, and provide credibility. In academia, scholarly peer review is typically used to determine an academic paper's suitability for publication. The reviewers are experts in the topic at hand and they have no connection to the author (they are not told the name of the author). They are anonymous and cannot be pressured. Top journals reject over 90% of submitted papers. Peer review can be categorized by the type and by the field or profession in which the activity occurs, e.g., medical peer review. It can also be used as a teaching tool to help students improve writing assignments. Henry Oldenburg (1619–1677) was a German-born British philosopher who is seen as the 'father' of modern scientific peer review. It developed over the following centuries with, for example, the journal Nature making it standard practice in 1973. The term "peer review" was first used in the early 1970s. A monument to peer


id: peer-review-20260505-1
file: data/en.wikipedia.org/wiki/Peer_review-1.md
category: reference
title: Peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Peer_review
date_saved: 2026-05-05T09:52:45.428893+00:00
instance: kb-cron
parent: peer-review-20260505-0
summary:
Peer review, or student peer assessment, is the method by which editors and writers work together in hopes of helping the author establish and further flesh out and develop their own writing. Peer review is widely used in secondary and post-secondary education as part of the writing process. This collaborative learning tool involves groups of students reviewing each other's work and providing feedback and suggestions for revision. Rather than a means of critiquing each other's work, peer review is often framed as a way to build connection between students and help develop writers' identity. While widely used in English and composition classrooms, peer review has gained popularity in other disciplines that require writing as part of the curriculum including the social and natural sciences. The concept of peer review has been extended to other practices, including the use of visual peer review for evaluating peer-produced data visualizations. Peer review in classrooms helps students become more invested in their work, and the classroom environment at large. Understanding how their work is read by a diverse readership before it is graded by the teacher may also help students clarify ideas and understand how to persuasively reach different audience members via their writing.


id: peer-review-20260505-2
file: data/en.wikipedia.org/wiki/Peer_review-2.md
category: reference
title: Peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Peer_review
date_saved: 2026-05-05T09:52:45.428893+00:00
instance: kb-cron
parent: peer-review-20260505-0
summary:
A particular concern in peer review is "role duality" as people are in parallel in the role of being an evaluator and being evaluated. Research illustrates that taking on both roles in parallel biases people in their role as evaluators as they engage in strategic actions to increase the chance of being evaluated positively themselves. The editorial peer review process has been found to be strongly biased against 'negative studies,' i.e. studies that do not work. This then biases the information base of medicine. Journals become biased against negative studies when values come into play. "Who wants to read something that doesn't work?" asks Richard Smith in the Journal of the Royal Society of Medicine. "That's boring." Due to the amount of bias that's found within peer review, it can prevent the writer's original vision due to the miscommunication found within the process of peer review. Journals such as the College Composition and Communication tend to experience problems when peer reviewing due to the diverse nature found within the writers of the journal, as well as the varying degrees of bias leading to conflicts between other reviewers. Teachers as well have expressed disdain in peer review, with plenty of them


id: peer-review-20260505-3
file: data/en.wikipedia.org/wiki/Peer_review-3.md
category: reference
title: Peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Peer_review
date_saved: 2026-05-05T09:52:45.428893+00:00
instance: kb-cron
parent: peer-review-20260505-0
summary:
== Comparison and improvement == Magda Tigchelaar compares peer review with self-assessment through an experiment that divided students into three groups: self-assessment, peer review, and no review. Across four writing projects, she observed changes in each group, with surprising results showing significant improvement only in the self-assessment group. The author's analysis suggests that self-assessment allows individuals to clearly understand the revision goals at each stage, as the author is the most familiar with their writing. Thus, self-checking naturally follows a systematic and planned approach to revision. In contrast, the effectiveness of peer review is often limited due to the lack of structured feedback, characterized by scattered, meaningless summaries and evaluations that fail to meet the author's expectations for revising their work. Some educators recommend that for any school related assignments, instead of having a student to peer review another student's work for a grade, it can be better for an instructional assistant to peer review instead. Since instructional assistants tend to have more experience in writing, as well as giving them enough time to discuss their ideas for the paper with, it would allow for a more valid review of their draft and be less varying when it comes to


id: academic-journal-20260505-0
file: data/en.wikipedia.org/wiki/Academic_journal-0.md
category: reference
title: Academic journal
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Academic_journal
date_saved: 2026-05-05T09:52:46.563930+00:00
instance: kb-cron
parent: academic-journal-20260505-0
summary:
An academic journal or scholarly journal is a periodical publication in which scholarship relating to a particular academic discipline is published. They serve as permanent and transparent forums for the dissemination, scrutiny, and discussion of research. Unlike professional magazines or trade magazines, the articles are mostly written by researchers rather than staff writers employed by the journal. They nearly universally require peer review for research articles or other scrutiny from contemporaries competent and established in their respective fields. Academic journals trace their origins back to the 17th century, with the Philosophical Transactions of the Royal Society being established in 1665 as the first scientific journal. As of 2012, it is estimated that over 28,100 active academic journals are in publication, with scopes ranging from the general sciences, as seen in journals like Science and Nature, to highly specialized fields. These journals publish a variety of articles including original research, review articles, and perspectives. The advent of electronic publishing has made academic journals more accessible. == Content == Content usually takes the form of articles presenting original research, review articles, or book reviews. The purpose of an academic journal, according to Henry Oldenburg (the first editor of Philosophical Transactions of the


id: academic-journal-20260505-1
file: data/en.wikipedia.org/wiki/Academic_journal-1.md
category: reference
title: Academic journal
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Academic_journal
date_saved: 2026-05-05T09:52:46.563930+00:00
instance: kb-cron
parent: academic-journal-20260505-0
summary:
There are two kinds of article or paper submissions in academia: solicited, where an individual has been invited to submit work either through direct contact or through a general submissions call, and unsolicited, where an individual submits a work for potential publication without directly being asked to do so. Upon receipt of a submitted article, editors at the journal determine whether to reject the submission outright or begin the process of peer review. In the latter case, the submission becomes subject to review by outside scholars of the editor's choosing who typically remain anonymous. The number of these peer reviewers (or "referees") varies according to each journal's editorial practice – typically, no fewer than two, though sometimes three or more, experts in the subject matter of the article produce reports upon the content, style, and other factors, which inform the editors' publication decisions. Though these reports are generally confidential, some journals and publishers also practice public peer review. The editors either choose to reject the article, ask for a revision and resubmission, or accept the article for publication. Even accepted articles are often subjected to further (sometimes considerable) editing by journal editorial staff before they appear in print. The peer


id: academic-journal-20260505-2
file: data/en.wikipedia.org/wiki/Academic_journal-2.md
category: reference
title: Academic journal
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Academic_journal
date_saved: 2026-05-05T09:52:46.563930+00:00
instance: kb-cron
parent: academic-journal-20260505-0
summary:
Review articles, also called "reviews of progress", are checks on the research published in journals. Some journals are devoted entirely to review articles, some contain a few in each issue, and others do not publish review articles. Such reviews often cover the research from the preceding year, some for longer or shorter terms; some are devoted to specific topics, some to general surveys. Some reviews are enumerative, listing all significant articles in a given subject; others are selective, including only what they think worthwhile. Yet others are evaluative, judging the state of progress in the subject field. Some journals are published in series, each covering a complete subject field year, or covering specific fields through several years. Unlike original research articles, review articles tend to be solicited or "peer-invited" submissions, often planned years in advance, which may themselves go through a peer-review process once received. They are typically relied upon by students beginning a study in a given field, or for current awareness of those already in the field. === Book reviews === Reviews of scholarly books are checks upon the research books published by scholars; unlike articles, book reviews tend to be solicited. Journals typically have a separate book


id: academic-journal-20260505-3
file: data/en.wikipedia.org/wiki/Academic_journal-3.md
category: reference
title: Academic journal
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Academic_journal
date_saved: 2026-05-05T09:52:46.563930+00:00
instance: kb-cron
parent: academic-journal-20260505-0
summary:
Many academic journals are subsidized by universities or professional organizations, and do not exist to make a profit. They often accept advertising, page and image charges from authors to pay for production costs. On the other hand, some journals are produced by commercial publishers who do make a profit by charging subscriptions to individuals and libraries. They may also sell all of their journals in discipline-specific collections or a variety of other packages. Many scientists and librarians have long protested these costs, especially as they see these payments going to large for-profit publishing houses. To allow their researchers online access to journals, many universities purchase site licenses, permitting access from anywhere in the university, and, with appropriate authorization, by university-affiliated users at home or elsewhere. These may be much more expensive than the cost for a print subscription. Despite the transition to electronic publishing, the costs of site licenses continue to rise relative to universities' budgets. This is known as the serials crisis. Journal editors tend to have other professional responsibilities, most often as teaching professors. In the case of the largest journals, there are paid staff assisting in the editing. The production of the journals is almost always done


id: academic-journal-20260505-4
file: data/en.wikipedia.org/wiki/Academic_journal-4.md
category: reference
title: Academic journal
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Academic_journal
date_saved: 2026-05-05T09:52:46.563930+00:00
instance: kb-cron
parent: academic-journal-20260505-0
summary:
The Internet has revolutionized the production of, and access to, academic journals, with their contents available online via services subscribed to by academic libraries. Individual articles are subject-indexed in databases such as Google Scholar. Some of the smallest, most specialized journals are prepared in-house, by an academic department, and published only online – this has sometimes been in the blog format, though some, like the open access journal Internet Archaeology, use the medium to embed searchable datasets, 3D models, and interactive mapping. Currently, there is a movement in higher education encouraging open access, either via self archiving, whereby the author deposits a paper in a disciplinary or institutional repository where it can be searched for and read, or via publishing it in a free open access journal, which does not charge for subscriptions, being either subsidized or financed by a publication fee. Given the goal of sharing scientific research to speed advances, open access has affected science journals more than humanities journals. Commercial publishers are experimenting with open access models, but are trying to protect their subscription revenues. Academic journals have been criticized for persistent gender, geographic, and other representation imbalances on editorial boards. === Predatory and junk journals ===


id: african-peer-review-mechanism-20260505-0
file: data/en.wikipedia.org/wiki/African_Peer_Review_Mechanism-0.md
category: reference
title: African Peer Review Mechanism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/African_Peer_Review_Mechanism
date_saved: 2026-05-05T09:52:47.737196+00:00
instance: kb-cron
parent: african-peer-review-mechanism-20260505-0
summary:
The African Peer Review Mechanism (APRM) is a mutually agreed instrument voluntarily acceded to by the member states of the African Union (AU) as a self-monitoring mechanism. The APRM was launched on 9 March 2003 by the NEPAD Heads of State and Government Implementation Committee (HSGIC) in Abuja, Nigeria (NEPAD/HSGIC/03-2003/APRM/MOU (9 March 2003), Assembly Decision 198 (XI), Decision 527 (XXIII) and Decision Ext/Assembly/AU/Dec.1-4(XI); The APRM is an African-owned and African-led platform for self-assessment, peer-learning, and experience-sharing in democracy and good governance, in full respect for democratic principles, human rights, rule of law, the acceleration of political, social and economic integration in Africa; == The Mandate == The mandate of the APRM is to encourage conformity with regards to political, economic and corporate governance values, codes and standards, among African countries and the objectives in socio-economic development as well as to ensure monitoring and evaluation of AU Agenda 2063 and SDGs 2030. The mandate of the APRM is to ensure that policies and practices of participating Member States conform to the agreed political, economic and corporate governance values, codes and standards contained in the African Union Declaration on Democracy, Political, Economic and Corporate Governance. As a voluntary self-monitoring instrument, APRM fosters


id: african-peer-review-mechanism-20260505-1
file: data/en.wikipedia.org/wiki/African_Peer_Review_Mechanism-1.md
category: reference
title: African Peer Review Mechanism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/African_Peer_Review_Mechanism
date_saved: 2026-05-05T09:52:47.737196+00:00
instance: kb-cron
parent: african-peer-review-mechanism-20260505-0
summary:
== What happens after the country review == The National Programme of Action (NPoA) is divided into short-term, medium-term and long-term goals and is continuously monitored by the National Governance Commission/Governing Council, or a smaller body of state and non-state representatives. Progress Reports on implementation are presented annually to the APR Forum. The APR Secretariat follows up on commitments made, holds regional workshops to share best practices identified in the reviews, and offers technical support to fulfill APRM plans. == APRM Structures == === APR FORUM === (Committee of Participating Heads of State and Government) Highest decision-making authority. APR PANEL (Panel of Eminent Persons) Oversees the review process to ensure its independence, professionalism and credibility, and reports to the Forum. The APR Panel is also responsible for selecting and appointing and the Review Teams. COMMITTEE OF FOCAL POINTS Committee of representatives of Heads of State and Government Manages the budgetary process, resource mobilisation through Member States, Strategic and Development Partners, and the APRM Trust Fund and Audit. National Governing Council (NGC) The National Governance Commission/National Governing Council (NGC) is the body that oversees implementation of the APRM process at the Member State level. In addition to providing guidance in terms


id: african-theological-studies-20260505-0
file: data/en.wikipedia.org/wiki/African_Theological_Studies-0.md
category: reference
title: African Theological Studies
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/African_Theological_Studies
date_saved: 2026-05-05T09:52:48.901352+00:00
instance: kb-cron
parent: african-theological-studies-20260505-0
summary:
African Theological Studies (Études Théologiques Africaines) is a book series published by Peter Lang Verlagsgruppe under the ISSN number 2196-0615. It includes, as of 2021, a total of 22 volumes written in English and French. Some editors have changed over the years since 2013, when the series was founded, but the University of Würzburg Professor Gerhard Droesser was a founding editor and serves as an editor still. The co-editors are currently Esther Hornung and Frédéric Fungula Kwilu. Chibueze Clement Udeani has served as the editor of some volumes. Many of the monographs were originally submitted as dissertations at the Catholic Theology Faculty of the University of Würzburg. According to the title pages of each volume, the series is peer-reviewed. == Scope of the Series == The editors of the series seek to promote "the characteristics of African theology and its independence". Many of the authors are from Nigeria; almost all of them are from Africa. Bernhard Dinkelaker's monograph was a successful book in the series, since it was a European reception of the work of the African theologian Kwame Bediako, important to African but not that well-known in Europe and North America. The book was well-received by reviewer Kees van


id: african-theological-studies-20260505-1
file: data/en.wikipedia.org/wiki/African_Theological_Studies-1.md
category: reference
title: African Theological Studies
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/African_Theological_Studies
date_saved: 2026-05-05T09:52:48.901352+00:00
instance: kb-cron
parent: african-theological-studies-20260505-0
summary:
==== Volume 2, Solving the Problems of Poverty ==== Kwazu's book on African poverty was the second monograph in the series. His bibliography lists "Hillman, K., Wörterbuch der Soziologie, Kröner, Stuttgart, Bd. 410, 1994" on p. 252, but Hillmann should be spelled with two n's and the dictionary does not contain 410 volumes, as Kwazu's entry suggests, but is volume 410 in the series called "Kröners Taschenausgabe". The 1994 printing is, furthermore, the fourth, revised edition. The bibliography cites E. Leuninger's "Take (sic) to Catholic Workers' Movement, (KAB) Diocese of Limburg, Germany, 1 April 2001" (p. 255). The talk (not "take") was certainly held in German, and Kwazu's bibliography lists several German titles, but here, Kwazu gives no specific title, making the reference inaccessible. ==== Volume 13, Women's Emancipation in Africa ==== Mutume's monograph on women's rights (volume 13 in the series) cites the Vatican's 1976 "Declaration on the Question of Admission of Women to the Ministerial Priesthood" and lists it twice in the bibliography, once under A for Austin Flannery, and once under F. Both are incorrect, since it was signed by Franjo Cardinal Seper, then Prefect of the Congregation of the Doctrine of the Faith. The Dominican priest


id: chen-zhenyuan-fake-peer-review-scandal-20260505-0
file: data/en.wikipedia.org/wiki/Chen_Zhenyuan_fake_peer_review_scandal-0.md
category: reference
title: Chen Zhenyuan fake peer review scandal
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Chen_Zhenyuan_fake_peer_review_scandal
date_saved: 2026-05-05T09:52:51.221154+00:00
instance: kb-cron
parent: chen-zhenyuan-fake-peer-review-scandal-20260505-0
summary:
The Chen Zhenyuan fake peer review scandal was an academic misconduct case that came to light in July 2014. The UK-based publisher SAGE Publications discovered that Chen Zhenyuan, then an associate professor in the Department of Computer Science at National Pingtung University of Education (now National Pingtung University, Minsheng Campus), was suspected of fabricating 130 fake reviewer accounts in order to manipulate peer review and citation networks to secure the publication of his papers. As a result, SAGE retracted 60 papers authored or co-authored by Chen Zhenyuan from the Journal of Vibration and Control (JVC). The mass retraction of 60 papers at once was extremely rare in the global academic community. The editor-in-chief of the Journal of Vibration and Control subsequently resigned and applied for retirement from his university. Among the retracted papers, five were authored by Taiwan’s then Minister of Education, Chiang Wei-ling, with Chen Zhenyuan listed as a co-author. Amid intense public pressure, Chiang Wei-ling resigned as Minister of Education. == Background and timeline == === 2009 === Chen Zhenyuan joined National Pingtung University of Education as a faculty member and began publishing a large number of papers in the Journal of Vibration and Control (JVC). === February


id: chen-zhenyuan-fake-peer-review-scandal-20260505-1
file: data/en.wikipedia.org/wiki/Chen_Zhenyuan_fake_peer_review_scandal-1.md
category: reference
title: Chen Zhenyuan fake peer review scandal
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Chen_Zhenyuan_fake_peer_review_scandal
date_saved: 2026-05-05T09:52:51.221154+00:00
instance: kb-cron
parent: chen-zhenyuan-fake-peer-review-scandal-20260505-0
summary:
=== February 6, 2015 === MOST decided to impose a 10-year ban on Chen Zhenwu and to recover principal investigator fees from three research projects. Chiang Wei-ling received a one-year ban. == Subsequent developments == === March 6, 2016 === The international journal Human Factors and Ergonomics in Manufacturing & Service Industries announced the retraction of three papers published by Chen Zhenwu between 2012 and 2013. These papers listed only Chen Zhenwu and other scholars as authors, without Chen Zhenyuan. Retraction Watch concluded that Chen Zhenwu was not merely an innocent bystander in the case, prompting MOST to reopen its investigation. The Ministry of Education stated that after the incident, National Kaohsiung University of Science and Technology verified that several papers submitted by Chen Zhenwu for promotion contained problems. The university decided to revoke his professorship, demote him to associate professor, and recover excess salary payments. Chen Zhenwu filed an administrative appeal. After court proceedings, the Ministry of Education initially lost the case, appealed, and the Supreme Administrative Court ultimately ruled to vacate the original judgment, revoke the appeal decision and the original administrative sanction, and ordered the case to be reinvestigated and re-sanctioned accordingly. As a result, Chen Zhenwu


id: clinical-peer-review-20260505-0
file: data/en.wikipedia.org/wiki/Clinical_peer_review-0.md
category: reference
title: Clinical peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Clinical_peer_review
date_saved: 2026-05-05T09:52:52.389008+00:00
instance: kb-cron
parent: clinical-peer-review-20260505-0
summary:
Clinical peer review, also known as medical peer review is the process by which health care professionals, including those in nursing and pharmacy, evaluate each other's clinical performance. A discipline-specific process may be referenced accordingly (e.g., physician peer review, nursing peer review). Today, clinical peer review is most commonly done in hospitals, but may also occur in other practice settings including surgical centers and large group practices. The primary purpose of peer review is to improve the quality and safety of care. Secondarily, it serves to reduce the organization's vicarious malpractice liability and meet regulatory requirements. In the US, these include accreditation, licensure and Medicare participation. Peer review also supports the other processes that healthcare organizations have in place to assure that physicians are competent and practice within the boundaries of professionally accepted norms. == Overview == Clinical peer review should be distinguished from the peer review that medical journals use to evaluate the merits of a scientific manuscript, from the peer review process used to evaluate health care research grant applications, and, also, from the process by which clinical teaching might be evaluated. All these forms of peer review are confounded in the term Medical Peer Review. Moreover, Medical


id: clinical-peer-review-20260505-1
file: data/en.wikipedia.org/wiki/Clinical_peer_review-1.md
category: reference
title: Clinical peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Clinical_peer_review
date_saved: 2026-05-05T09:52:52.389008+00:00
instance: kb-cron
parent: clinical-peer-review-20260505-0
summary:
The American Nurses Association believes nurses bare primary responsibility and accountability for the quality of nursing care their clients receive. Standards of nursing practice provide a means for measuring the quality of nursing care a client receives. Each nurse is responsible for interpreting and implementing the standards of nursing practice. Likewise, each nurse must participate with other nurses in the decision-making process for evaluating nursing care…Peer review implies that the nursing care delivered by a group of nurses or an individual nurse is evaluated by individuals of the same rank or standing according to established standards of practice…. Peer review is an organized effort whereby practicing professionals review the quality and appropriateness of services ordered or performed by their professional peers. Peer review in nursing is the process by which practicing registered nurses systematically access, monitor, and make judgments about the quality of nursing care provided by peers as measured against professional standards of practice. In Nursing, as in other professions, peer review applies professional control to practice, and is used by professionals to hold themselves accountable for their services to the public and the organization. Peer review plays a role in affecting the quality of outcomes, fostering practice development,


id: clinical-peer-review-20260505-2
file: data/en.wikipedia.org/wiki/Clinical_peer_review-2.md
category: reference
title: Clinical peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Clinical_peer_review
date_saved: 2026-05-05T09:52:52.389008+00:00
instance: kb-cron
parent: clinical-peer-review-20260505-0
summary:
The first documented description of a peer review process is found in the Ethics of the Physician written by Ishap bin Ali al-Rahawi (854–931) of al-Raha, Syria. His work, as well as later Arabic medical manuals, states that a visiting physician must always make duplicate notes of a patient's condition on every visit. When the patient was cured or had died, the notes of the physician were examined by a local medical council of other physicians, who would review the practicing physician's notes to decide whether his or her performance met the required standards of medical care. If their reviews were negative, the practicing physician could face a lawsuit from a maltreated patient. Such practices are known to have continued into the 11th century. In the 1900s, peer review methods evolved in relation to the pioneering work of Codman's End Result System and Ponton's concept of Medical Audit. Lembcke, himself a major contributor to audit methodology, in reviewing this history, notes the pre-emptive influence of hospital standardization promoted by the American College of Surgeons (ACS) following WWI. The Joint Commission (on Accreditation of Hospitals) followed the ACS in this role from 1952. Medicare legislation, enacted in 1964, boosted the stature


id: clinical-peer-review-20260505-3
file: data/en.wikipedia.org/wiki/Clinical_peer_review-3.md
category: reference
title: Clinical peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Clinical_peer_review
date_saved: 2026-05-05T09:52:52.389008+00:00
instance: kb-cron
parent: clinical-peer-review-20260505-0
summary:
==== Health Care Quality Improvement Act ==== US federal law generally trumps state law. The federal Health Care Quality Improvement Act ("HCQIA"), 42 U.S.C. § 11112, enacted in 1986, sets standards that professional review actions must meet in order to receive protection under the Act. It requires that the action be taken in the reasonable belief that it will advance healthcare quality based on facts obtained through reasonable efforts with due process and fairness to the involved physician. When peer review leads to an action to limit or revoke clinical privileges, the physician is entitled to both a fair hearing and the right of appeal. Congress explicitly stated the rationale for this legislation as follows: (1) The increasing occurrence of medical malpractice and the need to improve the quality of medical care have become nationwide problems that warrant greater efforts than those that can be undertaken by any individual State. (2) There is a national need to restrict the ability of incompetent physicians to move from State to State without disclosure or discovery of the physician's previous damaging or incompetent performance. (3) This nationwide problem can be remedied through effective professional peer review. (4) The threat of private money damage


id: clinical-peer-review-20260505-4
file: data/en.wikipedia.org/wiki/Clinical_peer_review-4.md
category: reference
title: Clinical peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Clinical_peer_review
date_saved: 2026-05-05T09:52:52.389008+00:00
instance: kb-cron
parent: clinical-peer-review-20260505-0
summary:
Sham peer review is a name given to the abuse of a medical peer review process to attack a doctor for personal or other non-medical reasons. State medical boards have withheld medical records from court to frame innocent physicians as negligent. Another type of review similar to sham peer review is "incompetent peer review," in which the reviewers are unable to accurately assess the quality of care provided by their colleagues. Controversy exists over whether medical peer review has been used as a competitive weapon in turf wars among physicians, hospitals, HMOs, and other entities and whether it is used in retaliation for whistleblowing. Many medical staff laws specify guidelines for the timeliness of peer review, in compliance with JCAHO standards, but state medical boards are not bound by such timely peer review and occasionally litigate cases for more than five years. Abuse is also referred to as "malicious peer review" by those who consider it endemic, and they allege that the creation of the National Practitioner Data Bank under the 1986 Healthcare Quality Improvement Act (HCQIA) facilitates such abuse, creating a 'third-rail' or a 'first-strike' mentality by granting significant immunity from liability to doctors and others who participate in


id: code-review-20260505-0
file: data/en.wikipedia.org/wiki/Code_review-0.md
category: reference
title: Code review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Code_review
date_saved: 2026-05-05T09:52:53.547978+00:00
instance: kb-cron
parent: code-review-20260505-0
summary:
Code review (sometimes referred to as peer review) is a software quality assurance activity in which one or more people examine the source code of a computer program, either after implementation or during the development process. The persons performing the checking, excluding the author, are called "reviewers". At least one reviewer must not be the code's author. Code review differs from related software quality assurance techniques like static code analysis, self-checks, testing, and pair programming. Static analysis relies primarily on automated tools, self-checks involve only the author, testing requires code execution, and pair programming is performed continuously during development rather than as a separate step. == Goal == Although direct discovery of quality problems is often the main goal, code reviews are usually performed to reach a combination of goals: Improving code quality – Improve internal code quality and maintainability through better readability, uniformity, and understandability Detecting defects – Improve quality regarding external aspects, especially correctness, but also find issues such as performance problems, security vulnerabilities, and injected malware Learning/Knowledge transfer – Sharing codebase knowledge, solution approaches, and quality expectations, both to the reviewers and the author Increase sense of mutual responsibility – Increase a sense of collective code ownership


id: commentary-article-20260505-0
file: data/en.wikipedia.org/wiki/Commentary_article-0.md
category: reference
title: Commentary article
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Commentary_article
date_saved: 2026-05-05T09:52:54.688511+00:00
instance: kb-cron
parent: commentary-article-20260505-0
summary:
A commentary article is a short narrowly focused article that is usually commissioned by an Academic journal, though they are sometimes published from unsolicited submissions. == References ==


id: funding-by-lottery-20260505-0
file: data/en.wikipedia.org/wiki/Funding_by_lottery-0.md
category: reference
title: Funding by lottery
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Funding_by_lottery
date_saved: 2026-05-05T09:52:55.911338+00:00
instance: kb-cron
parent: funding-by-lottery-20260505-0
summary:
Funding by lottery is the use of randomization in the allocation of science funding by research funding organizations. As of early 2025, about a dozen funders worldwide have implemented some form of funding by lottery in their funding calls. There is no scholarly consensus on the benefits and drawbacks of funding by lottery. Proponents argue that it can help mitigate biases in funding allocation and minimize the high costs associated with grant writing and peer review. Critics, however, express concerns that diminishing the role of peer review panels in funding decisions could lead to a decline in the quality of funded projects and undermine public trust in funders, in scientific peer review, and ultimately in science at large. == Overview == Science funding is generally distributed through competitive funding calls organized by research funding agencies (commonly referred to as "funders"). Eligible researchers submit research grant proposals in response to these calls, which are then evaluated by expert peer review panels. Funding is typically awarded to the proposals that receive the most positive evaluations from these panels. Compared to this traditional, peer-reviewed science funding, funding by lottery fully or partly replaces peer review panel evaluations with random selection. In a full


id: funding-by-lottery-20260505-1
file: data/en.wikipedia.org/wiki/Funding_by_lottery-1.md
category: reference
title: Funding by lottery
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Funding_by_lottery
date_saved: 2026-05-05T09:52:55.911338+00:00
instance: kb-cron
parent: funding-by-lottery-20260505-0
summary:
=== Arguments in favor === The main argument in favor of funding by lottery is that randomization can help reduce the costs of grant writing and peer review while promoting fairer and more diverse funding outcomes. More specifically, funding by lottery may curb or eliminate biases in research evaluation, such as reviewer conservatism – i.e. the tendency to favor conventional ideas over more innovative or riskier ones – as well as bias against interdisciplinary research. Furthermore, funding by lottery is seen as way to counteract the growing concentration of research funding in the hands of few renowned labs and research-performing institutions, a phenomenon known as the "Matthew effect" in science funding. Lastly, some have argued that funding by lottery could remove some incentives for research misconduct. Because grant acquisition is considered a marker of academic success, researchers may feel pressured to obtain as much funding as possible. In extreme cases, this can lead to unethical practices, like submitting virtually identical research grant proposals to multiple funding calls – a problem known as "double-dipping". By decoupling grant acquisition from academic prestige, funding by lottery could help mitigate such incentives for misconduct. === Arguments against === Some scholars highlight the lack of


id: ingelfinger-rule-20260505-0
file: data/en.wikipedia.org/wiki/Ingelfinger_rule-0.md
category: reference
title: Ingelfinger rule
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Ingelfinger_rule
date_saved: 2026-05-05T09:52:57.068716+00:00
instance: kb-cron
parent: ingelfinger-rule-20260505-0
summary:
The Ingelfinger rule is an eponymous rule named after Franz J. Ingelfinger, The New England Journal of Medicine (NEJM) editor-in-chief who enunciated it in 1969. The rule, as originally articulated in the editorial "Definition of 'Sole Contribution'", stated that NEJM would not publish findings that had been published elsewhere. Though originally meant only for NEJM, the guideline was subsequently adopted by several other scientific journals, and it has shaped scientific publishing ever since. Historically it has also helped to ensure that the journal's content is fresh and does not duplicate content previously reported elsewhere, and it seeks to protect the scientific embargo system. A similar policy had been earlier expressed in 1960 by Samuel Goudsmit, editor of the Physical Review Letters, but it did not become as well known. The Ingelfinger rule has been seen as having the aim of preventing authors from performing duplicate publications which would unduly inflate their publication record. On the other hand, it has also been stated that the real reason for the Ingelfinger rule is to protect the journals' revenue stream, and with the increase in popularity of preprint servers such as arXiv, bioRxiv, and HAL many journals have loosened their requirements concerning the


id: interdisciplinary-peer-review-20260505-0
file: data/en.wikipedia.org/wiki/Interdisciplinary_peer_review-0.md
category: reference
title: Interdisciplinary peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Interdisciplinary_peer_review
date_saved: 2026-05-05T09:52:58.269045+00:00
instance: kb-cron
parent: interdisciplinary-peer-review-20260505-0
summary:
Interdisciplinary Peer Review (IPR) is a peer review process with an additional focus outside of the area of the author's subject of expertise. Disciplines such as telecommunications, political science, engineering, and medicine require specific subject matter expertise, however, they still cross multiple disciplines and may require review from many alternate functional areas to achieve maximum perspective to prevent duplication or improper publication. Reviews of this nature may also cross cultures, race, and other demographics to gain perspective. == Reduplication and Interdisciplinarity == In Peer Commentary on Peer Review: A case study in scientific quality control, Stevan R. Harnard (p. 15) begins to touch on the concept by addressing how well the review process works and what factors prevent re-duplication. Julie Klein, a professor of interdisciplinary studies at Wayne State University, defined interdisciplinarity as "new divisions of intellectual labor, collaborative research, team teaching, hybrid fields, comparative studies, increased borrowing across disciplines, and a variety of unified, holistic perspectives that have created pressures upon traditional divisions of knowledge". Klein has also described how interdisciplinarity is used to "find answers to complex questions, address broad issues, explore disciplinary and professional relations, to solve problems beyond the scope of any one discipline, and to


id: journalreview-org-20260505-0
file: data/en.wikipedia.org/wiki/JournalReview.org-0.md
category: reference
title: JournalReview.org
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/JournalReview.org
date_saved: 2026-05-05T09:52:59.408510+00:00
instance: kb-cron
parent: journalreview-org-20260505-0
summary:
JournalReview.org was an online interdisciplinary journal club. It hosted an international community of doctors and existed for the purpose of facilitating critical discussion and post-publication peer review of all medical literature indexed by the National Library of Medicine in PubMed. It was part of the movement towards open peer review, telemedicine, and self-archiving/open access. == Contents == It was web-based and consisted of commentary and discussion related to published medical literature. Members could participate individually, or by creating their own "journal clubs" – collections of members who were actively participating in discussion of mutual interest. Some residency training programs adopted this resource to meet ACGME requirements. Members utilized this resource to suggest future research, and to identify bias or limitations to published work. Educators utilized this resource to help stimulate and document critical discussion of assigned journal articles. Authors utilized this resource to answer questions about and provide supplement to their published work. This resource was also utilized by the public as a vehicle to better understand published trade literature. Members consists of all the major specialties as well areas of basic science. Approximately 7,000 doctors, health care professionals, researchers, residency training programs, and students from all over the world


id: lehman-review-20260505-0
file: data/en.wikipedia.org/wiki/Lehman_Review-0.md
category: reference
title: Lehman Review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Lehman_Review
date_saved: 2026-05-05T09:53:00.564964+00:00
instance: kb-cron
parent: lehman-review-20260505-0
summary:
A Lehman Review is an independent peer review and evaluation of the status of a major construction project in the United States Department of Energy's (DOE's) Office of Science. Lehman Reviews evaluate all aspects of a construction project's current status, including technical aspects, cost, schedule and management, and they are usually held twice a year. Lehman Reviews are widely known in DOE, other agencies and abroad. The reviews are named after Daniel Lehman, Director of the DOE Office of Science's Office of Project Assessment since 1991. == History == The Office of Project Assessment within the DOE's Office of Science provides independent advice relating to those activities essential to the construction and operation of major research facilities, as well as professional management and staff support regarding these functions. Within this mission, the office conducts independent technical, cost, schedule and management peer reviews of major construction projects, which include civil construction projects and the construction of large experimental equipment. While the Office of Science is exempt from DOE Order 413.3B, Program and Project Management for the Acquisition of Capital Assets for independent project and peer review, a Lehman Review will typically meet the requirements of the order. During a project's proposal


id: henry-oldenburg-20260505-0
file: data/en.wikipedia.org/wiki/Henry_Oldenburg-0.md
category: reference
title: Henry Oldenburg
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Henry_Oldenburg
date_saved: 2026-05-05T09:53:01.743243+00:00
instance: kb-cron
parent: henry-oldenburg-20260505-0
summary:
Henry Oldenburg (also Henry Oldenbourg) (c. 1618 as Heinrich Oldenburg – 5 September 1677) was a German theologian, diplomat, and natural philosopher, known as one of the creators of modern scientific peer review. He was one of the foremost intelligencers of 17th-century Europe, with a network of correspondents to rival those of Fabri de Peiresc, Marin Mersenne, and Ismaël Boulliau. At the foundation of the Royal Society in London, he took on the task of foreign correspondence, as the first Secretary. == Early life == Born in Bremen, Germany, he was trained in theology and received his degree from the local Gymnasyum illustre on 2 November 1639. He had an initial very firm grasp of the German, Latin, and Greek languages. His movements during the 1640s are unclear, but he is thought to have worked as a tutor in England for much of the decade. In 1648 he left England and spent some time in Leiden and Utrecht in the Dutch Republic, where he became conversant in the Dutch language. After a short stay back in Bremen in the spring, he arrived back in London in July 1653 as a diplomatic envoy of the city of Bremen's senate to the


id: open-peer-review-20260505-0
file: data/en.wikipedia.org/wiki/Open_peer_review-0.md
category: reference
title: Open peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_peer_review
date_saved: 2026-05-05T09:53:02.960954+00:00
instance: kb-cron
parent: open-peer-review-20260505-0
summary:
Open peer review is the various possible modifications of the traditional scholarly peer review process. The three most common modifications to which the term is applied are: Open identities: Authors and reviewers are aware of each other's identity. Open reports: Review reports are published alongside the relevant article (rather than being kept confidential). Open participation: The wider community (and not just invited reviewers) are able to contribute to the review process. These modifications are supposed to address various perceived shortcomings of the traditional scholarly peer review process, in particular its lack of transparency, lack of incentives, wastefulness, bullying and harassment. == Definitions == Open identities Open peer review may be defined as "any scholarly review mechanism providing disclosure of author and referee identities to one another at any point during the peer review or publication process". Then reviewer's identities may or may not be disclosed to the public. This is in contrast to the traditional peer review process where reviewers remain anonymous to anyone but the journal's editors. Authors' names are disclosed during the process in a single-blind organisation of reviews. In the double-blind process, authors' names and reviewers' names all remain anonymous except to the editor. Open reports Open


id: open-peer-review-20260505-1
file: data/en.wikipedia.org/wiki/Open_peer_review-1.md
category: reference
title: Open peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_peer_review
date_saved: 2026-05-05T09:53:02.960954+00:00
instance: kb-cron
parent: open-peer-review-20260505-0
summary:
== History == In 1997, the open participation journal Electronic Transactions on Artificial Intelligence was launched. Articles were first "published" (meaning the day when the article is first made public), then "reviewed" (meaning a open process of public discussion), and then "refereed" (meaning the confidential pass/fail decision of whether to accept the article or not). In 1999, the open access journal Journal of Medical Internet Research was launched, which from its inception decided to publish the names of the reviewers at the bottom of each published article. Also in 1999, the British Medical Journal moved to an open peer review system, revealing reviewers' identities to the authors but not the readers, and in 2000, the medical journals in the open access BMC series published by BioMed Central, launched using open peer review. As with the BMJ, the reviewers' names are included on the peer review reports. In addition, if the article is published the reports are made available online as part of the "pre-publication history"'. Several other journals published by the BMJ Group allow optional open peer review, as does PLoS Medicine, published by the Public Library of Science. The BMJ's Rapid Responses allows ongoing debate and criticism following publication.


id: open-peer-review-20260505-2
file: data/en.wikipedia.org/wiki/Open_peer_review-2.md
category: reference
title: Open peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_peer_review
date_saved: 2026-05-05T09:53:02.960954+00:00
instance: kb-cron
parent: open-peer-review-20260505-0
summary:
Publishers: BioMed Central BMJ Group Copernicus Publications European Molecular Biology Organization (EMBO) Frontiers MDPI (authors have the option to publish peer review reports, etc.) Nature Research Open Research Europe (the European Union publishing platform for Horizon Europe and Horizon 2020 projects) Journals: eLife GigaScience Nature PeerJ PLOS ReScience C Semantic Web journal by IOS Press WikiJournal SciPost Peer review at The BMJ, BioMed Central, EMBO, eLife, ReScience C, and the Semantic Web journal involves posting the entire pre-publication history of the article online, including not only signed reviews of the article, but also its previous versions and in some cases names of handling editors and author responses to the reviewers. Furthermore, the Semantic Web journal publishes reviews of all submissions, including rejected ones, on its website, while eLife plans to publish the reviews not only for published articles, but also for rejected articles. The European Geosciences Union operates public discussions where open peer review is conducted before suitable articles are accepted for publication in the journal. Sci, an open access journal which covers all research fields, adapted a post publication public peer-review (P4R) in which it promised authors immediate visibility of their manuscripts on the journal's online platform after a


id: peer-critique-20260505-0
file: data/en.wikipedia.org/wiki/Peer_critique-0.md
category: reference
title: Peer critique
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Peer_critique
date_saved: 2026-05-05T09:53:04.124599+00:00
instance: kb-cron
parent: peer-critique-20260505-0
summary:
Peer critique, a specialized form of critique, is the common practice of professional peers, especially writers, reviewing and providing constructive criticism of each other's work before that work is turned in for credit or professional review. Writers in many genres and professions, including fiction writers and technical writers, use some form of peer critique as part of their process of writing. It is also commonly used as an instructional technique in school writing settings. Peer critique may also be referred to as peer review or a writing workshop. Writers who gather to critique each others' work are often called writing circles. == In action == === In the classroom === Peer critique has long been used as part of the process of teaching writing from primary school to secondary and post-secondary education. In traditional classrooms power and authority can often be teacher-centric, with teachers correcting work to their own vision of ideal writing. Many researchers have found that peer critique offers a complementary style of feedback Whereas teacher feedback may focus on general comments and error correction, peers tend to give specific, deep comments on the work before them rather than correcting to an ideal. Peer critique, furthermore, is used


id: peer-critique-20260505-1
file: data/en.wikipedia.org/wiki/Peer_critique-1.md
category: reference
title: Peer critique
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Peer_critique
date_saved: 2026-05-05T09:53:04.124599+00:00
instance: kb-cron
parent: peer-critique-20260505-0
summary:
=== Online writing classes === In recent years with the advent of the Blackboard Learning System, Canvas, and similar online teaching tools (see LMS), it has become possible to take writing courses entirely online. In online courses, students generally give each other feedback on writing in message-board style posts, though some instructors may allow for anonymity in posting responses. Comments are usually brief. Teachers must beware of the "pile-on effect" of students merely echoing what teachers and previous commenters have mentioned; it will be useful for teachers to apply lessons from peer critique websites, which have functioned online for many years. Some instructors may, for example, arrange students into small synchronous groups for discussion, mitigating overwhelming feedback from the entire class. === Online critique sites === Since at least 1985, with the Compuserve Books & Writer's Forum, writers have formed writing spaces online where they can discuss writing, share resources, and critique work. There are many active critique sites now, catering to all levels and genres of writers; the popular website Reddit has a sub-reddit dedicated to writing critique, titled critique my writing., while another popular forum, www.writingforums.com, is dedicated to writing and online critique. Other peer critique sites include


id: peer-jury-process-20260505-0
file: data/en.wikipedia.org/wiki/Peer_jury_process-0.md
category: reference
title: Peer jury process
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Peer_jury_process
date_saved: 2026-05-05T09:53:05.292981+00:00
instance: kb-cron
parent: peer-jury-process-20260505-0
summary:
The peer jury process is often used in the arts to assess applications for grants, proposals for exhibitions made in response to an open call for submissions and art competitions. Like the practice of peer review used in the academic sphere, a peer jury brings to an evaluation process expertise that an organization or agency awarding grants or sponsoring a competition might not itself have. A peer jury in the arts context is generally composed of artists selected to be representative of those working in a relevant discipline or area. While jurors are expected to have independence, anonymity is less of a concern than in academic peer review, in part because the jury's task is not to catch errors or offer criticism but to only to select. For example, in Canada, agencies that award grants typically publish lists of jurors or make lists available on request. In principle the peer jury process is an effective way for arts councils and other agencies to be responsive to and engaged with the constituencies they serve, and in touch with the latest developments in contemporary art. == See also == peer review


id: peerage-of-science-20260505-0
file: data/en.wikipedia.org/wiki/Peerage_of_Science-0.md
category: reference
title: Peerage of Science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Peerage_of_Science
date_saved: 2026-05-05T09:53:07.483675+00:00
instance: kb-cron
parent: peerage-of-science-20260505-0
summary:
Peerage of Science was a scientific peer review service aimed at improving "the current peer review system and make the peer review process more scientific, fair and transparent". The company was founded in 2011 by the scientists Janne Kotiaho, Mikko Mönkkönen, and Janne-Tuomas Seppänen in Jyväskylä, Finland. Initially it focused on the areas of "ecology, evolutionary biology and conservation biology", but within 2 years it expanded to other areas of science. The service was the winner of the 2012 Award for Publishing Innovation from The Association of Learned and Professional Society Publishers (ALPSP), and of the 2013 recognition award from Communications Professionals of Finnish Universities. Despite patronage from several paying customers, including the Society for Conservation Biology, Springer Nature, and Taylor & Francis, the company failed to expand its market base (it received only 102 submissions in 2017 and only 60% of submissions received at least one publishing offer) and went out of business soon after. == Differences to traditional peer review == The service had several practices that differed from the traditional approaches to academic peer review and submissions. === Concurrent and shared journal consideration === PoS implemented a "unified reviewing process", which reduced "the workload of the reviewers’


id: physicsoverflow-20260505-0
file: data/en.wikipedia.org/wiki/PhysicsOverflow-0.md
category: reference
title: PhysicsOverflow
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/PhysicsOverflow
date_saved: 2026-05-05T09:53:08.636905+00:00
instance: kb-cron
parent: physicsoverflow-20260505-0
summary:
PhysicsOverflow is a physics website that serves as a post-publication open peer review platform for research papers in physics, as well as a collaborative blog and online community of physicists. It allows users to ask, answer and comment on graduate-level physics questions, post and review manuscripts from ArXiv (which lists PhysicsOverflow discussion pages among its trackbacks) and other sources, and vote on both forms of content. In addition to the two primary forms of content, the PhysicsOverflow community also welcomes discussions on unsolved problems, and hosts a chat section for discussions on topics generally of interest to physicists and students of physics, such as those related to recent events in physics, physics academia, and the publishing process. == History == PhysicsOverflow was started in April 2014 as a physics-equivalent of MathOverflow by Rahel Knöpfel, a physics PhD at the University of Rostock, high-school student Abhimanyu Pallavi Sudhir, and Roger Cattin, a retired professor of computer science at the University of Applied Sciences, Switzerland. The site was initially a mere question-and-answer forum, as it was started by users dissatisfied by the policies of the Physics Stack Exchange, but it was eventually expanded to include a Reviews section in October 2014. ==


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: 2026-05-05T09:53:09.860059+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: 2026-05-05T09:53:09.860059+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: 2026-05-05T09:53:09.860059+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: 2026-05-05T09:53:09.860059+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: publons-20260505-0
file: data/en.wikipedia.org/wiki/Publons-0.md
category: reference
title: Publons
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Publons
date_saved: 2026-05-05T09:53:10.986861+00:00
instance: kb-cron
parent: publons-20260505-0
summary:
Publons was a commercial website that provided a free service for academics to track, verify, and showcase their peer review and editorial contributions for academic journals. It was launched in 2012, bought by Clarivate in 2017 and merged into Web of Science in 2022. It claimed that over 3,000,000 researchers joined the site, adding more than one million reviews across 25,000 journals. In 2019, ResearcherID was integrated with Publons. Publons produced a verified record of a person's review and editorial activity for journals, which could be downloaded to include in CVs, funding and job applications, and promotion and performance evaluations. Publons' business model was based on partnering with publishers. == Background == Publons was founded by Andrew Preston and Daniel Johnston to address the static state of peer-reviewing practices in academic research publishing, in view of encouraging collaboration and speeding scientific development. The Publons name was an homage to the "publon", the "minimum unit of publishable material". The company was registered in New Zealand and had an office in London, UK. It was acquired by Clarivate Analytics in 2017. == Services == Publons also provided: tools for publishers to find, screen, contact, and motivate peer reviewers; data and publications about


id: pubpeer-20260505-0
file: data/en.wikipedia.org/wiki/PubPeer-0.md
category: reference
title: PubPeer
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/PubPeer
date_saved: 2026-05-05T09:53:12.208470+00:00
instance: kb-cron
parent: pubpeer-20260505-0
summary:
PubPeer is a website that allows users to discuss and review scientific research after publication, i.e. post-publication peer review, established in 2012. The site has served as a whistleblowing platform, in that it highlighted shortcomings in several high-profile papers, in some cases leading to retractions and to accusations of scientific fraud, as noted by Retraction Watch. Contrary to most platforms, it allows anonymous post-publication commenting, a controversial feature which is the main factor for its success. Consequently, accusations of libel have been levelled at some of PubPeer's users; correspondingly the website has since 2016 told commentators to use only facts that can be publicly verified. Questions have been raised about the copyright ownership of PubPeer's often-anonymous contents. In 2021 a study found that "more than two-thirds of comments [on PubPeer] are posted to report some type of misconduct, mainly about image manipulation". Health sciences and life sciences were shown to have most comments, and most comments reporting publishing fraud and data manipulation. Social science and humanities disciplines in turn had fewer comments, but the highest percentage comments about critical reviews about theory and highlight methodological flaws. The research concluded that "while biochemists access the site to report misconduct... social scientists


id: quantitative-biology-journal-20260505-0
file: data/en.wikipedia.org/wiki/Quantitative_Biology_(journal)-0.md
category: reference
title: Quantitative Biology (journal)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Quantitative_Biology_(journal)
date_saved: 2026-05-05T09:53:13.415769+00:00
instance: kb-cron
parent: quantitative-biology-journal-20260505-0
summary:
Quantitative Biology is a quarterly peer-reviewed open-access interdisciplinary scientific journal covering experimental biology, theoretical biology, computational biology, bioinformatics, systems biology, synthetic biology, and digital and AI biology. The journal was established in 2013 by Higher Education Press and Tsinghua University. The co-founding editors-in-chief were Michael Q. Zhang and Chao Tang. As of 2019, the editors-in-chief were Chao Tang and Xuegong Zhang. Before 2020, Quantitative Biology was co-published by Higher Education Press and Springer Nature. As of 2023, it was published by Higher Education Press and Wiley. According to the Journal Citation Reports and Scopus, the journal had a 2024 impact factor of 1.4, and a 2024 CiteScore of 2.0, respectively. == Abstracting and indexing == The journal is abstracted and indexed in: Emerging Sources Citation Index (ESCI) Scopus Biological Abstracts BIOSIS Chemical Abstracts Service Embase == References ==


id: registered-report-20260505-0
file: data/en.wikipedia.org/wiki/Registered_report-0.md
category: reference
title: Registered report
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Registered_report
date_saved: 2026-05-05T09:53:14.608295+00:00
instance: kb-cron
parent: registered-report-20260505-0
summary:
A registered report is a scholarly publishing format in which the research question, study design, and analysis plan are peer-reviewed before data are collected or analyzed. This publication model aims to reduce publication bias and improve research transparency by restricting the evaluation of scientific manuscripts to the research question, method, and analysis plan, without consideration of whether the results support the research question or not. == Process == The Registered Reports process occurs in two stages of peer review. In the first stage, known as Stage 1 review, authors submit a detailed study proposal to a journal that offers the Registered Reports format. The proposal typically includes the theoretical rationale, research questions or hypotheses, experimental design, sampling plan, and a preregistered statistical analysis plan. At this point, no data have yet been collected or analyzed. Peer reviewers evaluate the submission based on the importance of the research question and the quality and rigor of the proposed methodology. Because the preregistration undergoes peer review, Registered Reports introduce an additional quality control mechanism compared with standard preregistration practices in which researchers register study plans independently. Following Stage 1 review, a journal may reject the submission, request revisions, or grant in-principle acceptance (IPA).


id: research-integrity-and-peer-review-20260505-0
file: data/en.wikipedia.org/wiki/Research_Integrity_and_Peer_Review-0.md
category: reference
title: Research Integrity and Peer Review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Research_Integrity_and_Peer_Review
date_saved: 2026-05-05T09:53:15.749896+00:00
instance: kb-cron
parent: research-integrity-and-peer-review-20260505-0
summary:
Research Integrity and Peer Review is an international, open access, peer reviewed journal that was launched in 2016. It is published by BioMed Central and focuses on problems in peer review, replication, and the scientific process. == See also == Metascience == References ==


id: al-ruhawi-20260505-0
file: data/en.wikipedia.org/wiki/Al-Ruhawi-0.md
category: reference
title: Al-Ruhawi
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Al-Ruhawi
date_saved: 2026-05-05T09:53:16.940835+00:00
instance: kb-cron
parent: al-ruhawi-20260505-0
summary:
Ishāq bin Ali al-Rohawi (Arabic: إسحاق بن علي الرهاوي) was a 9th-century author of the first medical ethics book in Arabic medicine. His Ethics of the Physician contains the first documented description for peer review processes. The processes gave the fundamentals of peer review processes where practising Arab physicians were reviewed by peers on the medical treatment they provide to their patients. If the treatment of a patient was incorrectly done with negative peer reviews, then the physician is at a lawsuit liability. Al-Ruhawi was probably an Iranian-Nestorian from Al-Ruha, modern-day Şanlıurfa. It is often known as Urfa. Not much is known about Al-Ruhawi, including his beliefs. The author Levey stated in his book that Al-Ruhawi was a Nestorian Christian while Johann Christoph Bürgel wrote that Al-Ruhawi was Jewish. However, both authors did not give evidence to support their argument, rather having it based on their interpretations. Although there are conflictions in these two beliefs, there is evidence to prove that Al-Rahawi had Islamic beliefs. Al-Rahawi began his book with the words "In the name of Allah," which is a traditional opening for Muslims. Additionally, Al-Rahawi uses the word "Allah" hundreds of times in his work, which is associated with


id: scholarly-peer-review-20260505-0
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-0.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T09:53:18.276870+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
Scholarly peer review or academic peer review (also known as refereeing) is the process of having a draft version of a researcher's methods and findings reviewed (usually anonymously) by experts (or "peers") in the same field. Peer review is widely used for helping the academic publisher (i.e., the editor-in-chief, the editorial board, or the program committee) decide whether the work should be accepted, considered acceptable with revisions, or rejected for official publication in an academic journal, a monograph, or in the proceedings of an academic conference. If the identities of authors are not revealed to each other, the procedure is called dual-anonymous peer review. Academic peer review requires a community of experts in a given (and often narrowly defined) academic field, who are qualified and able to perform reasonably impartial review. Impartial review, especially of work in less narrowly defined or inter-disciplinary fields, may be difficult to accomplish, and the significance (good or bad) of an idea may never be widely appreciated among its contemporaries. Peer review is generally considered necessary to academic quality and is used in most major scholarly journals. However, peer review does not prevent publication of invalid research, and as experimentally controlled studies of this process


id: scholarly-peer-review-20260505-1
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-1.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T09:53:18.276870+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
Workload. A small group of editors/assessors cannot devote sufficient time to each of the many articles submitted to many journals. Miscellany of ideas. Were the editor/assessor to judge all submitted material themselves, approved material would solely reflect their opinion. Limited expertise. An editor/assessor cannot be expected to be sufficiently expert in all areas covered by a single journal or funding agency to adequately judge all submitted material. Reviewers are often anonymous and independent. However, some reviewers may choose to waive their anonymity, and in other limited circumstances, such as the examination of a formal complaint against the referee, or a court order, the reviewer's identity may have to be disclosed. Anonymity may be unilateral or reciprocal (single- or double-blinded reviewing). Since reviewers are normally selected from experts in the fields discussed in the article, the process of peer review helps to keep some invalid or unsubstantiated claims out of the body of published research and knowledge. Scholars will read published articles outside their limited area of detailed expertise, and then rely, to some degree, on the peer-review process to have provided reliable and credible research that they can build upon for subsequent or related research. Significant scandal ensues when an


id: scholarly-peer-review-20260505-2
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-2.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T09:53:18.276870+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
to unconditionally accept the manuscript or the proposal, to accept it in the event that its authors improve it in certain ways to reject it, but encourage revision and invite re-submission to reject it outright. During this process, the role of the referees is advisory. The editor(s) is typically under no obligation to accept the opinions of the referees. However, the editor will most often follow the advice of the referees. Furthermore, the referees in scientific publication do not act as a group, do not communicate with each other, and typically are not aware of each other's identities or evaluations. Proponents argue that if the reviewers of a paper are unknown to each other, the editor(s) can more easily verify the objectivity of the reviews. There is usually no requirement that the referees achieve consensus, with the decision instead often made by the editor(s) based on her best judgement of the arguments. In situations where multiple referees disagree substantially about the quality of a work, there are a number of strategies for reaching a decision. The paper may be rejected outright, or the editor may choose which reviewer's point the authors should address. When a publisher receives very positive and


id: scholarly-peer-review-20260505-3
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-3.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T09:53:18.276870+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
== Recruiting referees == At a journal or book publisher, the task of picking reviewers typically falls to an editor. When a manuscript arrives, an editor solicits reviews from scholars or other experts who may or may not have already expressed a willingness to referee for that journal or book division. Granting agencies typically recruit a panel or committee of reviewers in advance of the arrival of applications. Referees are supposed to inform the editor of any conflict of interests that might arise. Journals or individual editors may invite a manuscript's authors to name people whom they consider qualified to referee their work. For some journals this is a requirement of submission. Authors are sometimes also given the opportunity to name natural candidates who should be disqualified, in which case they may be asked to provide justification (typically expressed in terms of conflict of interest). Editors solicit author input in selecting referees because academic writing typically is very specialized. Editors often oversee many specialties, and can not be experts in all of them. But after an editor selects referees from the pool of candidates, the editor typically is obliged not to disclose the referees' identities to the authors, and in


id: scholarly-peer-review-20260505-4
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-4.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T09:53:18.276870+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
=== Internal and external peer review === External peer review is the ordinary process of peer review for scholarly journals. The first evidence of formal external peer reviews dates back to 1752, and it became widely used and more standardized after World War II. In this process, scholars who are not beholden to the journal or the author review submitted articles, and in doing so, restrain academic and scientific journals from using solely their own commercial interests as a guide on what to publish. Nature began requiring external peer review in 1973, and other journals quickly followed suit. Internal peer review is having a colleague in the same department or academic institution read over a paper before submitting it to a journal for external peer review. For example, economists in the same government agency may review their peers' papers, as a type of peer professional accountability. People such as Nobel laureate Dan Shechtman and organizations such as the US National Institute of Standards and Technology use internal peer review because they believe that it gives them an opportunity to improve papers before submitting them to a journal. Internal peer review is also recommended for authors with weak writing skills. Unlike


id: scholarly-peer-review-20260505-5
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-5.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T09:53:18.276870+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
=== Transparent peer review === In transparent peer review the documents relating to the publication of the paper are published alongside the paper. This includes the initial report made by the peer reviewer, the authors' response to it, and the acceptance letter by the editor. The names of the reviewers may still be kept anonymous even in transparent peer review. === Pre- and post-publication peer review === The process of peer review is not restricted to the publication process managed by academic journals. In particular, some forms of peer review can occur before an article is submitted to a journal and/or after it is published by the journal. ==== Pre-publication peer review ==== Manuscripts are typically reviewed by colleagues before submission, and if the manuscript is uploaded to preprint servers, such as ArXiv, BioRxiv or SSRN, researchers can read and comment on the manuscript. The practice to upload to preprint servers, and the activity of discussion heavily depend on the field, and it allows an open pre-publication peer review. The advantage of this method is speed and transparency of the review process. Anyone can give feedback, typically in form of comments, and typically not anonymously. These comments are also public,


id: scholarly-peer-review-20260505-6
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-6.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T09:53:18.276870+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
Result-blind peer review or results blind peer review, first proposed 1966: Reviewers receive an edited version of the submitted paper which omits the results and conclusion section. In a two-stage version, a second round of reviews or editorial judgment is based on the full paper version, which was first proposed in 1977. Conclusion-blind review, proposed by Robin Hanson in 2007 extends this further asking all authors to submit a positive and a negative version, and only after the journal has accepted the article authors reveal which is the real version. Pre-accepted articles or outcome-unbiased journals or advance publication review or registered reports or prior to results submission or early acceptance extends study pre-registration to the point that journals accepted or reject papers based on the version of the paper written before the results or conclusions have been made (an enlarged study protocol), but instead describes the theoretical justification, experimental design, and statistical analysis. Only once the proposed hypothesis and methodology have been accepted by reviewers, the authors would collect the data or analyze previously collected data. A limited variant of a pre-accepted article was The Lancet's study protocol review from 1997 to 2015 reviewed and published randomized trial protocols with


id: scholarly-peer-review-20260505-7
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-7.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T09:53:18.276870+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
Scholarly peer review has been subject to several criticisms, and various proposals for reforming the system have been suggested over the years. Many studies have emphasized the problems inherent to the process of peer review. Moreover, Ragone et al. have shown that there is a low correlation between peer review outcomes and the future impact measured by citations. Various biomedical editors in particular have expressed criticism of peer review. A Cochrane review found little empirical evidence that peer review ensures quality in biomedical research, while a second systematic review and meta-analysis found a need for evidence-based peer review in biomedicine given the paucity of assessment of the interventions designed to improve the process. To an outsider, the anonymous, pre-publication peer review process is opaque. Certain journals are accused of not carrying out stringent peer review in order to more easily expand their customer base, particularly in journals where authors pay a fee before publication. Richard Smith, MD, former editor of the British Medical Journal, has claimed that peer review is "ineffective, largely a lottery, anti-innovatory, slow, expensive, wasteful of scientific time, inefficient, easily abused, prone to bias, unable to detect fraud and irrelevant; Several studies have shown that peer review


id: scholarly-peer-review-20260505-8
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-8.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T09:53:18.276870+00:00
instance: kb-cron
parent: scholarly-peer-review-20260505-0
summary:
=== Peer review and trust === Researchers have peer-reviewed manuscripts prior to publishing them in a variety of ways since the 18th century. The main goal of this practice is to improve the relevance and accuracy of scientific discussions. Even though experts often criticize peer review for a number of reasons, the process is still often considered the "gold standard" of science. Occasionally however, peer review approves studies that are later found to be wrong and rarely deceptive or fraudulent results are discovered prior to publication. Thus, there seems to be an element of discord between the ideology behind and the practice of peer review. By failing to effectively communicate that peer review is imperfect, the message conveyed to the wider public is that studies published in peer-reviewed journals are "true" and that peer review protects the literature from flawed science. A number of well-established criticisms exist of many elements of peer review. In the following we describe cases of the wider impact inappropriate peer review can have on public understanding of scientific literature. Multiple examples across several areas of science find that scientists elevated the importance of peer review for research that was questionable or corrupted. For example, climate


id: scholarly-peer-review-20260505-9
file: data/en.wikipedia.org/wiki/Scholarly_peer_review-9.md
category: reference
title: Scholarly peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scholarly_peer_review
date_saved: 2026-05-05T09:53:18.276870+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: 2026-05-05T09:53:18.276870+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: 2026-05-05T09:53:18.276870+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: sham-peer-review-20260505-0
file: data/en.wikipedia.org/wiki/Sham_peer_review-0.md
category: reference
title: Sham peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Sham_peer_review
date_saved: 2026-05-05T09:53:19.440577+00:00
instance: kb-cron
parent: sham-peer-review-20260505-0
summary:
Sham peer review or malicious peer review is a name given to the abuse of a medical peer review process to attack a doctor for personal or other non-medical reasons. The American Medical Association conducted an investigation of medical peer review in 2007 and concluded that while it is easy to allege misconduct and 15% of surveyed physicians indicated that they were aware of peer review misuse or abuse, cases of malicious peer review able to be proven through the legal system are rare. == Legal basis == Those who maintain that sham peer review is a pervasive problem suggest that the Healthcare Quality Improvement Act (HCQIA) of 1986 allows sham reviews by granting significant immunity from liability to doctors and others who participate in peer reviews. This immunity extends to investigative activities as well as to any associated peer review hearing, whether or not it leads to a disciplinary (or other) action. The definition of a peer review body can be broad, including not only individuals but also (for example, in Oregon), "tissue committees, governing bodies or committees including medical staff committees of a [licensed] health care facility...or any other medical group in connection with bona fide medical research,


id: sham-peer-review-20260505-1
file: data/en.wikipedia.org/wiki/Sham_peer_review-1.md
category: reference
title: Sham peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Sham_peer_review
date_saved: 2026-05-05T09:53:19.440577+00:00
instance: kb-cron
parent: sham-peer-review-20260505-0
summary:
=== Roland Chalifoux === Roland Chalifoux, member of an advocacy organisation called the Semmelweis Society, had his medical license revoked in Texas in 2004 after numerous incidents including the death of a patient. The Texas State Board of Medical Examiners stated that Chalifoux's practices "constitute such a deviation from the standard of care that revocation of his license is the only sanction that will adequately protect the public". Chalifoux subsequently secured permission to practice in West Virginia, and alleges that the Texas board's actions constitute sham peer review. === Charles Williams, MD === Six years after Charles Williams, MD, an anesthesiologist was summarily suspended by University Medical Center of Southern Nevada, a federal jury in Las Vegas awarded Dr. Williams $8.8 million as compensation for the due process violations he experienced in his sham peer review. Before the trial, which began May 16, U.S. District Judge Philip Pro made a finding that Ellerton and UMC's medical staff had violated Williams' due process rights. That left only the question of damages for the jury. This case appears to be the highest jury verdict in the nation for sham peer review which has not been overturned. === Richard Chudacoff, MD === On


id: software-peer-review-20260505-0
file: data/en.wikipedia.org/wiki/Software_peer_review-0.md
category: reference
title: Software peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Software_peer_review
date_saved: 2026-05-05T09:53:20.598380+00:00
instance: kb-cron
parent: software-peer-review-20260505-0
summary:
In software development, peer review is a type of software review in which a work product (document, code, or other) is examined by the author's colleagues, in order to evaluate the work product's technical content and quality. == Purpose == The purpose of a peer review is to provide "a disciplined engineering practice for detecting and correcting defects in software artifacts, and preventing their leakage into field operations" according to the Capability Maturity Model. When performed as part of each Software development process activity, peer reviews identify problems that can be fixed early in the lifecycle. That is to say, a peer review that identifies a requirements problem during the Requirements analysis activity is cheaper and easier to fix than during the Software architecture or Software testing activities. The National Software Quality Experiment, evaluating the effectiveness of peer reviews, finds, "a favorable return on investment for software inspections; savings exceeds costs by 4 to 1". To state it another way, it is four times more costly, on average, to identify and fix a software problem later. == Distinction from other types of software review == Peer reviews are distinct from management reviews, which are conducted by management representatives rather than


id: sternberg-peer-review-controversy-20260505-0
file: data/en.wikipedia.org/wiki/Sternberg_peer_review_controversy-0.md
category: reference
title: Sternberg peer review controversy
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Sternberg_peer_review_controversy
date_saved: 2026-05-05T09:53:21.772833+00:00
instance: kb-cron
parent: sternberg-peer-review-controversy-20260505-0
summary:
The Sternberg peer review controversy concerns the conflict arising from the publication of an article supporting pseudoscientific intelligent design creationism in a scientific journal, and the subsequent questions of whether proper editorial procedures had been followed and whether it was properly peer reviewed. On 4 August 2004, an article by Stephen C. Meyer (Director of Discovery Institute's Center for Science and Culture) titled "The origin of biological information and the higher taxonomic categories", appeared in the peer-reviewed journal, Proceedings of the Biological Society of Washington. Meyer's article was a literature review article, and contained no new primary scholarship itself on the topic of intelligent design. The following month, the publisher of the journal, the Council of the Biological Society of Washington, released a statement repudiating the article and stating that their former editor, biologist Richard M. Sternberg, handled the entire review process in an unusual manner, without consultation or review from an associate editor. The position of editor was unpaid and voluntary, and Sternberg had put in his resignation from it six months earlier. Sternberg disputes the Council's statement and asserts that the paper was appropriately peer reviewed by three biologists who "concluded that [the paper] warranted publication". The same


id: sternberg-peer-review-controversy-20260505-1
file: data/en.wikipedia.org/wiki/Sternberg_peer_review_controversy-1.md
category: reference
title: Sternberg peer review controversy
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Sternberg_peer_review_controversy
date_saved: 2026-05-05T09:53:21.772833+00:00
instance: kb-cron
parent: sternberg-peer-review-controversy-20260505-0
summary:
=== Richard M. Sternberg === Richard M. Sternberg is an American evolutionary biologist who has completed a BS degree from University of South Carolina and has two PhDs: the first from 1995 in molecular evolution from Florida International University, and a second in systems science from Binghamton University. He did post-doctoral work between 1999 and 2001 at the National Museum of Natural History (NMNH) at the Smithsonian Institution, on the phylogeny of crabs. In February 2001 he began work as an invertebrate taxonomist for the National Institutes of Health at the National Center for Biotechnology Information, later becoming a staff scientist. The position allowed time for research work, and he continued at the NMNH, now as an unpaid research associate. Soon after starting work, he agreed to take on the unpaid position of Managing Editor of the peer-reviewed scientific journal, Proceedings of the Biological Society of Washington, a taxonomic journal which usually publishes descriptions of newly identified species. Also in 2001, he joined the editorial board of the Baraminology Study Group, a young earth creationist "creation science" attempt to identify and classify the created kinds mentioned in scripture. He has stated that he is an outside critic and remained skeptical


id: sternberg-peer-review-controversy-20260505-2
file: data/en.wikipedia.org/wiki/Sternberg_peer_review_controversy-2.md
category: reference
title: Sternberg peer review controversy
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Sternberg_peer_review_controversy
date_saved: 2026-05-05T09:53:21.772833+00:00
instance: kb-cron
parent: sternberg-peer-review-controversy-20260505-0
summary:
Doubts were raised whether the reviewers were evolutionary biologists. According to an article by the Society of Academic Authors Meyer said the article grew out of a presentation he made at a conference attended by Richard Sternberg where they discussed the possibility of a paper for society's journal. Observers have pointed to affiliations that in most circumstances would have disqualified Sternberg from reviewing an article on intelligent design. They note that Sternberg is a Fellow of the International Society for Complexity, Information and Design, a Discovery Institute-affiliated group dedicated to promoting intelligent design. Sternberg is also a signatory of the Discovery Institute's A Scientific Dissent From Darwinism statement "We are skeptical of claims for the ability of random mutation and natural selection to account for the complexity of life. Careful examination of the evidence for Darwinian theory should be encouraged." Sternberg claims to have also checked with a Council member and to have followed the standard practice for peer review: Three reviewers responded and were willing to review the paper; all are experts in relevant aspects of evolutionary and molecular biology and hold full-time faculty positions in major research institutions, one at an Ivy League university, another at a major


id: sternberg-peer-review-controversy-20260505-3
file: data/en.wikipedia.org/wiki/Sternberg_peer_review_controversy-3.md
category: reference
title: Sternberg peer review controversy
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Sternberg_peer_review_controversy
date_saved: 2026-05-05T09:53:21.772833+00:00
instance: kb-cron
parent: sternberg-peer-review-controversy-20260505-0
summary:
== Smithsonian controversy == After the peer review controversy became public, Sternberg filed a religious discrimination complaint against the Smithsonian Institution's National Museum of Natural History, where he had an unpaid appointment as a research associate, while employed by the National Institutes of Health. Sternberg claims that he was "targeted for retaliation and harassment" and subject to efforts to remove him from the museum in retaliation for his views in support of intelligent design. He continues to cite a letter by the United States Office of Special Counsel as supporting his version of events, despite the fact that the Office of Special Counsel did not proceed beyond its initial investigation. In its initial investigation, Office of Special Counsel concluded that there was a strategy by several managers to force Sternberg out of the Smithsonian Institution and that there was a strong religious and political component to the actions taken after the publication of the Meyer article. For example, one of the emails sent between Smithsonian employees stated, "Scientists have been perfectly willing to let these people alone in their churches, but now it looks like these people are coming out and invading our schools, biology classes, museums and now our


id: technical-peer-review-20260505-0
file: data/en.wikipedia.org/wiki/Technical_peer_review-0.md
category: reference
title: Technical peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Technical_peer_review
date_saved: 2026-05-05T09:53:22.988147+00:00
instance: kb-cron
parent: technical-peer-review-20260505-0
summary:
In engineering, technical peer review is a well defined review process for finding and correcting defects conducted by a team of peers with assigned roles. Technical peer reviews are carried out by peers representing areas of life cycle affected by material being reviewed (usually limited to 6 or fewer people). Technical peer reviews are held within development phases, between milestone reviews, on completed products, or on completed portions of products. A technical peer review may also be called an engineering peer review, a product peer review, a peer review/inspection or an inspection. == Overview == The purpose of a technical peer review is to remove defects as early as possible in the development process. By removing defects at their origin (e.g., requirements and design documents, test plans and procedures, software code, etc.), technical peer reviews prevent defects from propagating through multiple phases and work products and reduce the overall amount of rework necessary on projects. Improved team efficiency is a side effect (e.g., by improving team communication, integrating the viewpoints of various engineering specialty disciplines, more quickly bringing new members up to speed, and educating project members about effective development practices). In CMMI, peer reviews are used as a principal


id: u-s-government-peer-review-policies-20260505-0
file: data/en.wikipedia.org/wiki/U.S._Government_peer_review_policies-0.md
category: reference
title: U.S. Government peer review policies
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/U.S._Government_peer_review_policies
date_saved: 2026-05-05T09:53:24.170660+00:00
instance: kb-cron
parent: u-s-government-peer-review-policies-20260505-0
summary:
Most federal regulatory agencies in the United States government must comply with specific peer review requirements before the agencies publicly disseminate certain scientific information. These requirements were published in a Peer Review Bulletin issued by the White House Office of Management and Budget (OMB), which establishes "government-wide standards concerning when peer review is required and, if required, what type of peer review processes are appropriate." == Bulletin == OMB's peer review bulletin requires that US federal regulatory agencies submit all "influential scientific information" to peer review before the information is publicly disseminated. The Bulletin defines "scientific information" as: factual inputs, data, models, analyses, technical information, or scientific assessments related to such disciplines as the behavioral and social sciences, public health and medical sciences, life and earth sciences, engineering, or physical sciences. This Bulletin defines "influential scientific information" as scientific information the agency reasonably can determine will have or does have a clear and substantial impact on important public policies or private sector decisions. In the term 'influential scientific information,' the term 'influential' should be interpreted consistently with OMB's government-wide information quality guidelines and the information quality guidelines of the agency. As noted in the preceding quotation, the Bulletin must be


id: united-states-office-of-research-integrity-20260505-0
file: data/en.wikipedia.org/wiki/United_States_Office_of_Research_Integrity-0.md
category: reference
title: United States Office of Research Integrity
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/United_States_Office_of_Research_Integrity
date_saved: 2026-05-05T09:53:25.437954+00:00
instance: kb-cron
parent: united-states-office-of-research-integrity-20260505-0
summary:
The Office of Research Integrity (ORI) is a U.S. government agency that focuses on research integrity, especially in health. It was created when the Office of Scientific Integrity (OSI) in the National Institutes of Health (NIH) and the Office of Scientific Integrity Review (OSIR) in the Office of the Assistant Secretary for Health merged in May 1992. The Office of Research Integrity oversees and directs Public Health Service (PHS) research integrity activities on behalf of the Secretary of Health and Human Services, except for the regulatory research integrity activities of the Food and Drug Administration. Organizationally, ORI is located within the Office of the Assistant Secretary for Health (OASH) within the Office of the Secretary of Health and Human Services (OS), in the Department of Health and Human Services (HHS). == Activities == ORI carries out its responsibility by: Developing policies, procedures and regulations related to the detection, investigation, and prevention of research misconduct and the responsible conduct of research; Reviewing and monitoring research misconduct investigations conducted by applicant and awardee institutions, intramural research programs, and the Office of Inspector General in the Department of Health and Human Services (HHS); Recommending research misconduct findings and administrative actions to the Assistant


id: writers-workshop-activity-20260505-0
file: data/en.wikipedia.org/wiki/Writers_workshop_(activity)-0.md
category: reference
title: Writers workshop (activity)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Writers_workshop_(activity)
date_saved: 2026-05-05T09:53:27.785028+00:00
instance: kb-cron
parent: writers-workshop-activity-20260505-0
summary:
A writing workshop is a group session where writers gather to share, critique and improve their work. Various models of writing workshops have been developed over time to suit different educational settings and writing goals. Workshop attendance might be restricted to a select group (such as a writers' circle or class) or open to the public. == Workshop models == Most workshops include some kind of sharing of work, reflection upon and discussion of the work, and often suggestions for improvement. There may or may not be a facilitator, who is usually an established writer or teacher, but can be a volunteer from among the group. Workshops can work on very established rules and formats, or can be more freeform. === Traditional workshop model === The traditional workshop model, also known as the Iowa model, was developed at the University of Iowa Writers' Workshop in the mid-20th century, under the directorship of Paul Engle. It typically involves: Submission: Participants submit their writing in advance. Each participant is expected to read everyone else's work and come with feedback. Critique: During the workshop, participants take turns to provide feedback on each person's work in turn. When it's their turn to receive group


id: data-analysis-20260505-0
file: data/en.wikipedia.org/wiki/Data_analysis-0.md
category: reference
title: Data analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Data_analysis
date_saved: 2026-05-05T09:53:29.395055+00:00
instance: kb-cron
parent: data-analysis-20260505-0
summary:
Data analysis is the process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today's business world, data analysis plays an important role in making decisions more scientific and helping businesses operate more effectively. It is widely used in fields such as business analytics, healthcare, and artificial intelligence to extract meaningful insights from data. Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. In statistical applications, data analysis can be divided into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA). EDA focuses on discovering new features in the data, while CDA focuses on confirming or falsifying existing hypotheses. Predictive analytics focuses on the application of statistical models for predictive forecasting or classification, while text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a


id: data-analysis-20260505-1
file: data/en.wikipedia.org/wiki/Data_analysis-1.md
category: reference
title: Data analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Data_analysis
date_saved: 2026-05-05T09:53:29.395055+00:00
instance: kb-cron
parent: data-analysis-20260505-0
summary:
Mathematical formulas or mathematical models (supported by algorithms) may be applied to the data in order to identify relationships among the variables; for example, checking for correlation and by determining whether or not there is the presence of causality. In general terms, models may be developed to evaluate a specific variable based on other variable(s) contained within the dataset, with some residual error depending on the implemented model's accuracy (e.g., Data = Model + Error). Inferential statistics utilizes techniques that measure the relationships between particular variables. For example, regression analysis may be used to model whether a change in advertising (independent variable X), provides an explanation for the variation in sales (dependent variable Y), i.e. is Y a function of X? This can be described as (Y = aX + b + error), where the model is designed such that (a) and (b) minimize the error when the model predicts Y for a given range of values of X. === Data product === A data product is a computer application that takes data inputs and generates outputs, feeding them back into the environment. It may be based on a model or algorithm. For instance, an application that analyzes data about


id: data-analysis-20260505-2
file: data/en.wikipedia.org/wiki/Data_analysis-2.md
category: reference
title: Data analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Data_analysis
date_saved: 2026-05-05T09:53:29.395055+00:00
instance: kb-cron
parent: data-analysis-20260505-0
summary:
McKinsey and Company named a technique for breaking down a quantitative problem into its component parts called the MECE principle. MECE means "Mutually Exclusive and Collectively Exhaustive". Each layer can be broken down into its components; each of the sub-components must be mutually exclusive of each other and collectively add up to the layer above them. For example, profit by definition can be broken down into total revenue and total cost. Analysts may use robust statistical measurements to solve certain analytical problems. Hypothesis testing is used when a particular hypothesis about the true state of affairs is made by the analyst and data is gathered to determine whether that hypothesis is true or false. For example, the hypothesis might be that "Unemployment has no effect on inflation", which relates to an economics concept called the Phillips Curve. Hypothesis testing involves considering the likelihood of Type I and type II errors, which relate to whether the data supports accepting or rejecting the hypothesis. Regression analysis may be used when the analyst is trying to determine the extent to which independent variable X affects dependent variable Y (e.g., "To what extent do changes in the unemployment rate (X) affect the inflation rate


id: data-analysis-20260505-3
file: data/en.wikipedia.org/wiki/Data_analysis-3.md
category: reference
title: Data analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Data_analysis
date_saved: 2026-05-05T09:53:29.395055+00:00
instance: kb-cron
parent: data-analysis-20260505-0
summary:
=== Initial data analysis === The most important distinction between the initial data analysis phase and the main analysis phase is that during initial data analysis one refrains from any analysis that is aimed at answering the original research question. The initial data analysis phase is guided by the following four questions: ==== Quality of data ==== The quality of the data should be checked as early as possible. Data quality can be assessed in several ways, using different types of analysis: frequency counts, descriptive statistics (mean, standard deviation, median), normality (skewness, kurtosis, frequency histograms), normal imputation is needed. Analysis of extreme observations: outlying observations in the data are analyzed to see if they seem to disturb the distribution. Comparison and correction of differences in coding schemes: variables are compared with coding schemes of variables external to the data set, and possibly corrected if coding schemes are not comparable. Test for common-method variance. The choice of analyses to assess the data quality during the initial data analysis phase depends on the analyses that will be conducted in the main analysis phase. ==== Quality of measurements ==== The quality of the measurement instruments should only be checked during the initial data


id: data-analysis-20260505-4
file: data/en.wikipedia.org/wiki/Data_analysis-4.md
category: reference
title: Data analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Data_analysis
date_saved: 2026-05-05T09:53:29.395055+00:00
instance: kb-cron
parent: data-analysis-20260505-0
summary:
==== Exploratory and confirmatory approaches ==== In the main analysis phase, either an exploratory or confirmatory approach can be adopted. Usually the approach is decided before data is collected. In an exploratory analysis no clear hypothesis is stated before analysing the data, and the data is searched for models that describe the data well. In a confirmatory analysis, clear hypotheses about the data are tested. Exploratory data analysis should be interpreted carefully. When testing multiple models at once there is a high chance on finding at least one of them to be significant, but this can be due to a type 1 error. It is important to always adjust the significance level when testing multiple models with, for example, a Bonferroni correction. Also, one should not follow up an exploratory analysis with a confirmatory analysis in the same dataset. An exploratory analysis is used to find ideas for a theory, but not to test that theory as well. When a model is found exploratory in a dataset, then following up that analysis with a confirmatory analysis in the same dataset could simply mean that the results of the confirmatory analysis are due to the same type 1 error that resulted


id: causal-analysis-20260505-0
file: data/en.wikipedia.org/wiki/Causal_analysis-0.md
category: reference
title: Causal analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Causal_analysis
date_saved: 2026-05-05T09:53:30.564050+00:00
instance: kb-cron
parent: causal-analysis-20260505-0
summary:
Causal analysis is the field of experimental design and statistics pertaining to establishing cause and effect. Typically it involves establishing four elements: correlation, sequence in time (that is, causes must occur before their proposed effect), a plausible physical or information-theoretical mechanism for an observed effect to follow from a possible cause, and eliminating the possibility of common and alternative ("special") causes. Such analysis usually involves one or more controlled or natural experiments. == Motivation == Data analysis is primarily concerned with causal questions. For example, did the fertilizer cause the crops to grow? Or, can a given sickness be prevented? Or, why is my friend depressed? The potential outcomes and regression analysis techniques handle such queries when data is collected using designed experiments. Data collected in observational studies require different techniques for causal inference (because, for example, of issues such as confounding). Causal inference techniques used with experimental data require additional assumptions to produce reasonable inferences with observation data. The difficulty of causal inference under such circumstances is often summed up as "correlation does not imply causation". == In philosophy and physics == The nature of causality is systematically investigated in several academic disciplines, including philosophy and physics. In academia,


id: ads-txt-20260505-0
file: data/en.wikipedia.org/wiki/Ads.txt-0.md
category: reference
title: Ads.txt
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Ads.txt
date_saved: 2026-05-05T09:53:31.690614+00:00
instance: kb-cron
parent: ads-txt-20260505-0
summary:
ads.txt (Authorized Digital Sellers) is an initiative from IAB Technology Laboratory. It specifies a text file that companies can host on their web servers, listing the other companies authorized to sell their products or services. This is designed to allow online buyers to check the validity of the sellers from whom they buy, for the purposes of internet fraud prevention. == State of adoption == By November 2017, more than 44% of publishers had ads.txt files. More than 90,000 sites were using ads.txt, up from 3,500 in September 2017, according to Pixalate. Among the top 1,000 sites that sold programmatic ads, 57 percent had ads.txt files, compared to 16 percent in September, per Pixalate. Latest adoption data per FirstImpression.io's Ads.txt Industry Dashboard: Google has been an active proponent of ads.txt. From the end of October 2017 Google Display & Video 360 only buys inventory from sources identified as authorized sellers in a publisher's ads.txt file, when a file is available. ads.txt may become a requirement for Display & Video 360. == File format == The IAB's ads.txt specification dictates the formatting of ads.txt files, which can contain three types of record; data records, variables and comments. An ads.txt file can


id: augmented-analytics-20260505-0
file: data/en.wikipedia.org/wiki/Augmented_Analytics-0.md
category: reference
title: Augmented Analytics
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Augmented_Analytics
date_saved: 2026-05-05T09:53:32.830056+00:00
instance: kb-cron
parent: augmented-analytics-20260505-0
summary:
Augmented Analytics is an approach of data analytics that employs the use of machine learning and natural language processing to automate analysis processes normally done by a specialist or data scientist. The term was introduced in 2017 by Rita Sallam, Cindi Howson, and Carlie Idoine in a Gartner research paper. Augmented analytics is based on business intelligence and analytics. In the graph extraction step, data from different sources are investigated. == Defining Augmented Analytics == Machine Learning – a systematic computing method that uses algorithms to sift through data to identify relationships, trends, and patterns. It is a process that allows algorithms to dynamically learn from data instead of having a set base of programmed rules. Natural language generation (NLG) – a software capability that takes unstructured data and translates it into plain-English, readable, language. Automating Insights – using machine learning algorithms to automate data analysis processes. Natural Language Query – enabling users to query data using business terms that are either typed onto a search box or spoken. == Data Democratization == Data Democratization is the democratizing data access in order to relieve data congestion and get rid of any sense of data "gatekeepers". This process must be implemented


id: badir-20260505-0
file: data/en.wikipedia.org/wiki/BADIR-0.md
category: reference
title: BADIR
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/BADIR
date_saved: 2026-05-05T09:53:34.122536+00:00
instance: kb-cron
parent: badir-20260505-0
summary:
The BADIR (pronounced /ˈbaːdɪr/) is a structured data science and data analytics process designed to enhance data-driven decision-making within organizations by addressing both analytical output as well as usefulness to management. It was developed by Piyanka Jain and Puneet Sharma and first published in the 2014 book “Behind Every Good Decision”. == Overview == The BADIR Framework employs a hypothesis-driven approach that involves understanding business objectives and challenges, analysis planning before acquiring and ensuring the quality of relevant data, applying analytics to derive insights and the potential impact on the business challenge, developing actionable recommendations aligned with strategic goals, and implementing decisions while monitoring outcomes and making necessary adjustments. == Key Components of BADIR == The BADIR Framework consists of interrelated components essential for decision-making. Its main assertion is that if data analytics does not drive business impact, then it is just statistics, not analytics. The acronym in the framework stands for the following five steps: B = Business Question: The first step in the framework is to define the real business question. Market trends, customer feedback, competitor actions, etc. are data sources commonly used by businesses. However, these data alone do not help teams to make decisions. A =


id: base-effect-20260505-0
file: data/en.wikipedia.org/wiki/Base_effect-0.md
category: reference
title: Base effect
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Base_effect
date_saved: 2026-05-05T09:53:35.330547+00:00
instance: kb-cron
parent: base-effect-20260505-0
summary:
The base effect is a mathematical effect that originates from the fact that a given percentage of a reference value, is not the same as the absolute difference of the same given percentage of a much larger or smaller reference value. E.g. 1% of a GDP of US$1 million is not equal to 1% of GDP of US$1 billion in terms of absolute difference. The reference value is common called a base year in economics. A low base effect is the tendency of an absolute change from a low initial amount to be translated into a larger percentage change, while a high base effect would be the tendency of an absolute change from a high initial amount to be translated into a smaller percentage change. Because of the base effect percentages in time series analysis can be misleading, in particular when percentages are compounded annually over a period of many years. A high base effect can mislead because of decreasing percentages even while the underlying absolute difference is increasing over time as much as the measured value or population increases over time. Also, a low base effect can mislead because of increasing percentages even while the absolute difference is decreasing


id: big-data-20260505-0
file: data/en.wikipedia.org/wiki/Big_data-0.md
category: reference
title: Big data
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Big_data
date_saved: 2026-05-05T09:53:36.506913+00:00
instance: kb-cron
parent: big-data-20260505-0
summary:
Big data primarily refers to data sets that are too large or complex to be dealt with by traditional data-processing software. Data with many entries (rows) offers greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate. Big data analysis challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy, and data sources. Big data was originally associated with three key concepts: volume, variety, and velocity. The analysis of big data that have only volume, velocity, and variety can pose challenges in sampling. A fourth concept, veracity, which refers to the level of reliability of data, was thus added. Without sufficient investment in expertise to ensure big data veracity, the volume and variety of data can produce costs and risks that exceed an organization's capacity to create and capture value from big data. Current usage of the term big data tends to refer to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from big data, and seldom to a particular size of data set. "There is little doubt that the quantities of data now


id: big-data-20260505-1
file: data/en.wikipedia.org/wiki/Big_data-1.md
category: reference
title: Big data
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Big_data
date_saved: 2026-05-05T09:53:36.506913+00:00
instance: kb-cron
parent: big-data-20260505-0
summary:
Volume The quantity of generated and stored data. The size of the data determines the value and potential insight, and whether it can be considered big data or not. The size of big data is usually larger than terabytes and petabytes. Variety The type and nature of the data. Earlier technologies like RDBMSs were capable to handle structured data efficiently and effectively. However, the change in type and nature from structured to semi-structured or unstructured challenged the existing tools and technologies. Big data technologies evolved with the prime intention to capture, store, and process the semi-structured and unstructured (variety) data generated with high speed (velocity), and huge in size (volume). Later, these tools and technologies were explored and used for handling structured data also but preferable for storage. Eventually, the processing of structured data was still kept as optional, either using big data or traditional RDBMSs. This helps in analyzing data towards effective usage of the hidden insights exposed from the data collected via social media, log files, sensors, etc. Big data draws from text, images, audio, video; plus it completes missing pieces through data fusion. Velocity The speed at which the data is generated and processed to meet the


id: big-data-20260505-2
file: data/en.wikipedia.org/wiki/Big_data-2.md
category: reference
title: Big data
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Big_data
date_saved: 2026-05-05T09:53:36.506913+00:00
instance: kb-cron
parent: big-data-20260505-0
summary:
== Architecture == Big data repositories have existed in many forms, often built by corporations with a special need. Commercial vendors historically offered parallel database management systems for big data beginning in the 1990s. For many years, WinterCorp published the largest database report. Teradata Corporation in 1984 marketed the parallel processing DBC 1012 system. Teradata systems were the first to store and analyze 1 terabyte of data in 1992. Hard disk drives were 2.5 GB in 1991 so the definition of big data continuously evolves. Teradata installed the first petabyte class RDBMS based system in 2007. As of 2017, there are a few dozen petabyte class Teradata relational databases installed, the largest of which exceeds 50 PB. Systems up until 2008 were 100% structured relational data. Since then, Teradata has added semi structured data types including XML, JSON, and Avro. In 2000, Seisint Inc. (now LexisNexis Risk Solutions) developed a C++-based distributed platform for data processing and querying known as the HPCC Systems platform. This system automatically partitions, distributes, stores and delivers structured, semi-structured, and unstructured data across multiple commodity servers. Users can write data processing pipelines and queries in a declarative dataflow programming language called ECL. Data analysts working


id: big-data-20260505-3
file: data/en.wikipedia.org/wiki/Big_data-3.md
category: reference
title: Big data
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Big_data
date_saved: 2026-05-05T09:53:36.506913+00:00
instance: kb-cron
parent: big-data-20260505-0
summary:
Big data has increased the demand of information management specialists so much so that Software AG, Oracle Corporation, IBM, Microsoft, SAP, EMC, HP, and Dell have spent more than $15 billion on software firms specializing in data management and analytics. In 2010, this industry was worth more than $100 billion and was growing at almost 10 percent a year, about twice as fast as the software business as a whole. Developed economies increasingly use data-intensive technologies. There are 4.6 billion mobile-phone subscriptions worldwide, and between 1 billion and 2 billion people accessing the internet. Between 1990 and 2005, more than 1 billion people worldwide entered the middle class, which means more people became more literate, which in turn led to information growth. The world's effective capacity to exchange information through telecommunication networks was 281 petabytes in 1986, 471 petabytes in 1993, 2.2 exabytes in 2000, 65 exabytes in 2007 and predictions put the amount of internet traffic at 667 exabytes annually by 2014. According to one estimate, one-third of the globally stored information is in the form of alphanumeric text and still image data, which is the format most useful for most big data applications. This also shows the potential


id: big-data-20260505-4
file: data/en.wikipedia.org/wiki/Big_data-4.md
category: reference
title: Big data
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Big_data
date_saved: 2026-05-05T09:53:36.506913+00:00
instance: kb-cron
parent: big-data-20260505-0
summary:
=== Healthcare === Big data analytics has been used in healthcare in providing personalized medicine and prescriptive analytics, clinical risk intervention and predictive analytics, waste and care variability reduction, automated external and internal reporting of patient data, standardized medical terms and patient registries. Some areas of improvement are more aspirational than actually implemented. The level of data generated within healthcare systems is not trivial. With the added adoption of mHealth, eHealth and wearable technologies the volume of data will continue to increase. This includes electronic health record data, imaging data, patient generated data, sensor data, and other forms of difficult to process data. There is now an even greater need for such environments to pay greater attention to data and information quality. "Big data very often means 'dirty data' and the fraction of data inaccuracies increases with data volume growth." Human inspection at the big data scale is impossible and there is a desperate need in health service for intelligent tools for accuracy and believability control and handling of information missed. While extensive information in healthcare is now electronic, it fits under the big data umbrella as most is unstructured and difficult to use. The use of big data in


id: big-data-20260505-5
file: data/en.wikipedia.org/wiki/Big_data-5.md
category: reference
title: Big data
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Big_data
date_saved: 2026-05-05T09:53:36.506913+00:00
instance: kb-cron
parent: big-data-20260505-0
summary:
=== Survey science === Compared to survey-based data collection, big data has low cost per data point, applies analysis techniques via machine learning and data mining, and includes diverse and new data sources, e.g., registers, social media, apps, and other forms digital data. Since 2018, survey scientists have started to examine how big data and survey science can complement each other to allow researchers and practitioners to improve the production of statistics and its quality. There have been three Big Data Meets Survey Science (BigSurv) conferences in 2018, 2020 (virtual), 2023, and as of 2023 one conference forthcoming in 2025, a special issue in the Social Science Computer Review, a special issue in Journal of the Royal Statistical Society, and a special issue in EP J Data Science, and a book called Big Data Meets Social Sciences edited by Craig Hill and five other Fellows of the American Statistical Association. In 2021, the founding members of BigSurv received the Warren J. Mitofsky Innovators Award from the American Association for Public Opinion Research. === Marketing === Big data is notable in marketing due to the constant "datafication" of everyday consumers of the internet, in which all forms of data are tracked.


id: big-data-20260505-6
file: data/en.wikipedia.org/wiki/Big_data-6.md
category: reference
title: Big data
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Big_data
date_saved: 2026-05-05T09:53:36.506913+00:00
instance: kb-cron
parent: big-data-20260505-0
summary:
=== Science === The Large Hadron Collider experiments represent about 150 million sensors delivering data 40 million times per second. There are nearly 600 million collisions per second. After filtering and refraining from recording more than 99.99995% of these streams, there are 1,000 collisions of interest per second. As a result, only working with less than 0.001% of the sensor stream data, the data flow from all four LHC experiments represents 25 petabytes annual rate before replication (as of 2012). This becomes nearly 200 petabytes after replication. If all sensor data were recorded in LHC, the data flow would be extremely hard to work with. The data flow would exceed 150 million petabytes annual rate, or nearly 500 exabytes per day, before replication. To put the number in perspective, this is equivalent to 500 quintillion (5×1020) bytes per day, almost 200 times more than all the other sources combined in the world. The Square Kilometre Array is a radio telescope built of thousands of antennas. It is expected to be operational by 2024. Collectively, these antennas are expected to gather 14 exabytes and store one petabyte per day. It is considered one of the most ambitious scientific projects ever undertaken.


id: big-data-20260505-7
file: data/en.wikipedia.org/wiki/Big_data-7.md
category: reference
title: Big data
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Big_data
date_saved: 2026-05-05T09:53:36.506913+00:00
instance: kb-cron
parent: big-data-20260505-0
summary:
== Research activities == Encrypted search and cluster formation in big data were demonstrated in March 2014 at the American Society of Engineering Education. Gautam Siwach engaged at Tackling the challenges of Big Data by MIT Computer Science and Artificial Intelligence Laboratory and Amir Esmailpour at the UNH Research Group investigated the key features of big data as the formation of clusters and their interconnections. They focused on the security of big data and the orientation of the term towards the presence of different types of data in an encrypted form at cloud interface by providing the raw definitions and real-time examples within the technology. Moreover, they proposed an approach for identifying the encoding technique to advance towards an expedited search over encrypted text leading to the security enhancements in big data. In March 2012, The White House announced a national "Big Data Initiative" that consisted of six federal departments and agencies committing more than $200 million to big data research projects. The initiative included a National Science Foundation "Expeditions in Computing" grant of $10 million over five years to the AMPLab at the University of California, Berkeley. The AMPLab also received funds from DARPA, and over a dozen industrial


id: big-data-20260505-8
file: data/en.wikipedia.org/wiki/Big_data-8.md
category: reference
title: Big data
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Big_data
date_saved: 2026-05-05T09:53:36.506913+00:00
instance: kb-cron
parent: big-data-20260505-0
summary:
=== Critiques of the big data paradigm === "A crucial problem is that we do not know much about the underlying empirical micro-processes that lead to the emergence of the[se] typical network characteristics of Big Data." In their critique, Snijders, Matzat, and Reips point out that often very strong assumptions are made about mathematical properties that may not at all reflect what is really going on at the level of micro-processes. Mark Graham has leveled broad critiques at Chris Anderson's assertion that big data will spell the end of theory: focusing in particular on the notion that big data must always be contextualized in their social, economic, and political contexts. Even as companies invest eight- and nine-figure sums to derive insight from information streaming in from suppliers and customers, less than 40% of employees have sufficiently mature processes and skills to do so. To overcome this insight deficit, big data, no matter how comprehensive or well analyzed, must be complemented by "big judgment", according to an article in the Harvard Business Review. Much in the same line, it has been pointed out that the decisions based on the analysis of big data are inevitably "informed by the world as it


id: big-data-20260505-9
file: data/en.wikipedia.org/wiki/Big_data-9.md
category: reference
title: Big data
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Big_data
date_saved: 2026-05-05T09:53:36.506913+00:00
instance: kb-cron
parent: big-data-20260505-0
summary:
=== Critiques of big data execution === Ulf-Dietrich Reips and Uwe Matzat wrote in 2014 that big data had become a "fad" in scientific research. Researcher danah boyd has raised concerns about the use of big data in science neglecting principles such as choosing a representative sample by being too concerned about handling the huge amounts of data. This approach may lead to results that have a bias in one way or another. Integration across heterogeneous data resources—some that might be considered big data and others not—presents formidable logistical as well as analytical challenges, but many researchers argue that such integrations are likely to represent the most promising new frontiers in science. In the provocative article "Critical Questions for Big Data", the authors title big data a part of mythology: "large data sets offer a higher form of intelligence and knowledge [...], with the aura of truth, objectivity, and accuracy". Users of big data are often "lost in the sheer volume of numbers", and "working with Big Data is still subjective, and what it quantifies does not necessarily have a closer claim on objective truth". Recent developments in BI domain, such as pro-active reporting especially target improvements in the usability


id: collocation-remote-sensing-20260505-0
file: data/en.wikipedia.org/wiki/Collocation_(remote_sensing)-0.md
category: reference
title: Collocation (remote sensing)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Collocation_(remote_sensing)
date_saved: 2026-05-05T09:53:37.734675+00:00
instance: kb-cron
parent: collocation-remote-sensing-20260505-0
summary:
Collocation is a procedure used in remote sensing to match measurements from two or more different instruments. This is done for two main reasons: for validation purposes when comparing measurements of the same variable, and to relate measurements of two different variables either for performing retrievals or for prediction. In the second case the data is later fed into some type of statistical inverse method such as an artificial neural network, statistical classification algorithm, kernel estimator or a linear least squares. In principle, most collocation problems can be solved by a nearest neighbor search, but in practice there are many other considerations involved and the best method is highly specific to the particular matching of instruments. Here we deal with some of the most important considerations along with specific examples. There are at least two main considerations when performing collocations. The first is the sampling pattern of the instrument. Measurements may be dense and regular, such as those from a cross-track scanning satellite instrument. In this case, some form of interpolation may be appropriate. On the other hand, the measurements may be sparse, such as a one-off field campaign designed for some particular validation exercise. The second consideration is the


id: collocation-remote-sensing-20260505-1
file: data/en.wikipedia.org/wiki/Collocation_(remote_sensing)-1.md
category: reference
title: Collocation (remote sensing)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Collocation_(remote_sensing)
date_saved: 2026-05-05T09:53:37.734675+00:00
instance: kb-cron
parent: collocation-remote-sensing-20260505-0
summary:
== Interpolation == For gridded data such as assimilation or reanalysis data, interpolation is likely the most appropriate method for performing any type of comparison. A specific point in both physical position and time is easy to locate within the grid and interpolation performed between the nearest neighbors. Linear interpolation (bilinear, trilinear etc.) is the most common, though cubic is used as well but is probably not worth the extra computational overhead. If the variable of interest has a relatively smooth rate of change (temperature is a good example of this because it has a diffusion mechanism, radiative transfer, not available to other atmospheric variables), then interpolation can eliminate much of the error associated with collocation. Interpolation may also be appropriate for many types of satellite instruments, for instance a cross-track scanning instrument like Landsat. In data derived from the Advanced Microwave Sounding Unit (AMSU) are interpolated (although not for the purposes of collocation) using a slight variation of trilinear interpolation. Since measurements within a single scan track are laid out in an approximately rectangular grid, bilinear interpolation can be performed. By searching for the nearest overlapping scan track both forwards and backwards in time, the spatial interpolates can then


id: combinatorial-data-analysis-20260505-0
file: data/en.wikipedia.org/wiki/Combinatorial_data_analysis-0.md
category: reference
title: Combinatorial data analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Combinatorial_data_analysis
date_saved: 2026-05-05T09:53:38.930340+00:00
instance: kb-cron
parent: combinatorial-data-analysis-20260505-0
summary:
In statistics, combinatorial data analysis (CDA) is the study of data sets where the order in which objects are arranged is important. CDA can be used either to determine how well a given combinatorial construct reflects the observed data, or to search for a suitable combinatorial construct that does fit the data. == See also == Cluster analysis Geometric data analysis Structured data analysis (statistics) Seriation (statistics) == References ==


id: concept-drift-20260505-0
file: data/en.wikipedia.org/wiki/Concept_drift-0.md
category: reference
title: Concept drift
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Concept_drift
date_saved: 2026-05-05T09:53:40.159460+00:00
instance: kb-cron
parent: concept-drift-20260505-0
summary:
In predictive analytics, data science, machine learning and related fields, concept drift or drift is an evolution of data that invalidates the data model. It happens when the statistical properties of the target variable, which the model is trying to predict, change over time in unforeseen ways. This causes problems because the predictions become less accurate as time passes. Drift detection and drift adaptation are of paramount importance in the fields that involve dynamically changing data and data models. == Predictive model decay == In machine learning and predictive analytics this drift phenomenon is called concept drift. In machine learning, a common element of a data model are the statistical properties, such as probability distribution of the actual data. If they deviate from the statistical properties of the training data set, then the learned predictions may become invalid, if the drift is not addressed. == Data configuration decay == Another important area is software engineering, where three types of data drift affecting data fidelity may be recognized. Changes in the software environment ("infrastructure drift") may invalidate software infrastructure configuration. "Structural drift" happens when the data schema changes, which may invalidate databases. "Semantic drift" is changes in the meaning of data


id: concept-drift-20260505-1
file: data/en.wikipedia.org/wiki/Concept_drift-1.md
category: reference
title: Concept drift
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Concept_drift
date_saved: 2026-05-05T09:53:40.159460+00:00
instance: kb-cron
parent: concept-drift-20260505-0
summary:
=== Remedy methods === DDM (Drift Detection Method): detects drift by monitoring the model's error rate over time. When the error rate passes a set threshold, it enters a warning phase, and if it passes another threshold, it enters a drift phase. EDDM (Early Drift Detection Method): improves DDM's detection rate by tracking the average distance between two errors instead of only the error rate. ADWIN (Adaptive Windowing): dynamically stores a window of recent data and warns the user if it detects a significant change between the statistics of the window's earlier data compared to more recent data. KSWIN (Kolmogorov–Smirnov Windowing): detects drift based on the Kolmogorov-Smirnov statistical test. DDM and EDDM: Concept Drift Detection online supervised methods that rely on sequential error monitoring to estimate the evolving error rate. ADWIN and KSWIN: Windowing maintain a "window", a subset of the most recent data, of the data stream, which it checks for statistical differences across the window. == Applications in security == Concept drift is a recurring issue in security analytics, especially in malware and intrusion detection. In these systems, models are often trained on past logs, binaries or network traces, but the behaviour of attackers changes over time as


id: concept-drift-20260505-2
file: data/en.wikipedia.org/wiki/Concept_drift-2.md
category: reference
title: Concept drift
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Concept_drift
date_saved: 2026-05-05T09:53:40.159460+00:00
instance: kb-cron
parent: concept-drift-20260505-0
summary:
=== Concept Drift, Concept Evolution, and Adversarial Manipulation === Concept drift refers to when the relationship between input features and their corresponding labels changes gradually over time. Consider a simple example: phishing emails. The older kinds of attacks would leverage prominent signals, such as the presence of the word "lottery," while newer variants would introduce more polished phrasing, such as "lottery and not scam," in attempts to appear more legitimate and evade detection. This is a form of concept drift because the underlying class—phishing—remains the same, but the feature patterns that define it shift as attackers adapt. It is important, though, to distinguish concept drift from its related but inherently different challenge: concept evolution.“Concept evolution” occurs when an entirely new attack family emerges with feature patterns the model has never encountered. The model is exposed to new features and patterns that were not present in the training data. In the multi-class setting, it means a completely new label is introduced. For the binary setting (benign vs malicious), a completely new malicious family is introduced; however, the model still tries to make predictions based on outdated feature patterns. One such example would be if a model is trained with both trojan


id: concept-drift-20260505-3
file: data/en.wikipedia.org/wiki/Concept_drift-3.md
category: reference
title: Concept drift
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Concept_drift
date_saved: 2026-05-05T09:53:40.159460+00:00
instance: kb-cron
parent: concept-drift-20260505-0
summary:
==== Real ==== USP Data Stream Repository, 27 real-world stream datasets with concept drift compiled by Souza et al. (2020). Access Airline, approximately 116 million flight arrival and departure records (cleaned and sorted) compiled by E. Ikonomovska. Reference: Data Expo 2009 Competition [1]. Access Chess.com (online games) and Luxembourg (social survey) datasets compiled by I. Zliobaite. Access ECUE spam 2 datasets each consisting of more than 10,000 emails collected over a period of approximately 2 years by an individual. Access from S.J.Delany webpage Elec2, electricity demand, 2 classes, 45,312 instances. Reference: M. Harries, Splice-2 comparative evaluation: Electricity pricing, Technical report, The University of South Wales, 1999. Access from J.Gama webpage. Comment on applicability. PAKDD'09 competition data represents the credit evaluation task. It is collected over a five-year period. Unfortunately, the true labels are released only for the first part of the data. Access Sensor stream and Power supply stream datasets are available from X. Zhu's Stream Data Mining Repository. Access SMEAR is a benchmark data stream with a lot of missing values. Environment observation data over 7 years. Predict cloudiness. Access Text mining, a collection of text mining datasets with concept drift, maintained by I. Katakis. Access Gas Sensor Array


id: continuous-analytics-20260505-0
file: data/en.wikipedia.org/wiki/Continuous_analytics-0.md
category: reference
title: Continuous analytics
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Continuous_analytics
date_saved: 2026-05-05T09:53:41.378945+00:00
instance: kb-cron
parent: continuous-analytics-20260505-0
summary:
Continuous analytics is a data science process that abandons ETLs and complex batch data pipelines in favor of cloud-native and microservices paradigms. Continuous data processing enables real time interactions and immediate insights with fewer resources. == Defined == Analytics is the application of mathematics and statistics to big data. Data scientists write analytics programs to look for solutions to business problems, like forecasting demand or setting an optimal price. The continuous approach runs multiple stateless engines which concurrently enrich, aggregate, infer and act on the data. Data scientists, dashboards and client apps all access the same raw or real-time data derivatives with proper identity-based security, data masking and versioning in real-time. Traditionally, data scientists have not been part of IT development teams, like regular Java programmers. This is because their skills set them apart in their own department not normally related to IT, i.e., math, statistics, and data science. So it is logical to conclude that their approach to writing software code does not enjoy the same efficiencies as the traditional programming team. In particular traditional programming has adopted the Continuous Delivery approach to writing code and the agile methodology. That releases software in a continuous circle, called iterations. Continuous


id: cosine-similarity-20260505-0
file: data/en.wikipedia.org/wiki/Cosine_similarity-0.md
category: reference
title: Cosine similarity
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Cosine_similarity
date_saved: 2026-05-05T09:53:42.619881+00:00
instance: kb-cron
parent: cosine-similarity-20260505-0
summary:
In data analysis, cosine similarity is a measure of similarity between two non-zero vectors defined in an inner product space. Cosine similarity is the cosine of the angle between the vectors; that is, it is the dot product of the vectors divided by the product of their lengths. It follows that the cosine similarity does not depend on the magnitudes of the vectors, but only on their angle. The cosine similarity always belongs to the interval [ − 1 , + 1 ] . {\displaystyle [-1,+1].} For example, two proportional vectors have a cosine similarity of +1, two orthogonal vectors have a similarity of 0, and two opposite vectors have a similarity of −1. In some contexts, the component values of the vectors cannot be negative, in which case the cosine similarity is bounded in [ 0 , 1 ] {\displaystyle [0,1]} . For example, in information retrieval and text mining, each word is assigned a different coordinate and a document is represented by the vector of the numbers of occurrences of each word in the document. Cosine similarity then gives a useful measure of how similar two documents are likely to be, in terms of their subject matter, and


id: cosine-similarity-20260505-1
file: data/en.wikipedia.org/wiki/Cosine_similarity-1.md
category: reference
title: Cosine similarity
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Cosine_similarity
date_saved: 2026-05-05T09:53:42.619881+00:00
instance: kb-cron
parent: cosine-similarity-20260505-0
summary:
angular distance = D θ := arccos ⁡ ( cosine similarity ) π = θ π {\displaystyle {\text{angular distance}}=D_{\theta }:={\frac {\arccos({\text{cosine similarity}})}{\pi }}={\frac {\theta }{\pi }}} angular similarity = S θ := 1 − angular distance = 1 − θ π {\displaystyle {\text{angular similarity}}=S_{\theta }:=1-{\text{angular distance}}=1-{\frac {\theta }{\pi }}} Or, if the vector elements are always positive: angular distance = D θ := 2 ⋅ arccos ⁡ ( cosine similarity ) π = 2 θ π {\displaystyle {\text{angular distance}}=D_{\theta }:={\frac {2\cdot \arccos({\text{cosine similarity}})}{\pi }}={\frac {2\theta }{\pi }}} angular similarity = S θ := 1 − angular distance = 1 − 2 θ π {\displaystyle {\text{angular similarity}}=S_{\theta }:=1-{\text{angular distance}}=1-{\frac {2\theta }{\pi }}} Unfortunately, computing the inverse cosine (arccos) function is slow, making the use of the angular distance more computationally expensive than using the more common (but not metric) cosine distance above. === L2-normalized Euclidean distance === Another effective proxy for cosine distance can be obtained by L 2 {\displaystyle L_{2}} normalisation of the vectors, followed by the application of normal Euclidean distance. Using this technique each term in each vector is first divided by the magnitude of the vector, yielding a vector of unit length. Then the Euclidean distance over


id: cosine-similarity-20260505-2
file: data/en.wikipedia.org/wiki/Cosine_similarity-2.md
category: reference
title: Cosine similarity
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Cosine_similarity
date_saved: 2026-05-05T09:53:42.619881+00:00
instance: kb-cron
parent: cosine-similarity-20260505-0
summary:
cos ⁡ ( ∠ A C − ∠ C B ) ≥ cos ⁡ ( ∠ A B ) ≥ cos ⁡ ( ∠ A C + ∠ C B ) . {\displaystyle \cos(\angle {AC}-\angle {CB})\geq \cos(\angle {AB})\geq \cos(\angle {AC}+\angle {CB}).} Using the cosine addition and subtraction formulas, these two inequalities can be written in terms of the original cosines, cos ⁡ ( A , C ) ⋅ cos ⁡ ( C , B ) + ( 1 − cos ⁡ ( A , C ) 2 ) ⋅ ( 1 − cos ⁡ ( C , B ) 2 ) ≥ cos ⁡ ( A , B ) , {\displaystyle \cos(A,C)\cdot \cos(C,B)+{\sqrt {\left(1-\cos(A,C)^{2}\right)\cdot \left(1-\cos(C,B)^{2}\right)}}\geq \cos(A,B),} cos ⁡ ( A , B ) ≥ cos ⁡ ( A , C ) ⋅ cos ⁡ ( C , B ) − ( 1 − cos ⁡ ( A , C ) 2 ) ⋅ ( 1 − cos ⁡ ( C , B ) 2 ) . {\displaystyle \cos(A,B)\geq \cos(A,C)\cdot \cos(C,B)-{\sqrt {\left(1-\cos(A,C)^{2}\right)\cdot \left(1-\cos(C,B)^{2}\right)}}.} This form of the triangle inequality can be used to bound the minimum and maximum similarity of two objects A and B if the similarities to a reference


id: cross-industry-standard-process-for-data-mining-20260505-0
file: data/en.wikipedia.org/wiki/Cross-industry_standard_process_for_data_mining-0.md
category: reference
title: Cross-industry standard process for data mining
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Cross-industry_standard_process_for_data_mining
date_saved: 2026-05-05T09:53:43.884915+00:00
instance: kb-cron
parent: cross-industry-standard-process-for-data-mining-20260505-0
summary:
The Cross-industry standard process for data mining, known as CRISP-DM, is an open standard process model that describes common approaches used by data mining experts. It is the most widely-used analytics model. In 2015, IBM released a new methodology called Analytics Solutions Unified Method for Data Mining/Predictive Analytics (also known as ASUM-DM), which refines and extends CRISP-DM. == History == CRISP-DM was conceived in 1996 and became a European Union project under the ESPRIT funding initiative in 1997. The project was led by five companies: Integral Solutions Ltd (ISL), Teradata, Daimler AG, NCR Corporation, and OHRA, an insurance company. This core consortium brought different experiences to the project. ISL, was later acquired and merged into SPSS. The computer giant NCR Corporation produced the Teradata data warehouse and its own data mining software. Daimler-Benz had a significant data mining team. OHRA was starting to explore the potential use of data mining. The first version of the methodology was presented at the 4th CRISP-DM SIG Workshop in Brussels in March 1999, and published as a step-by-step data mining guide later that year. Between 2006 and 2008, a CRISP-DM 2.0 SIG was formed, and there were discussions about updating the CRISP-DM process model.


id: dark-data-20260505-0
file: data/en.wikipedia.org/wiki/Dark_data-0.md
category: reference
title: Dark data
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Dark_data
date_saved: 2026-05-05T09:53:45.047576+00:00
instance: kb-cron
parent: dark-data-20260505-0
summary:
Dark data is data which is acquired through various computer network operations but not used in any manner to derive insights or for decision making. The ability of an organisation to collect data can exceed the throughput at which it can analyse the data. In some cases the organisation may not even be aware that the data is being collected. IBM estimate that roughly 90 percent of data generated by sensors and analog-to-digital conversions never get used. In an industrial context, dark data can include information gathered by sensors and telematics. Organizations retain dark data for a multitude of reasons, and it is estimated that most companies are only analyzing 1% of their data. Often it is stored for regulatory compliance and record keeping. Some organizations believe that dark data could be useful to them in the future, once they have acquired better analytic and business intelligence technology to process the information. Because storage is inexpensive, storing data is easy. However, storing and securing the data usually entails greater expenses (or even risk) than the potential return profit. In academic discourse, the term dark data was essentially coined by Bryan P. Heidorn. He uses it to describe research data, especially


id: data-access-20260505-0
file: data/en.wikipedia.org/wiki/Data_access-0.md
category: reference
title: Data access
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Data_access
date_saved: 2026-05-05T09:53:46.267466+00:00
instance: kb-cron
parent: data-access-20260505-0
summary:
Data access is a generic term referring to a process which has both an IT-specific meaning and other connotations involving access rights in a broader legal and/or political sense. In the former it typically refers to software and activities related to storing, retrieving, or acting on data housed in a database or other repository. == Details == Two fundamental types of data access exist: sequential access (as in magnetic tape, for example) random access (as in indexed media) Data access crucially involves authorization to access different data repositories. Data access can help distinguish the abilities of administrators and users. For example, administrators may have the ability to remove, edit and add data, while general users may not even have "read" rights if they lack access to particular information. Historically, each repository (including each different database, file system, etc.), might require the use of different methods and languages, and many of these repositories stored their content in different and incompatible formats. Over the years standardized languages, methods, and formats, have developed to serve as interfaces between the often proprietary, and always idiosyncratic, specific languages and methods. Such standards include SQL (1974- ), ODBC (ca 1990- ), JDBC, XQJ, ADO.NET, XML, XQuery,


id: data-analysis-for-fraud-detection-20260505-0
file: data/en.wikipedia.org/wiki/Data_analysis_for_fraud_detection-0.md
category: reference
title: Data analysis for fraud detection
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Data_analysis_for_fraud_detection
date_saved: 2026-05-05T09:53:47.474137+00:00
instance: kb-cron
parent: data-analysis-for-fraud-detection-20260505-0
summary:
Fraud represents a significant problem for governments and businesses and specialized analysis techniques for discovering fraud using them are required. Some of these methods include knowledge discovery in databases (KDD), data mining, machine learning and statistics. They offer applicable and successful solutions in different areas of electronic fraud crimes. In general, the primary reason to use data analytics techniques is to tackle fraud since many internal control systems have serious weaknesses. For example, the currently prevailing approach employed by many law enforcement agencies to detect companies involved in potential cases of fraud consists in receiving circumstantial evidence or complaints from whistleblowers. As a result, a large number of fraud cases remain undetected and unprosecuted. In order to effectively test, detect, validate, correct error and monitor control systems against fraudulent activities, businesses entities and organizations rely on specialized data analytics techniques such as data mining, data matching, the sounds like function, regression analysis, clustering analysis, and gap analysis. Techniques used for fraud detection fall into two primary classes: statistical techniques and artificial intelligence. == Statistical techniques == Examples of statistical data analysis techniques are: Data preprocessing techniques for detection, validation, error correction, and filling up of missing or incorrect data. Calculation


id: data-analysis-for-fraud-detection-20260505-1
file: data/en.wikipedia.org/wiki/Data_analysis_for_fraud_detection-1.md
category: reference
title: Data analysis for fraud detection
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Data_analysis_for_fraud_detection
date_saved: 2026-05-05T09:53:47.474137+00:00
instance: kb-cron
parent: data-analysis-for-fraud-detection-20260505-0
summary:
Early data analysis techniques were oriented toward extracting quantitative and statistical data characteristics. These techniques facilitate useful data interpretations and can help to get better insights into the processes behind the data. Although the traditional data analysis techniques can indirectly lead us to knowledge, it is still created by human analysts. To go beyond, a data analysis system has to be equipped with a substantial amount of background knowledge, and be able to perform reasoning tasks involving that knowledge and the data provided. In effort to meet this goal, researchers have turned to ideas from the machine learning field. This is a natural source of ideas, since the machine learning task can be described as turning background knowledge and examples (input) into knowledge (output). If data mining results in discovering meaningful patterns, data turns into information. Information or patterns that are novel, valid and potentially useful are not merely information, but knowledge. One speaks of discovering knowledge, before hidden in the huge amount of data, but now revealed. The machine learning and artificial intelligence solutions may be classified into two categories: 'supervised' and 'unsupervised' learning. These methods seek for accounts, customers, suppliers, etc. that behave 'unusually' in order to output


id: data-exploration-20260505-0
file: data/en.wikipedia.org/wiki/Data_exploration-0.md
category: reference
title: Data exploration
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Data_exploration
date_saved: 2026-05-05T09:53:48.638463+00:00
instance: kb-cron
parent: data-exploration-20260505-0
summary:
Data exploration is an approach similar to initial data analysis, whereby a data analyst uses visual exploration to understand what is in a dataset and the characteristics of the data, rather than through traditional data management systems. These characteristics can include size or amount of data, completeness of the data, correctness of the data, possible relationships amongst data elements or files/tables in the data. Data exploration is typically conducted using a combination of automated and manual activities. Automated activities can include data profiling or data visualization or tabular reports to give the analyst an initial view into the data and an understanding of key characteristics. This is often followed by manual drill-down or filtering of the data to identify anomalies or patterns identified through the automated actions. Data exploration can also require manual scripting and queries into the data (e.g. using languages such as SQL or R) or using spreadsheets or similar tools to view the raw data. All of these activities are aimed at creating a mental model and understanding of the data in the mind of the analyst, and defining basic metadata (statistics, structure, relationships) for the data set that can be used in further analysis. Once this


id: data-farming-20260505-0
file: data/en.wikipedia.org/wiki/Data_farming-0.md
category: reference
title: Data farming
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Data_farming
date_saved: 2026-05-05T09:53:49.876847+00:00
instance: kb-cron
parent: data-farming-20260505-0
summary:
Data farming is the process of using designed computational experiments to “grow” data, which can then be analyzed using statistical and visualization techniques to obtain insight into complex systems. These methods can be applied to any computational model. Data farming differs from Data mining, as the following metaphors indicate: Miners seek valuable nuggets of ore buried in the earth, but have no control over what is out there or how hard it is to extract the nuggets from their surroundings. ... Similarly, data miners seek to uncover valuable nuggets of information buried within massive amounts of data. Data-mining techniques use statistical and graphical measures to try to identify interesting correlations or clusters in the data set. Farmers cultivate the land to maximize their yield. They manipulate the environment to their advantage using irrigation, pest control, crop rotation, fertilizer, and more. Small-scale designed experiments let them determine whether these treatments are effective. Similarly, data farmers manipulate simulation models to their advantage, using large-scale designed experimentation to grow data from their models in a manner that easily lets them extract useful information. ...the results can reveal root cause-and-effect relationships between the model input factors and the model responses, in addition to rich


id: data-fusion-20260505-0
file: data/en.wikipedia.org/wiki/Data_fusion-0.md
category: reference
title: Data fusion
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Data_fusion
date_saved: 2026-05-05T09:53:51.162435+00:00
instance: kb-cron
parent: data-fusion-20260505-0
summary:
Data fusion is the process of integrating multiple data sources to produce more consistent, accurate, and useful information than that provided by any individual data source. Data fusion processes are often categorized as low, intermediate, or high, depending on the processing stage at which fusion takes place. Low-level data fusion combines several sources of raw data to produce new raw data. The expectation is that fused data is more informative and synthetic than the original inputs. For example, sensor fusion is also known as (multi-sensor) data fusion and is a subset of information fusion. The concept of data fusion has origins in the evolved capacity of humans and animals to incorporate information from multiple senses to improve their ability to survive. For example, a combination of sight, touch, smell, and taste may indicate whether a substance is edible. == The JDL/DFIG model == In the mid-1980s, the Joint Directors of Laboratories formed the Data Fusion Subpanel (which later became known as the Data Fusion Group). With the advent of the World Wide Web, data fusion thus included data, sensor, and information fusion. The JDL/DFIG introduced a model of data fusion that divided the various processes. Currently, the six levels with


id: data-fusion-20260505-1
file: data/en.wikipedia.org/wiki/Data_fusion-1.md
category: reference
title: Data fusion
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Data_fusion
date_saved: 2026-05-05T09:53:51.162435+00:00
instance: kb-cron
parent: data-fusion-20260505-0
summary:
== For enhanced contextual awareness == With a multitude of built-in sensors including motion sensor, environmental sensor, position sensor, a modern mobile device typically gives mobile applications access to a number of sensory data which could be leveraged to enhance the contextual awareness. Using signal processing and data fusion techniques such as feature generation, feasibility study and principal component analysis (PCA) such sensory data will greatly improve the positive rate of classifying the motion and contextual relevant status of the device. Many context-enhanced information techniques are provided by Snidaro, et al. == Statistical methods == === Bayesian auto-regressive Gaussian processes === Gaussian processes are a popular machine learning model. If an auto-regressive relationship between the data is assumed, and each data source is assumed to be a Gaussian process, this constitutes a non-linear Bayesian regression problem. === Semiparametric estimation === Many data fusion methods assume common conditional distributions across several data sources. Recently, methods have been developed to enable efficient estimation within the resulting semiparametric model. == See also == Data assimilation Data munging Image fusion Information integration Integrative level Meta-analysis Sensor fusion == References == === Sources === General references Hall, Dave L.; Llinas, James (1997). "Introduction to Multisensor


id: data-profiling-20260505-0
file: data/en.wikipedia.org/wiki/Data_profiling-0.md
category: reference
title: Data profiling
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Data_profiling
date_saved: 2026-05-05T09:53:52.402806+00:00
instance: kb-cron
parent: data-profiling-20260505-0
summary:
Data profiling is the process of examining the data available from an existing information source (e.g. a database or a file) and collecting statistics or informative summaries about that data. The purpose of these statistics may be to: Find out whether existing data can be easily used for other purposes Improve the ability to search data by tagging it with keywords, descriptions, or assigning it to a category Assess data quality, including whether the data conforms to particular standards or patterns Assess the risk involved in integrating data in new applications, including the challenges of joins Discover metadata of the source database, including value patterns and distributions, key candidates, foreign-key candidates, and functional dependencies Assess whether known metadata accurately describes the actual values in the source database Understanding data challenges early in any data intensive project, so that late project surprises are avoided. Finding data problems late in the project can lead to delays and cost overruns. Have an enterprise view of all data, for uses such as master data management, where key data is needed, or data governance for improving data quality. == Introduction == Data profiling refers to the analysis of information for use in a data warehouse


id: data-science-20260505-0
file: data/en.wikipedia.org/wiki/Data_science-0.md
category: reference
title: Data science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Data_science
date_saved: 2026-05-05T09:53:53.540719+00:00
instance: kb-cron
parent: data-science-20260505-0
summary:
Data science is an interdisciplinary academic field that uses statistics, scientific computing, scientific methods, processing, scientific visualization, algorithms, and systems to extract or extrapolate knowledge from potentially noisy, structured, or unstructured data. Data science plays a critical role in modern decision-making by enabling organizations to extract actionable insights from large and complex datasets. Data science also integrates domain knowledge from the underlying application domain (e.g., natural sciences, information technology, and medicine). Data science is multifaceted and can be described as a science, a research paradigm, a research method, a discipline, a workflow, and a profession. Data science is "a concept to unify statistics, data analysis, informatics, and their related methods" to "understand and analyze actual phenomena" with data. It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, information science, and domain knowledge. However, data science is distinct from computer science and information science. Turing Award winner Jim Gray imagined data science as a "fourth paradigm" of science (empirical, theoretical, computational, and now data-driven) and asserted that "everything about science is changing because of the impact of information technology" and the data deluge. Data science is often described as a multidisciplinary and rapidly


id: data-science-20260505-1
file: data/en.wikipedia.org/wiki/Data_science-1.md
category: reference
title: Data science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Data_science
date_saved: 2026-05-05T09:53:53.540719+00:00
instance: kb-cron
parent: data-science-20260505-0
summary:
data collection and integration; data cleaning and preparation (handling missing values, outliers, encoding, normalisation); feature engineering and selection; visualisation and descriptive statistics; fitting and evaluating statistical or machine-learning models; communicating results and ensuring reproducibility (e.g., reports, notebooks, and dashboards). Lifecycle frameworks such as CRISP-DM describe these steps from business understanding through deployment and monitoring. Data science involves working with larger datasets that often require advanced computational and statistical methods to analyze. Data scientists often work with unstructured data such as text or images and use machine learning algorithms to build predictive models. Data science often uses statistical analysis, data preprocessing, and supervised learning. Recent studies indicate that AI is moving towards data-centric approaches, focusing on the quality of datasets rather than just improving AI models. This trend focuses on improving system performance by cleaning, refining, and labeling data (Bhatt et al., 2024). As AI systems grow larger, the data-centric view has become increasingly important. == Cloud computing for data science == Cloud computing can offer access to large amounts of computational power and storage. In big data, where volumes of information are continually generated and processed, these platforms can be used to handle complex and resource-intensive analytical tasks. Some distributed


id: data-version-control-20260505-0
file: data/en.wikipedia.org/wiki/Data_version_control-0.md
category: reference
title: Data version control
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Data_version_control
date_saved: 2026-05-05T09:53:54.761385+00:00
instance: kb-cron
parent: data-version-control-20260505-0
summary:
Data version control is a method of working with data sets. It is similar to the version control systems used in traditional software development, but is optimized to allow better processing of data and collaboration in the context of data analytics, research, and any other form of data analysis. Data version control may also include specific features and configurations designed to facilitate work with large data sets and data lakes. == History == === Background === As early as 1985, researchers recognized the need for defining timing attributes in database tables, which would be necessary for tracking changes to databases. This research continued into the 1990s, and the theory was formalized into practical methods for managing data in relational databases, providing some of the foundational concepts for what would later become data version control. In the early 2010s the size of data sets was rapidly expanding, and relational databases were no longer sufficient to manage the amounts of data organizations were accumulating. The rise of the Apache Hadoop eco system, with HDFS as a storage layer, and later object storage had become dominant in big data operations. Research into data management tools and data version control systems increased sharply, along


id: data-driven-model-20260505-0
file: data/en.wikipedia.org/wiki/Data-driven_model-0.md
category: reference
title: Data-driven model
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Data-driven_model
date_saved: 2026-05-05T09:53:55.946685+00:00
instance: kb-cron
parent: data-driven-model-20260505-0
summary:
Data-driven models are a class of computational models that primarily rely on historical data collected throughout a system's or process' lifetime to establish relationships between input, internal, and output variables. Commonly found in numerous articles and publications, data-driven models have evolved from earlier statistical models, overcoming limitations posed by strict assumptions about probability distributions. These models have gained prominence across various fields, particularly in the era of big data, artificial intelligence, and machine learning, where they offer valuable insights and predictions based on the available data. == Background == These models have evolved from earlier statistical models, which were based on certain assumptions about probability distributions that often proved to be overly restrictive. The emergence of data-driven models in the 1950s and 1960s coincided with the development of digital computers, advancements in artificial intelligence research, and the introduction of new approaches in non-behavioural modelling, such as pattern recognition and automatic classification. == Key Concepts == Data-driven models encompass a wide range of techniques and methodologies that aim to intelligently process and analyse large datasets. Examples include fuzzy logic, fuzzy and rough sets for handling uncertainty, neural networks for approximating functions, global optimization and evolutionary computing, statistical learning theory, and Bayesian


id: data-informed-decision-making-20260505-0
file: data/en.wikipedia.org/wiki/Data-informed_decision-making-0.md
category: reference
title: Data-informed decision-making
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Data-informed_decision-making
date_saved: 2026-05-05T09:53:57.178692+00:00
instance: kb-cron
parent: data-informed-decision-making-20260505-0
summary:
Data-informed decision-making (DIDM) refers to the collection and analysis of data to guide decisions and improve chances of success. Another form of this process is referred to as data-driven decision-making, "which is defined similarly as making decisions based on hard data as opposed to intuition, observation, or guesswork." DIDM is used in education communities, where data is used with the goal of helping students and improving curricula, among other fields in which data is used to inform decisions. While "data based decision-making" is a more common term, "data-informed decision-making" is the preferred term, since decisions should not be based solely on quantitative data. Data-driven decision-making is commonly used in the context of business growth and entrepreneurship. Many educators have access to some type of a data system for analyzing their students' data. These data systems present data to educators in an over-the-counter data format (embedding labels, supplemental documentation, and a help system, making key package/display and content decisions) to improve the success of educators' data-informed decision-making. In business, fostering and actively supporting data-driven decision-making in their firm and among their colleagues may be one of the central responsibilities of CIOs (Chief Information Officers) or CDOs (Chief Data Officers). Assessment in


id: dataset-shift-20260505-0
file: data/en.wikipedia.org/wiki/Dataset_shift-0.md
category: reference
title: Dataset shift
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Dataset_shift
date_saved: 2026-05-05T09:53:59.453907+00:00
instance: kb-cron
parent: dataset-shift-20260505-0
summary:
Dataset shift is a phenomenon in machine learning and statistics in which the joint distribution of input variables and target labels is different in the training phase and the deployment or test phase (i.e., P t r a i n ( X , Y ) ≠ P t e s t ( X , Y ) {\displaystyle P_{train}(X,Y)\neq P_{test}(X,Y)} ). This happens when the statistical properties of data used to train a model are no longer representative of the data encountered in real-world use, often resulting in degraded predictive performance and diminished generalization ability. Dataset shift is a generic term for a number of particular types of distributional change. Covariate shift is when the distribution of the input features changes, but the conditional relationship between inputs and outputs remains constant . Prior probability shift (or label shift) happens when the distribution of target labels changes, but the conditional distribution of inputs given labels stays the same. Concept shift (also known as concept drift) is the change of the conditional relationship between inputs and outputs that renders previously learned patterns invalid over time. A key challenge for deploying machine learning systems is dataset shift, in particular in dynamic environments where the


id: degree-rips-bifiltration-20260505-0
file: data/en.wikipedia.org/wiki/Degree-Rips_bifiltration-0.md
category: reference
title: Degree-Rips bifiltration
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Degree-Rips_bifiltration
date_saved: 2026-05-05T09:54:00.644610+00:00
instance: kb-cron
parent: degree-rips-bifiltration-20260505-0
summary:
The degree-Rips bifiltration is a simplicial filtration used in topological data analysis for analyzing the shape of point cloud data. It is a multiparameter extension of the Vietoris–Rips filtration that possesses greater stability to data outliers than single-parameter filtrations, and which is more amenable to practical computation than other multiparameter constructions. Introduced in 2015 by Lesnick and Wright, the degree-Rips bifiltration is a parameter-free and density-sensitive vehicle for performing persistent homology computations on point cloud data. == Definition == It is standard practice in topological data analysis (TDA) to associate a sequence of nested simplicial complexes to a finite data set in order to detect the persistence of topological features over a range of scale parameters. One way to do this is by considering the sequence of Vietoris–Rips complexes of a finite set in a metric space indexed over all scale parameters. If X {\displaystyle X} is a finite set in a metric space, then this construction is known as the Vietoris–Rips (or simply "Rips") filtration on X {\displaystyle X} , commonly denoted Rips ( X ) {\displaystyle {\text{Rips}}(X)} or R ( X ) {\displaystyle {\mathcal {R}}(X)} . The Rips filtration can be expressed as a functor Rips ( X


id: directional-component-analysis-20260505-0
file: data/en.wikipedia.org/wiki/Directional_component_analysis-0.md
category: reference
title: Directional component analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Directional_component_analysis
date_saved: 2026-05-05T09:54:01.826255+00:00
instance: kb-cron
parent: directional-component-analysis-20260505-0
summary:
Directional component analysis (DCA) is a statistical method used in climate science for identifying representative patterns of variability in space-time data-sets such as historical climate observations, weather prediction ensembles or climate ensembles. The first DCA pattern is a pattern of weather or climate variability that is both likely to occur (measured using likelihood) and has a large impact (for a specified linear impact function, and given certain mathematical conditions: see below). The first DCA pattern contrasts with the first PCA pattern, which is likely to occur, but may not have a large impact, and with a pattern derived from the gradient of the impact function, which has a large impact, but may not be likely to occur. DCA differs from other pattern identification methods used in climate research, such as EOFs, rotated EOFs and extended EOFs in that it takes into account an external vector, the gradient of the impact. DCA provides a way to reduce large ensembles from weather forecasts or climate models to just two patterns. The first pattern is the ensemble mean, and the second pattern is the DCA pattern, which represents variability around the ensemble mean in a way that takes impact into account. DCA contrasts


id: directional-component-analysis-20260505-1
file: data/en.wikipedia.org/wiki/Directional_component_analysis-1.md
category: reference
title: Directional component analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Directional_component_analysis
date_saved: 2026-05-05T09:54:01.826255+00:00
instance: kb-cron
parent: directional-component-analysis-20260505-0
summary:
The two axes represent anomalies of annual mean rainfall at two locations, with the highest total rainfall anomaly values towards the top right corner of the diagram The joint variability of the rainfall anomalies at the two locations is assumed to follow a bivariate normal distribution The ellipse shows a single contour of probability density from this bivariate normal, with higher values inside the ellipse The red dot at the centre of the ellipse shows zero rainfall anomalies at both locations The blue parallel-line arrow shows the principal axis of the ellipse, which is also the first PCA spatial pattern vector In this case, the PCA pattern is scaled so that it touches the ellipse The diagonal straight line shows a line of constant positive total rainfall anomaly, assumed to be at some fairly extreme level The red dotted-line arrow shows the first DCA pattern, which points towards the point at which the diagonal line is tangent to the ellipse In this case, the DCA pattern is scaled so that it touches the ellipse From this diagram, the DCA pattern can be seen to possess the following properties: Of all the points on the diagonal line, it is the one


id: directional-change-intrinsic-time-20260505-0
file: data/en.wikipedia.org/wiki/Directional-change_intrinsic_time-0.md
category: reference
title: Directional-change intrinsic time
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Directional-change_intrinsic_time
date_saved: 2026-05-05T09:54:03.000900+00:00
instance: kb-cron
parent: directional-change-intrinsic-time-20260505-0
summary:
Directional-change intrinsic time is an event-based operator to dissect a data series into a sequence of alternating trends of defined size δ {\displaystyle \delta } . The directional-change intrinsic time operator was developed for the analysis of financial market data series. It is an alternative methodology to the concept of continuous time. Directional-change intrinsic time operator dissects a data series into a set of drawups and drawdowns or up and down trends that alternate with each other. An established trend comes to an end as soon as a trend reversal is observed. A price move that extends a trend is called overshoot and leads to new price extremes. Figure 1 provides an example of a price curve dissected by the directional change intrinsic time operator. The frequency of directional-change intrinsic events maps (1) the volatility of price changes conditional to (2) the selected threshold δ {\displaystyle \delta } . The stochastic nature of the underlying process is mirrored in the non-equal number of intrinsic events observed over equal periods of physical time. Directional-change intrinsic time operator is a noise filtering technique. It identifies regime shifts, when trend changes of a particular size occur and hides price fluctuations that are smaller


id: doacross-parallelism-20260505-0
file: data/en.wikipedia.org/wiki/DOACROSS_parallelism-0.md
category: reference
title: DOACROSS parallelism
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/DOACROSS_parallelism
date_saved: 2026-05-05T09:54:04.174776+00:00
instance: kb-cron
parent: doacross-parallelism-20260505-0
summary:
DOACROSS parallelism is a parallelization technique used to perform Loop-level parallelism by utilizing synchronisation primitives between statements in a loop. This technique is used when a loop cannot be fully parallelized by DOALL parallelism due to data dependencies between loop iterations, typically loop-carried dependencies. The sections of the loop which contain loop-carried dependence are synchronized, while treating each section as a parallel task on its own. Therefore, DOACROSS parallelism can be used to complement DOALL parallelism to reduce loop execution times. == Description == DOACROSS parallelism is particularly useful when one statement depends on the values generated by another statement. In such a loop, DOALL parallelism can not be implemented in a straightforward manner. If the first statement blocks the execution of the second statement until the required value has been produced, then the two statements would be able to execute independent of each other (i.e.), each of the aforementioned statements would be parallelized for simultaneous execution using DOALL parallelism. The following pseudocode illustrates the operation of DOACROSS parallelism in such a situation. === Example === In this example, each iteration of the loop requires the value written into a by the previous iteration. However, the entire statement is not


id: document-oriented-database-20260505-0
file: data/en.wikipedia.org/wiki/Document-oriented_database-0.md
category: reference
title: Document-oriented database
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Document-oriented_database
date_saved: 2026-05-05T09:54:05.454091+00:00
instance: kb-cron
parent: document-oriented-database-20260505-0
summary:
A document-oriented database, or document store, is a computer program and data storage system designed for storing, retrieving, and managing document-oriented information, also known as semi-structured data. Document-oriented databases are one of the main categories of NoSQL databases, and the popularity of the term "document-oriented database" has grown alongside the adoption of NoSQL itself. XML databases are a subclass of document-oriented databases optimized for XML documents. Graph databases are similar, but add another layer, the relationship, which allows them to link documents for rapid traversal. Document-oriented databases are conceptually an extension of the key–value store, another type of NoSQL database. In key-value stores, data is treated as opaque by the database, whereas document-oriented systems exploit the internal structure of documents to extract metadata and optimize storage and queries. Although in practice the distinction can be minimal due to modern tooling, document stores are designed to provide a richer programming experience with modern programming techniques. Document databases differ significantly from traditional relational databases (RDBs). Relational databases store data in predefined tables, often requiring an object to be split across multiple tables. In contrast, document databases store all information for a given object in a single document, with each document potentially having


id: document-oriented-database-20260505-1
file: data/en.wikipedia.org/wiki/Document-oriented_database-1.md
category: reference
title: Document-oriented database
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Document-oriented_database
date_saved: 2026-05-05T09:54:05.454091+00:00
instance: kb-cron
parent: document-oriented-database-20260505-0
summary:
=== Relationship to relational databases === In a relational database, data is organized into predefined types represented as tables. Each table contains rows (records) with a fixed set of columns (fields), so all records in a table share the same structure. Administrators typically define indexes on selected fields to improve query performance. A central principle of relational database design is database normalization, in which data that might otherwise be repeated is stored in separate tables and linked using keys. When records in different tables are related, a foreign key is used to associate them. For example, an address book application may store a contact's name, image, phone numbers, mailing addresses, and email addresses. In a normalized relational design, separate tables might be created for contacts, phone numbers, and email addresses. The phone number table would include a foreign key referencing the associated contact. To reconstruct a complete contact record, the database retrieves related information from each table using the foreign keys and combines it into a single record. In contrast, a document-oriented database stores all data related to an object within a single document, and stored in the database as a single entry. In the address book example,the contact's name,


id: doe-mean-plot-20260505-0
file: data/en.wikipedia.org/wiki/DOE_mean_plot-0.md
category: reference
title: DOE mean plot
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/DOE_mean_plot
date_saved: 2026-05-05T09:54:06.621514+00:00
instance: kb-cron
parent: doe-mean-plot-20260505-0
summary:
In statistics, specifically the analysis of data in the design of experiments, a DOE mean plot is a graphical tool used to analyze data from an experiment. In it, it demonstrates the relative importance of all the factors in an experiment with respect to a chosen location statistic, in this case the mean. Each factor is plotted and all mean levels of the factor (two or more) are connected with straight lines. The plot is meant to complement other analyses, such as the typical analysis of variance. == Example == Shown below is an example from Box, Hunter, and Hunter (1978). In it, several factors are chosen to determine their effect(s) on bike speed. These factors are: seat height, dynamo engaged (yes/no), angle of the handlebar, chosen gear, raincoat (on/off), breakfast (yes/no), and tire pressure (low/high), each at two levels. The plot shows the mean response level for each variable (vertical) versus the factor (horizontal). To interpret the graph, all that is needed is to determine the ordering of the factors by length of the line connecting the two dots. The longer the line, the more significant the factor. Therefore, this plot clearly demonstrates that factor 4 (chosen gear) is


id: double-mass-analysis-20260505-0
file: data/en.wikipedia.org/wiki/Double_mass_analysis-0.md
category: reference
title: Double mass analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Double_mass_analysis
date_saved: 2026-05-05T09:54:07.787564+00:00
instance: kb-cron
parent: double-mass-analysis-20260505-0
summary:
Double mass analysis is a simple graphical method to evaluate the consistency of hydrological data. The DM approach plots the cumulative data of one variable against the cumulative data of a second variable. A break in the slope of a linear function fit to the data is thought to represent a change in the relation between the variables. This approach provides a robust method to determine a change in the behavior of precipitation and recharge in a simple graphical method. It is a commonly used data analysis approach for investigating the behaviour of records made of hydrological or meteorological data at a number of locations. It is used to determine whether there is a need for corrections to the data - to account for changes in data collection procedures or other local conditions. Such changes may result from a variety of things including changes in instrumentation, changes in observation procedures, or changes in gauge location or surrounding conditions. Double mass analysis for checking consistency of a hydrological or meteorological record is considered to be an essential tool before taking it for analysis purpose. This method is based on the hypothesis that each item of the recorded data of a population


id: error-level-analysis-20260505-0
file: data/en.wikipedia.org/wiki/Error_level_analysis-0.md
category: reference
title: Error level analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Error_level_analysis
date_saved: 2026-05-05T09:54:08.944694+00:00
instance: kb-cron
parent: error-level-analysis-20260505-0
summary:
Error level analysis (ELA) is the analysis of compression artifacts in digital data with lossy compression such as JPEG. == Principles == When used, lossy compression is normally applied uniformly to a set of data, such as an image, resulting in a uniform level of compression artifacts. Alternatively, the data may consist of parts with different levels of compression artifacts. This difference may arise from the different parts having been repeatedly subjected to the same lossy compression a different number of times, or the different parts having been subjected to different kinds of lossy compression. A difference in the level of compression artifacts in different parts of the data may therefore indicate that the data has been edited. In the case of JPEG, even a composite with parts subjected to matching compressions will have a difference in the compression artifacts. In order to make the typically faint compression artifacts more readily visible, the data to be analyzed is subjected to an additional round of lossy compression, this time at a known, uniform level, and the result is subtracted from the original data under investigation. The resulting difference image is then inspected manually for any variation in the level of compression


id: facet-theory-20260505-0
file: data/en.wikipedia.org/wiki/Facet_theory-0.md
category: reference
title: Facet theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Facet_theory
date_saved: 2026-05-05T09:54:10.375224+00:00
instance: kb-cron
parent: facet-theory-20260505-0
summary:
Facet theory is a metatheory for the multivariate behavioral sciences that posits that scientific theories and measurements can be advanced by discovering relationships between conceptual classifications of research variables and empirical partitions of data-representation spaces. For this purpose, facet theory proposes procedures for (1) Constructing or selecting variables for observation, using the mapping sentence technique (a formal definitional framework for a system of observations), and (2) Analyzing multivariate data, using data representation spaces, notably those depicting similarity measures (e.g., correlations), or partially ordered sets, derived from the data. Facet theory is characterized by its direct concern with the entire content-universe under study, containing many, possibly infinitely many, variables. Observed variables are regarded just as a sample of statistical units from the multitude of variables that make up the investigated attribute (the content-universe). Hence, Facet theory proposes techniques for sampling variables for observation from the entire content universe; and for making inferences from the sample of observed variables to the entire content universe. The sampling of variables is done with the aid of the mapping sentence technique (see Section 1); and inferences from the sample of observed variables to the entire content universe are made with respect to correspondences between conceptual


id: facet-theory-20260505-1
file: data/en.wikipedia.org/wiki/Facet_theory-1.md
category: reference
title: Facet theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Facet_theory
date_saved: 2026-05-05T09:54:10.375224+00:00
instance: kb-cron
parent: facet-theory-20260505-0
summary:
An initial framework for observing intelligence could be Mapping Sentence 1. The mapping sentence serves as a unified semantic device for specifying the system of intelligence test items, according to the present conceptualization. Its content facet, the material facet, may now serve as a classification of intelligence test items to be considered. Thus, in designing observations, a stratified sampling of items is afforded by ensuring an appropriate selection of items from each of the material facet elements; that is, from each class of items: the verbal, the numerical and the spatial. === Enriching the mapping sentence === The research design can be enriched by introducing to the mapping sentence an additional, independent classification of the observations in the form of an additional content-facet, thereby facilitating systematic differentiations of the observations. For example, intelligence items may be classified also according to the cognitive operation required in order to respond correctly to an item: whether rule-recall (memory), rule-application, or rule-inference. Instead of the three sub-content-universes of intelligence defined by the material facet alone, we now have nine sub-content-universes defined by the cartesian multiplication of the material and the mental-operation facets. See mapping sentence 2. Another way of enriching a mapping sentence (and


id: facet-theory-20260505-2
file: data/en.wikipedia.org/wiki/Facet_theory-2.md
category: reference
title: Facet theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Facet_theory
date_saved: 2026-05-05T09:54:10.375224+00:00
instance: kb-cron
parent: facet-theory-20260505-0
summary:
Receiving as input (or computing from input data) a matrix of similarity coefficients, specifying, for each pair of items how similar they are. A common example is the computation of a correlation-coefficient matrix from input data, where the size of a correlation coefficient between two variables reflects the degree of similarity between them. Mapping the items (variables) as points in a geometric space of a given dimensionality while preserving as well as possible the condition: If rij>rkl then dij<dkl for all i,j,k,l where rij is the similarity measure (e.g., correlation coefficient) between variables i,j and dij is the distance between their points in the space. Most often, Euclidean distance function (Minkowsky distance of order 2) is used. But other distance functions, especially the Manhattan distance function (Minkowsky distance of order 1) are called for. (See Subsection Relating POSAC Measurement Space to the SSA Concept Space below.) The goodness-of-fit of the resulting mapping may be assessed by a loss function– Kruskal's Stress coefficient or Guttman's Coefficient of Alienation. Partitioning the space as well as possible, into simple regions (stripe, sectors or concentric rings) whose variables are in 1-1 correspondence with a pre-conceived content-facet. To run this option, content facet(s) must be


id: facet-theory-20260505-3
file: data/en.wikipedia.org/wiki/Facet_theory-3.md
category: reference
title: Facet theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Facet_theory
date_saved: 2026-05-05T09:54:10.375224+00:00
instance: kb-cron
parent: facet-theory-20260505-0
summary:
=== Principles of faceted SSA: A summary === 1. The attribute under study is represented by a geometric space. 2. Variables of the attribute are represented as points in that space. Conversely, every point in the geometric space is a variable of the attribute. This is the Continuity Principle. 3. The observed variables, located as points in the empirical Faceted SSA map, constitute but a sample drawn from the many (possibly infinitely many) variables constituting the content universe of the attribute investigated. 4. The observed variables chosen for SSA must all belong to the same content universe. This is ensured by including in the SSA only variables whose ranges are similarly ordered with respect to a common meaning (CMR). 5. The sample of variables marked on the Faceted SSA map is used as a guide for inferring possible partitions of the SSA-attribute-map into distinct regions, each region representing a component, or subdomain, of the attribute. 6. In Facet Theory, relationships between attribute components (such as verbal intelligence and numeric intelligence as components of intelligence), are expressed in geometric terms –such as shapes and spatial orientation – rather than in algebraic terms. Just as one would describe relationships between neighboring countries


id: facet-theory-20260505-4
file: data/en.wikipedia.org/wiki/Facet_theory-4.md
category: reference
title: Facet theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Facet_theory
date_saved: 2026-05-05T09:54:10.375224+00:00
instance: kb-cron
parent: facet-theory-20260505-0
summary:
Variables processed by Facet Theory measurement operations to be described below, evenly cover the attribute content universe. To ensure such coverage, Facet Theory measurement operations are often performed not on the sample of the observed items themselves, but rather on composite variables that represent facet elements that had been validated by Faceted SSA. The sample of individuals is rich enough to allow existing score-profiles of the processed variables to be observed. In the resulting measurement, order relations among individuals should preserve sufficiently well order relations (including comparability and incomparability; see below) between individuals' profiles of the processed variables. The result of the measurement operation yields the smallest number of scales; The resultant scales represent fundamental variables whose interpretation derives from the contents of the observed items, but does not depend on the particular sample of items observed. Partial order analysis of observed data. Let observed items v1,...,vn with a common-meaning range (CMR) represent an investigated content universe; let A1,...,An be their ranges with each Aj ordered from high to low with respect to the common meaning; and let A = A1×A2 × ... × An be the cartesian product of all the range facets, Aj (j = 1,...,n). A system


id: facet-theory-20260505-5
file: data/en.wikipedia.org/wiki/Facet_theory-5.md
category: reference
title: Facet theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Facet_theory
date_saved: 2026-05-05T09:54:10.375224+00:00
instance: kb-cron
parent: facet-theory-20260505-0
summary:
Given this POSAC solution, an attempt is made to interpret the two coordinates, X1 and X2, as two fundamental scales of the investigated phenomenon of evening TV watching by the investigated population. This is done by, first, interpreting the intervals (equivalence classes) within each coordinate, and then trying to conceptualize the derived meanings of the ordered intervals, in terms of a meaningful notion that may be attributed to the coordinate. In the present simplified example, this is easy: Inspecting the map, we attempt to identify the feature that distinguishes all profiles with given score in X1. Thus, we find that profiles with X1=4, and only they, represent TV watching in the fourth hour. Profiles with X1 = 3 all have 1 in the third watching hour but 0 at the fourth hour, i.e., the third hour is the latest watching hour. X1 = 2 is assigned to, and only to, profiles whose latest watching hour is the second hour. And, finally, X1 = 1 is for the profile 1000 which represents the fact that the first hour is the only – and therefore the latest – watching hour (ignoring the profile 0000 of those who didn't watch TV at the


id: facet-theory-20260505-6
file: data/en.wikipedia.org/wiki/Facet_theory-6.md
category: reference
title: Facet theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Facet_theory
date_saved: 2026-05-05T09:54:10.375224+00:00
instance: kb-cron
parent: facet-theory-20260505-0
summary:
Interval 1. Low Utility & Low Equality DJ Attitude Interval 2. Low Utility & High Equality DJ Attitude Interval 3. High Utility s & Low Corrective Action DJ Attitude Interval 4. High Utility & High Corrective Action DJ Attitude That is, Enhanced Utility Attitude, even if low, (interval 1 and 2) is somewhat present when Equality is favored (interval 2). If Enhanced Utility Attitude is high (intervals 3 and 4), it reaches the extreme level (interval 4) when Corrective Action is favored. (This may well reflect the sentiment that, in the long run, the advancement of disadvantaged pupils serves the common good.) The meanings of the fundamental variables, X and Y, while relying on the concepts of fairness and of utility, respectively, suggest new notions that modify them. The new notions were christened Enhanced (or Extended) Fairness and Enhanced (or Extended) Utility. === Complementary topics in partial order spaces === Higher order partition lines. The above simple measurement space illustrates partition-lines that are straight or have one bend. More complex measurement spaces result with items whose partition-lines have two or more bends. While partial order spaces are used mainly for analyzing score profiles (based on range facets), under certain conditions,


id: facet-theory-20260505-7
file: data/en.wikipedia.org/wiki/Facet_theory-7.md
category: reference
title: Facet theory
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Facet_theory
date_saved: 2026-05-05T09:54:10.375224+00:00
instance: kb-cron
parent: facet-theory-20260505-0
summary:
== Facet theory: comparisons and comments == Concerned with the entire cycle of multivariate research – concept definition, observational design, and data analysis for concept-structure and measurement, Facet Theory constitutes a novel paradigm for the behavioral sciences. Hence, only limited aspects of it can be compared with specific statistical methods. A distinctive feature of Facet Theory is its explicit concern with the entire set of variables included in the investigated content-universe, regarding the subset of observed variables as but a sample from which inferences can be made. Hence, clusters of variables, if observed, are of no significance. They are simply unimportant artifacts of the procedure for sampling of the variables. This is in contrast with cluster analysis or factor analysis where recorded clustering patterns determine research results and interpretations. There have been various attempts to describe technical differences between Factor Analysis and Facet Theory. Briefly, it may be said that while Factor Analysis aims to structure the set of variables selected for observation, Facet Theory aims to structure the entire content universe of all variables, observed as well as unobserved, relying on the continuity principle and using regional hypotheses as an inferential procedure. Guttman's SSA, as well as Multidimensional Scaling


id: feature-engineering-20260505-0
file: data/en.wikipedia.org/wiki/Feature_engineering-0.md
category: reference
title: Feature engineering
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Feature_engineering
date_saved: 2026-05-05T09:54:11.527727+00:00
instance: kb-cron
parent: feature-engineering-20260505-0
summary:
Feature engineering is a preprocessing step in supervised machine learning and statistical modeling which transforms raw data into a more effective set of inputs. Each input comprises several attributes, known as features. By providing models with relevant information, feature engineering significantly enhances their predictive accuracy and decision-making capability. Beyond machine learning, the principles of feature engineering are applied in various scientific fields, including physics. For example, physicists construct dimensionless numbers such as the Reynolds number in fluid dynamics, the Nusselt number in heat transfer, and the Archimedes number in sedimentation. They also develop first approximations of solutions, such as analytical solutions for the strength of materials in mechanics. == Clustering == One of the applications of feature engineering has been clustering of feature-objects or sample-objects in a dataset. Especially, feature engineering based on matrix decomposition has been extensively used for data clustering under non-negativity constraints on the feature coefficients. These include Non-Negative Matrix Factorization (NMF), Non-Negative Matrix-Tri Factorization (NMTF), Non-Negative Tensor Decomposition/Factorization (NTF/NTD), etc. The non-negativity constraints on coefficients of the feature vectors mined by the above-stated algorithms yields a part-based representation, and different factor matrices exhibit natural clustering properties. Several extensions of the above-stated feature engineering methods have been


id: geographic-analytics-20260505-0
file: data/en.wikipedia.org/wiki/Geographic_analytics-0.md
category: reference
title: Geographic analytics
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Geographic_analytics
date_saved: 2026-05-05T09:54:12.686727+00:00
instance: kb-cron
parent: geographic-analytics-20260505-0
summary:
Geographic analytics is an analytical approach to strategic management and data analytics to make geographic decisions efficiently. Examples of such decisions are choosing the location for a warehouse or planning the regions for a marketing campaign. Data, information and framing conditions are visualized on maps to derive recommendations for action. In comparison to geographic information systems (GIS), which primarily aim at the representation of information on maps (descriptive analytics), Geographic analytics additionally focuses on making business decisions based on the data visualization on the map (prescriptive analytics). == Background == Purely mathematically based approaches that are used in data analytics in order to support management decisions often have the disadvantage that they require a large amount of data. This often results in considerable efforts for gathering, cleaning and understanding the data. Furthermore, there can be “intangible” framing conditions that are disruptive to any purely data-driven optimization solution. Example logistics: For the task to find the optimal location for a warehouse in a logistics network, so-called Center of Gravity models are being used. To minimize the cost, these models use transport volume data, customer locations, cost data, etc., in order to determine the optimal location. However, there are often framing conditions


id: health-care-analytics-20260505-0
file: data/en.wikipedia.org/wiki/Health_care_analytics-0.md
category: reference
title: Health care analytics
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Health_care_analytics
date_saved: 2026-05-05T09:54:13.909352+00:00
instance: kb-cron
parent: health-care-analytics-20260505-0
summary:
Health care analytics is the health care analysis activities that can be undertaken as a result of data collected from four areas within healthcare: (1) claims and cost data, (2) pharmaceutical and research and development (R&D) data, (3) clinical data (such as collected from electronic medical records (EHRs)), and (4) patient behaviors and preferences data (e.g. patient satisfaction or retail purchases, such as data captured in stores selling personal health products). Health care analytics is a growing industry in many countries including the United States, where it is expected to grow to more than $31 billion by 2022. It is also increasingly important to governments and public health agencies to support health policy and meet public expectations for transparency, as accelerated by the COVID-19 pandemic. Health care analytics allows for the examination of patterns in various healthcare data in order to determine how clinical care can be improved for patients and provider teams, while limiting excessive spending and improving the health of populations. Areas of the industry focuses on clinical analysis, financial analysis, supply chain analysis, as well as marketing, fraud and HR analysis. There is increasing demand in many countries to incorporate social indicators of patients and providers within


id: health-care-analytics-20260505-1
file: data/en.wikipedia.org/wiki/Health_care_analytics-1.md
category: reference
title: Health care analytics
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Health_care_analytics
date_saved: 2026-05-05T09:54:13.909352+00:00
instance: kb-cron
parent: health-care-analytics-20260505-0
summary:
=== Federal government role in health IT === In the United States, multiple federal entities are heavily involved in health analytics infrastructure. Within the executive branch, the administration itself, Centers for Medicare and Medicaid Services (CMS), and Office of the National Coordinator for Health Information Technology (ONC) each have strategic plans and are involved in determining regulation. Within the legislative branch, multiple committees within the House of Representatives and Senate hold hearings and have opinions on using data and technology to reduce costs and improve outcomes in healthcare. The ONC issued the Federal Health IT Strategic Plan 2015-2020. The plan outlines the steps federal agencies will take to achieve widespread use of health information technology (health IT) and electronic health information to enhance the health IT infrastructure, to advance person-centered and self-managed health, to transform health care delivery and community health, and to foster research, scientific knowledge and innovation. The plan is intended “to provide clarity in federal policies, programs, and actions and includes strategies to align program requirements, harmonize and simplify regulations, and aims to help health IT users to advance the learning health system to achieve better health.” The Strategic Plan includes several key initiatives employing multiple strategies


id: iconography-of-correlations-20260505-0
file: data/en.wikipedia.org/wiki/Iconography_of_correlations-0.md
category: reference
title: Iconography of correlations
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Iconography_of_correlations
date_saved: 2026-05-05T09:54:15.084817+00:00
instance: kb-cron
parent: iconography-of-correlations-20260505-0
summary:
In exploratory data analysis, the iconography of correlations, or representation of correlations, is a data visualization technique which replaces a numeric correlation matrix by its graphical projection onto a diagram. Correlations between variables are represented by solid lines (positive correlations) or dotted lines (negative correlations) plotted on the diagram, where shorter lengths or thicker lines (or both) represent greater projection components and thus greater degree of correlation. == History == The idea of using graphical models to represent correlations has a notable application in genomic mapping, though the iconography of correlations is more general for not assuming the probability distribution, since it only relies on representing the correlation coefficients geometrically. The iconography of correlations first dates to 1975, applied to marine geochemistry in a 1981 thesis, and later in a 1982 data analysis article. Afterwards, the method was applied widely in the aerospace industry but for about fifteen years manufacturers kept it fairly confidential; generally, they preferred not to broadcast useful techniques to their competitors. In 1997 the first company was incorporated to distribute iconography of correlations software. Since then the topic of iconography of correlations has been incorporated into university courses, and typical topical articles' citation lists have rapidly


id: interleaving-distance-20260505-0
file: data/en.wikipedia.org/wiki/Interleaving_distance-0.md
category: reference
title: Interleaving distance
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Interleaving_distance
date_saved: 2026-05-05T09:54:16.289352+00:00
instance: kb-cron
parent: interleaving-distance-20260505-0
summary:
In topological data analysis, the interleaving distance is a measure of similarity between persistence modules, a common object of study in topological data analysis and persistent homology. The interleaving distance was first introduced by Frédéric Chazal et al. in 2009. since then, it and its generalizations have been a central consideration in the study of applied algebraic topology and topological data analysis. == Definition == A persistence module V {\displaystyle \mathbb {V} } is a collection ( V t ∣ t ∈ R ) {\displaystyle (V_{t}\mid t\in \mathbb {R} )} of vector spaces indexed over the real line, along with a collection ( v t s : V s → V t ∣ s ≤ t ) {\displaystyle (v_{t}^{s}:V_{s}\to V_{t}\mid s\leq t)} of linear maps such that v t t {\displaystyle v_{t}^{t}} is always an isomorphism, and the relation v t s ∘ v s r = v t r {\displaystyle v_{t}^{s}\circ v_{s}^{r}=v_{t}^{r}} is satisfied for every r ≤ s ≤ t {\displaystyle r\leq s\leq t} . The case of R {\displaystyle \mathbb {R} } indexing is presented here for simplicity, though the interleaving distance can be readily adapted to more general settings, including multi-dimensional persistence modules. Let U {\displaystyle


id: item-tree-analysis-20260505-0
file: data/en.wikipedia.org/wiki/Item_tree_analysis-0.md
category: reference
title: Item tree analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Item_tree_analysis
date_saved: 2026-05-05T09:54:17.467755+00:00
instance: kb-cron
parent: item-tree-analysis-20260505-0
summary:
Item tree analysis (ITA) is a data analytical method which allows constructing a hierarchical structure on the items of a questionnaire or test from observed response patterns. Assume that we have a questionnaire with m items and that subjects can answer positive (1) or negative (0) to each of these items, i.e. the items are dichotomous. If n subjects answer the items this results in a binary data matrix D with m columns and n rows. Typical examples of this data format are test items which can be solved (1) or failed (0) by subjects. Other typical examples are questionnaires where the items are statements to which subjects can agree (1) or disagree (0). Depending on the content of the items it is possible that the response of a subject to an item j determines her or his responses to other items. It is, for example, possible that each subject who agrees to item j will also agree to item i. In this case we say that item j implies item i (short i → j {\displaystyle i\rightarrow j} ). The goal of an ITA is to uncover such deterministic implications from the data set D. == Algorithms for ITA


id: item-tree-analysis-20260505-1
file: data/en.wikipedia.org/wiki/Item_tree_analysis-1.md
category: reference
title: Item tree analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Item_tree_analysis
date_saved: 2026-05-05T09:54:17.467755+00:00
instance: kb-cron
parent: item-tree-analysis-20260505-0
summary:
== Notes == == References == Bart, W. M., & Krus, D. J. (1973). An ordering-theoretic method to determine hierarchies among items. Educational and psychological measurement, 33, 291–300. Duquenne V (1987). Conceptual Implications Between Attributes and some Representation Properties for Finite Lattices. In B Ganter, R Wille, K Wolfe (eds.), Beiträge zur Begriffsanalyse: Vorträge der Arbeitstagung Begriffsanalyse, Darmstadt 1986, pp. 313–339. Wissenschafts-Verlag, Mannheim. Flament C (1976). L’Analyse Bool´eenne de Questionnaire. Mouton, Paris. Held, T., & Korossy, K. (1998). Data-analysis as heuristic for establishing theoretically founded item structures. Zeitschrift für Psychologie, 206, 169–188. Janssens, R. (1999). A Boolean approach to the measurement of group processes and attitudes. The concept of integration as an example. Mathematical Social Sciences, 38, 275–293. Schrepp M (1999). On the Empirical Construction of Implications on Bi-valued Test Items. Mathematical Social Sciences, 38(3), 361–375. Schrepp, M (2002). Explorative analysis of empirical data by boolean analysis of questionnaires. Zeitschrift für Psychologie, 210/2, S. 99-109. Schrepp, M. (2003). A method for the analysis of hierarchical dependencies between items of a questionnaire. Methods of Psychological Research, 19, 43–79. Schrepp, M. (2006). ITA 2.0: A program for Classical and Inductive Item Tree Analysis. Journal of Statistical Software, Vol. 16, Issue 10. Schrepp,


