# kb

id: fisher-information-20260505-2
file: data/en.wikipedia.org/wiki/Fisher_information-2.md
category: reference
title: Fisher information
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Fisher_information
date_saved: 2026-05-05T09:50:15.726073+00:00
instance: kb-cron
parent: fisher-information-20260505-0
summary:
In other words, the precision to which we can estimate θ is fundamentally limited by the Fisher information of the likelihood function. Alternatively, the same conclusion can be obtained directly from the Cauchy–Schwarz inequality for random variables, | Cov ⁡ ( A , B ) | 2 ≤ Var ⁡ ( A ) Var ⁡ ( B ) {\displaystyle |\operatorname {Cov} (A,B)|^{2}\leq \operatorname {Var} (A)\operatorname {Var} (B)} , applied to the random variables θ ^ ( X ) {\displaystyle {\hat {\theta }}(X)} and ∂ θ log ⁡ f ( X ; θ ) {\displaystyle \partial _{\theta }\log f(X;\theta )} , and observing that for unbiased estimators we have Cov ⁡ [ θ ^ ( X ) , ∂ θ log ⁡ f ( X ; θ ) ] = ∫ θ ^ ( x ) ∂ θ f ( x ; θ ) d x = ∂ θ E ⁡ [ θ ^ ] = 1. {\displaystyle \operatorname {Cov} [{\hat {\theta }}(X),\partial _{\theta }\log f(X;\theta )]=\int {\hat {\theta }}(x)\,\partial _{\theta }f(x;\theta )\,dx=\partial _{\theta }\operatorname {E} [{\hat {\theta }}]=1.} == Examples == === Single-parameter Bernoulli experiment === A Bernoulli trial is a random variable with two possible outcomes, 0 and 1,


id: fisher-information-20260505-3
file: data/en.wikipedia.org/wiki/Fisher_information-3.md
category: reference
title: Fisher information
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Fisher_information
date_saved: 2026-05-05T09:50:15.726073+00:00
instance: kb-cron
parent: fisher-information-20260505-0
summary:
=== Estimate θ from X ~ Bern (√θ) === As another toy example consider a random variable X {\displaystyle X} with possible outcomes 0 and 1, with probabilities p 0 = 1 − θ {\displaystyle p_{0}=1-{\sqrt {\theta }}} and p 1 = θ {\displaystyle p_{1}={\sqrt {\theta }}} , respectively, for some θ ∈ [ 0 , 1 ] {\displaystyle \theta \in [0,1]} . Our goal is estimating θ {\displaystyle \theta } from observations of X {\displaystyle X} . The Fisher information reads in this case I ( θ ) = E [ ( ∂ ∂ θ log ⁡ f ( X ; θ ) ) 2 | θ ] = ( 1 − θ ) ( − 1 2 θ ( 1 − θ ) ) 2 + θ ( 1 2 θ ) 2 = 1 4 θ ( 1 1 − θ + 1 θ ) . {\displaystyle {\begin{aligned}{\mathcal {I}}(\theta )&=\mathrm {E} \left[\left({\frac {\partial }{\partial \theta }}\log f(X;\theta )\right)^{2}{\Bigg |}\,\theta \right]\\&=(1-{\sqrt {\theta }})\left({\frac {-1}{2{\sqrt {\theta }}(1-{\sqrt {\theta }})}}\right)^{2}+{\sqrt {\theta }}\left({\frac {1}{2\theta }}\right)^{2}\\&={\frac {1}{4\theta }}\left({\frac {1}{1-{\sqrt {\theta }}}}+{\frac {1}{\sqrt {\theta }}}\right)\end{aligned}}.} This expression can also be derived directly from the change of reparametrization formula given below. More generally, for


id: fisher-information-20260505-4
file: data/en.wikipedia.org/wiki/Fisher_information-4.md
category: reference
title: Fisher information
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Fisher_information
date_saved: 2026-05-05T09:50:15.726073+00:00
instance: kb-cron
parent: fisher-information-20260505-0
summary:
=== Multivariate normal distribution === The FIM for a N-variate multivariate normal distribution, X ∼ N ( μ ( θ ) , Σ ( θ ) ) {\displaystyle \,X\sim N\left(\mu (\theta ),\,\Sigma (\theta )\right)} has a special form. Let the K-dimensional vector of parameters be θ = [ θ 1 … θ K ] T {\displaystyle \theta ={\begin{bmatrix}\theta _{1}&\dots &\theta _{K}\end{bmatrix}}^{\textsf {T}}} and the vector of normal random variables be X = [ X 1 … X N ] T {\displaystyle X={\begin{bmatrix}X_{1}&\dots &X_{N}\end{bmatrix}}^{\textsf {T}}} . Assume that the mean values of these random variables are μ ( θ ) = [ μ 1 ( θ ) … μ N ( θ ) ] T {\displaystyle \,\mu (\theta )={\begin{bmatrix}\mu _{1}(\theta )&\dots &\mu _{N}(\theta )\end{bmatrix}}^{\textsf {T}}} , and let Σ ( θ ) {\displaystyle \,\Sigma (\theta )} be the covariance matrix. Then, for 1 ≤ m , n ≤ K {\displaystyle 1\leq m,\,n\leq K} , the (m, n) entry of the FIM is: I m , n = ∂ μ T ∂ θ m Σ − 1 ∂ μ ∂ θ n + 1 2 tr ⁡ ( Σ − 1 ∂ Σ ∂ θ m Σ − 1 ∂ Σ ∂


id: fisher-information-20260505-5
file: data/en.wikipedia.org/wiki/Fisher_information-5.md
category: reference
title: Fisher information
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Fisher_information
date_saved: 2026-05-05T09:50:15.726073+00:00
instance: kb-cron
parent: fisher-information-20260505-0
summary:
Given a convex function f : [ 0 , ∞ ) → ( − ∞ , ∞ ] {\displaystyle f:[0,\infty )\to (-\infty ,\infty ]} that f ( x ) {\displaystyle f(x)} is finite for all x > 0 {\displaystyle x>0} , f ( 1 ) = 0 {\displaystyle f(1)=0} , and f ( 0 ) = lim t → 0 + f ( t ) {\displaystyle f(0)=\lim _{t\to 0^{+}}f(t)} , (which could be infinite), it defines an f-divergence D f {\displaystyle D_{f}} . Then if f {\displaystyle f} is strictly convex at 1 {\displaystyle 1} , then locally at θ ∈ Θ {\displaystyle \theta \in \Theta } , the Fisher information matrix is a metric, in the sense that ( δ θ ) T I ( θ ) ( δ θ ) = 1 f ″ ( 1 ) D f ( P θ + δ θ ∥ P θ ) {\displaystyle (\delta \theta )^{T}I(\theta )(\delta \theta )={\frac {1}{f''(1)}}D_{f}(P_{\theta +\delta \theta }\parallel P_{\theta })} where P θ {\displaystyle P_{\theta }} is the distribution parametrized by θ {\displaystyle \theta } . That is, it's the distribution with pdf f ( x ; θ ) {\displaystyle f(x;\theta )} . In this form,


id: fisher-information-20260505-6
file: data/en.wikipedia.org/wiki/Fisher_information-6.md
category: reference
title: Fisher information
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Fisher_information
date_saved: 2026-05-05T09:50:15.726073+00:00
instance: kb-cron
parent: fisher-information-20260505-0
summary:
where Z ε {\displaystyle Z_{\varepsilon }} is a Gaussian variable with covariance matrix ε I {\displaystyle \varepsilon I} . The name "surface area" is apt because the entropy power e H ( X ) {\displaystyle e^{H(X)}} is the volume of the "effective support set", so S ( X ) {\displaystyle S(X)} is the "derivative" of the volume of the effective support set, much like the Minkowski-Steiner formula. The remainder of the proof uses the entropy power inequality, which is like the Brunn–Minkowski inequality. The trace of the Fisher information matrix is found to be a factor of S ( X ) {\displaystyle S(X)} . == Applications == === Optimal design of experiments === Fisher information is widely used in optimal experimental design. Because of the reciprocity of estimator-variance and Fisher information, minimizing the variance corresponds to maximizing the information. When the linear (or linearized) statistical model has several parameters, the mean of the parameter estimator is a vector and its variance is a matrix. The inverse of the variance matrix is called the "information matrix". Because the variance of the estimator of a parameter vector is a matrix, the problem of "minimizing the variance" is complicated. Using statistical theory, statisticians


id: fisher-information-20260505-7
file: data/en.wikipedia.org/wiki/Fisher_information-7.md
category: reference
title: Fisher information
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Fisher_information
date_saved: 2026-05-05T09:50:15.726073+00:00
instance: kb-cron
parent: fisher-information-20260505-0
summary:
Thus the Fisher information represents the curvature of the relative entropy of a conditional distribution with respect to its parameters. == History == The Fisher information was discussed by several early statisticians, notably F. Y. Edgeworth. For example, Savage says: "In it [Fisher information], he [Fisher] was to some extent anticipated (Edgeworth 1908–9 esp. 502, 507–8, 662, 677–8, 82–5 and references he [Edgeworth] cites including Pearson and Filon 1898 [. . .])." There are a number of early historical sources and a number of reviews of this early work. == See also == Efficiency (statistics) Observed information Fisher information metric Formation matrix Information geometry Jeffreys prior Cramér–Rao bound Minimum Fisher information Quantum Fisher information White information matrix test Other measures employed in information theory: Entropy (information theory) Kullback–Leibler divergence Self-information == Notes == == References == Cramér, Harald (1946). Mathematical methods of statistics. Princeton mathematical series. Princeton: Princeton University Press. ISBN 0-691-08004-6. {{cite book}}: ISBN / Date incompatibility (help) Edgeworth, F. Y. (Jun 1908). "On the Probable Errors of Frequency-Constants". Journal of the Royal Statistical Society. 71 (2): 381–397. doi:10.2307/2339461. JSTOR 2339461. Edgeworth, F. Y. (Sep 1908). "On the Probable Errors of Frequency-Constants (Contd.)". Journal of the Royal Statistical Society.


id: fisher-s-inequality-20260505-0
file: data/en.wikipedia.org/wiki/Fisher's_inequality-0.md
category: reference
title: Fisher's inequality
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Fisher's_inequality
date_saved: 2026-05-05T09:50:16.972104+00:00
instance: kb-cron
parent: fisher-s-inequality-20260505-0
summary:
Fisher's inequality is a necessary condition for the existence of a balanced incomplete block design, that is, a system of subsets that satisfy certain prescribed conditions in combinatorial mathematics. Outlined by Ronald Fisher, a population geneticist and statistician, who was concerned with the design of experiments such as studying the differences among several different varieties of plants, under each of a number of different growing conditions, called blocks. Let: v be the number of varieties of plants; b be the number of blocks. To be a balanced incomplete block design it is required that: k different varieties are in each block, 1 ≤ k < v; no variety occurs twice in any one block; any two varieties occur together in exactly λ blocks; each variety occurs in exactly r blocks. Fisher's inequality states simply that b ≥ v. == Proof == Let the incidence matrix M be a v × b matrix defined so that Mi,j is 1 if element i is in block j and 0 otherwise. Then B = MMT is a v × v matrix such that Bi,i = r and Bi,j = λ for i ≠ j. Since r ≠ λ, det(B) ≠ 0, so rank(B)


id: fractional-factorial-design-20260505-0
file: data/en.wikipedia.org/wiki/Fractional_factorial_design-0.md
category: reference
title: Fractional factorial design
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Fractional_factorial_design
date_saved: 2026-05-05T09:50:18.197962+00:00
instance: kb-cron
parent: fractional-factorial-design-20260505-0
summary:
In statistics, a fractional factorial design is a way to conduct experiments with fewer experimental runs than a full factorial design. Instead of testing every single combination of factors, it tests only a carefully selected portion. This "fraction" of the full design is chosen to reveal the most important information about the system being studied (sparsity-of-effects principle), while significantly reducing the number of runs required. It is based on the idea that many tests in a full factorial design can be redundant. However, this reduction in runs comes at the cost of potentially more complex analysis, as some effects can become intertwined, making it impossible to isolate their individual influences. Therefore, choosing which combinations to test in a fractional factorial design must be done carefully. == History == Fractional factorial design was introduced by British statistician David John Finney in 1945, extending previous work by Ronald Fisher on the full factorial experiment at Rothamsted Experimental Station. Developed originally for agricultural applications, it has since been applied to other areas of engineering, science, and business. == Basic working principle == Similar to a full factorial experiment, a fractional factorial experiment investigates the effects of independent variables, known as factors, on a


id: fractional-factorial-design-20260505-1
file: data/en.wikipedia.org/wiki/Fractional_factorial_design-1.md
category: reference
title: Fractional factorial design
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Fractional_factorial_design
date_saved: 2026-05-05T09:50:18.197962+00:00
instance: kb-cron
parent: fractional-factorial-design-20260505-0
summary:
== Generation == In practice, experimenters typically rely on statistical reference books to supply the "standard" fractional factorial designs, consisting of the principal fraction. The principal fraction is the set of treatment combinations for which the generators evaluate to + under the treatment combination algebra. However, in some situations, experimenters may take it upon themselves to generate their own fractional design. A fractional factorial experiment is generated from a full factorial experiment by choosing an alias structure. The alias structure determines which effects are confounded with each other. For example, the five-factor 25 − 2 can be generated by using a full three-factor factorial experiment involving three factors (say A, B, and C) and then choosing to confound the two remaining factors D and E with interactions generated by D = A*B and E = A*C. These two expressions are called the generators of the design. So for example, when the experiment is run and the experimenter estimates the effects for factor D, what is really being estimated is a combination of the main effect of D and the two-factor interaction involving A and B. An important characteristic of a fractional design is the defining relation, which gives the set


id: generalized-randomized-block-design-20260505-0
file: data/en.wikipedia.org/wiki/Generalized_randomized_block_design-0.md
category: reference
title: Generalized randomized block design
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Generalized_randomized_block_design
date_saved: 2026-05-05T09:50:19.398918+00:00
instance: kb-cron
parent: generalized-randomized-block-design-20260505-0
summary:
In randomized statistical experiments, generalized randomized block designs (GRBDs) are used to study the interaction between blocks and treatments. For a GRBD, each treatment is replicated at least two times in each block; this replication allows the estimation and testing of an interaction term in the linear model (without making parametric assumptions about a normal distribution for the error). == Univariate response == === GRBDs versus RCBDs: Replication and interaction === Like a randomized complete block design (RCBD), a GRBD is randomized. Within each block, treatments are randomly assigned to experimental units: this randomization is also independent between blocks. In a (classic) RCBD, however, there is no replication of treatments within blocks. === Two-way linear model: Blocks and treatments === The experimental design guides the formulation of an appropriate linear model. Without replication, the (classic) RCBD has a two-way linear-model with treatment- and block-effects but without a block-treatment interaction. Without replicates, this two-way linear-model that may be estimated and tested without making parametric assumptions (by using the randomization distribution, without using a normal distribution for the error). In the RCBD, the block-treatment interaction cannot be estimated using the randomization distribution; a fortiori there exists no "valid" (i.e. randomization-based) test for


id: gittins-index-20260505-0
file: data/en.wikipedia.org/wiki/Gittins_index-0.md
category: reference
title: Gittins index
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Gittins_index
date_saved: 2026-05-05T09:50:20.644914+00:00
instance: kb-cron
parent: gittins-index-20260505-0
summary:
The Gittins index is a measure of the reward that can be achieved through a given stochastic process with certain properties, namely: the process has an ultimate termination state and evolves with an option, at each intermediate state, of terminating. Upon terminating at a given state, the reward achieved is the sum of the probabilistic expected rewards associated with every state from the actual terminating state to the ultimate terminal state, inclusive. The index is a real scalar. == Terminology == To illustrate the theory we can take two examples from a developing sector, such as from electricity generating technologies: wind power and wave power. If we are presented with the two technologies when they are both proposed as ideas we cannot say which will be better in the long run as we have no data, as yet, to base our judgments on. It would be easy to say that wave power would be too problematic to develop as it seems easier to put up many wind turbines than to make the long floating generators, tow them out to sea and lay the cables necessary. If we were to make a judgment call at that early time in development we


id: gittins-index-20260505-1
file: data/en.wikipedia.org/wiki/Gittins_index-1.md
category: reference
title: Gittins index
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Gittins_index
date_saved: 2026-05-05T09:50:20.644914+00:00
instance: kb-cron
parent: gittins-index-20260505-0
summary:
== History == Questions about the optimal stopping policies in the context of clinical trials have been open from the 1940s and in the 1960s a few authors analyzed simple models leading to optimal index policies, but it was only in the 1970s that Gittins and his collaborators demonstrated in a Markovian framework that the optimal solution of the general case is an index policy whose "dynamic allocation index" is computable in principle for every state of each project as a function of the single project's dynamics. In parallel to Gittins, Martin Weitzman established the same result in the economics literature. Soon after the seminal paper of Gittins, Peter Whittle demonstrated that the index emerges as a Lagrange multiplier from a dynamic programming formulation of the problem called retirement process and conjectured that the same index would be a good heuristic in a more general setup named Restless bandit. The question of how to actually calculate the index for Markov chains was first addressed by Varaiya and his collaborators with an algorithm that computes the indexes from the largest first down to the smallest and by Chen and Katehakis who showed that standard LP could be used to calculate the


id: gittins-index-20260505-2
file: data/en.wikipedia.org/wiki/Gittins_index-2.md
category: reference
title: Gittins index
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Gittins_index
date_saved: 2026-05-05T09:50:20.644914+00:00
instance: kb-cron
parent: gittins-index-20260505-0
summary:
=== Generalized index === If the probability of survival β ( i ) {\displaystyle \beta (i)} depends on the state i {\displaystyle i} , a generalization introduced by Sonin (2008) defines the Gittins index α ( i ) {\displaystyle \alpha (i)} as the maximum discounted total reward per chance of termination. α ( i ) = sup τ > 0 R τ ( i ) Q τ ( i ) {\displaystyle \alpha (i)=\sup _{\tau >0}{\frac {R^{\tau }(i)}{Q^{\tau }(i)}}} where R τ ( i ) = ⟨ ∑ t = 0 τ − 1 R [ Z ( t ) ] ⟩ Z ( 0 ) = i {\displaystyle R^{\tau }(i)=\left\langle \sum _{t=0}^{\tau -1}R[Z(t)]\right\rangle _{Z(0)=i}} Q τ ( i ) = ⟨ 1 − ∏ t = 0 τ − 1 β [ Z ( t ) ] ⟩ Z ( 0 ) = i {\displaystyle Q^{\tau }(i)=\left\langle 1-\prod _{t=0}^{\tau -1}\beta [Z(t)]\right\rangle _{Z(0)=i}} If β t {\displaystyle \beta ^{t}} is replaced by ∏ j = 0 t − 1 β [ Z ( j ) ] {\displaystyle \prod _{j=0}^{t-1}\beta [Z(j)]} in the definitions of ν ( i ) {\displaystyle \nu (i)} , w ( i ) {\displaystyle w(i)} and h (


id: glossary-of-experimental-design-20260505-0
file: data/en.wikipedia.org/wiki/Glossary_of_experimental_design-0.md
category: reference
title: Glossary of experimental design
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Glossary_of_experimental_design
date_saved: 2026-05-05T09:50:21.948791+00:00
instance: kb-cron
parent: glossary-of-experimental-design-20260505-0
summary:
A glossary of terms used in experimental research. == Concerned fields == Statistics Experimental design Estimation theory


id: glossary-of-experimental-design-20260505-1
file: data/en.wikipedia.org/wiki/Glossary_of_experimental_design-1.md
category: reference
title: Glossary of experimental design
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Glossary_of_experimental_design
date_saved: 2026-05-05T09:50:21.948791+00:00
instance: kb-cron
parent: glossary-of-experimental-design-20260505-0
summary:
== Glossary == Alias: When the estimate of an effect also includes the influence of one or more other effects (usually high order interactions) the effects are said to be aliased (see confounding). For example, if the estimate of effect D in a four factor experiment actually estimates (D + ABC), then the main effect D is aliased with the 3-way interaction ABC. Note: This causes no difficulty when the higher order interaction is either non-existent or insignificant. Analysis of variance (ANOVA): A mathematical process for separating the variability of a group of observations into assignable causes and setting up various significance tests. Balanced design: An experimental design where all cells (i.e. treatment combinations) have the same number of observations. Blocking: A schedule for conducting treatment combinations in an experimental study such that any effects on the experimental results due to a known change in raw materials, operators, machines, etc., become concentrated in the levels of the blocking variable. Note: the reason for blocking is to isolate a systematic effect and prevent it from obscuring the main effects. Blocking is achieved by restricting randomization. Center Points: Points at the center value of all factor ranges. Coding Factor Levels: Transforming the


id: glossary-of-experimental-design-20260505-2
file: data/en.wikipedia.org/wiki/Glossary_of_experimental_design-2.md
category: reference
title: Glossary of experimental design
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Glossary_of_experimental_design
date_saved: 2026-05-05T09:50:21.948791+00:00
instance: kb-cron
parent: glossary-of-experimental-design-20260505-0
summary:
Lack of fit error: Error that occurs when the analysis omits one or more important terms or factors from the process model. Note: Including replication in a designed experiment allows separation of experimental error into its components: lack of fit and random (pure) error. Model: Mathematical relationship which relates changes in a given response to changes in one or more factors. Nested Factors: See factors above. Orthogonality: Two vectors of the same length are orthogonal if the sum of the products of their corresponding elements is 0. Note: An experimental design is orthogonal if the effects of any factor balance out (sum to zero) across the effects of the other factors. Paradigm: a model created given the basic design, the hypothesis and the particular conditions for the experiment. Random effect: An effect associated with input variables chosen at random from a population having a large or infinite number of possible values. Random error: Error that occurs due to natural variation in the process. Note: Random error is typically assumed to be normally distributed with zero mean and a constant variance. Note: Random error is also called experimental error. Randomization: A schedule for allocating treatment material and for conducting treatment combinations


id: group-testing-20260505-0
file: data/en.wikipedia.org/wiki/Group_testing-0.md
category: reference
title: Group testing
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Group_testing
date_saved: 2026-05-05T09:50:23.496143+00:00
instance: kb-cron
parent: group-testing-20260505-0
summary:
In statistics and combinatorial mathematics, group testing is any procedure that breaks up the task of identifying objects into tests on groups of items, rather than testing each item individually. First studied by Robert Dorfman in 1943, group testing is a relatively new field of mathematics that can be applied to a wide range of practical applications and is an active area of research today. A familiar example of group testing involves a string of light bulbs connected in series, where exactly one of the bulbs is known to be broken. The objective is to find the broken bulb using the smallest number of tests (where a test is when some of the bulbs are connected to a power supply). A simple approach is to test each bulb individually. However, when there are a large number of bulbs it would be much more efficient to pool the bulbs into groups. For example, by connecting the first half of the bulbs at once, it can be determined which half the broken bulb is in, ruling out half of the bulbs in just one test. Schemes for carrying out group testing can be simple or complex and the tests involved at each


id: group-testing-20260505-1
file: data/en.wikipedia.org/wiki/Group_testing-1.md
category: reference
title: Group testing
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Group_testing
date_saved: 2026-05-05T09:50:23.496143+00:00
instance: kb-cron
parent: group-testing-20260505-0
summary:
=== Variations and extensions === There are many ways to extend the problem of group testing. One of the most important is called noisy group testing, and deals with a big assumption of the original problem: that testing is error-free. A group-testing problem is called noisy when there is some chance that the result of a group test is erroneous (e.g. comes out positive when the test contained no defectives). The Bernoulli noise model assumes this probability is some constant, q {\displaystyle q} , but in general it can depend on the true number of defectives in the test and the number of items tested. For example, the effect of dilution can be modelled by saying a positive result is more likely when there are more defectives (or more defectives as a fraction of the number tested), present in the test. A noisy algorithm will always have a non-zero probability of making an error (that is, mislabeling an item). Group testing can be extended by considering scenarios in which there are more than two possible outcomes of a test. For example, a test may have the outcomes 0 , 1 {\displaystyle 0,1} and 2 + {\displaystyle 2^{+}} , corresponding to


id: group-testing-20260505-2
file: data/en.wikipedia.org/wiki/Group_testing-2.md
category: reference
title: Group testing
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Group_testing
date_saved: 2026-05-05T09:50:23.496143+00:00
instance: kb-cron
parent: group-testing-20260505-0
summary:
=== Combinatorial group testing === Group testing was first studied in the combinatorial context by Li in 1962, with the introduction of Li’s s {\displaystyle s} -stage algorithm. Li proposed an extension of Dorfman's '2-stage algorithm' to an arbitrary number of stages that required no more than t = e log 2 ⁡ ( e ) d log 2 ⁡ ( n ) {\textstyle t={\frac {e}{\log _{2}(e)}}d\log _{2}(n)} tests to be guaranteed to find d {\displaystyle d} or fewer defectives among n {\displaystyle n} items. The idea was to remove all the items in negative tests, and divide the remaining items into groups as was done with the initial pool. This was to be done s − 1 {\displaystyle s-1} times before performing individual testing. Combinatorial group testing in general was later studied more fully by Katona in 1973. Katona introduced the matrix representation of non-adaptive group-testing and produced a procedure for finding the defective in the non-adaptive 1-defective case in no more than t = ⌈ log 2 ⁡ ( n ) ⌉ {\displaystyle t=\lceil \log _{2}(n)\rceil } tests, which he also proved to be optimal. In general, finding optimal algorithms for adaptive combinatorial group testing is difficult, and


id: group-testing-20260505-3
file: data/en.wikipedia.org/wiki/Group_testing-3.md
category: reference
title: Group testing
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Group_testing
date_saved: 2026-05-05T09:50:23.496143+00:00
instance: kb-cron
parent: group-testing-20260505-0
summary:
x {\displaystyle \mathbf {x} } is intended to describe the (unknown) set of defective items. The key property of x {\displaystyle \mathbf {x} } is that it is an implicit input. That is to say, there is no direct knowledge of what the entries of x {\displaystyle \mathbf {x} } are, other than that which can be inferred via some series of 'tests'. This leads on to the next definition. Let x {\displaystyle \mathbf {x} } be an input vector. A set, S ⊆ { 1 , 2 , … , n } {\displaystyle S\subseteq \{1,2,\dots ,n\}} is called a test. When testing is noiseless, the result of a test is positive when there exists j ∈ S {\displaystyle j\in S} such that x j = 1 {\displaystyle x_{j}=1} , and the result is negative otherwise. Therefore, the goal of group testing is to come up with a method for choosing a 'short' series of tests that allow x {\displaystyle \mathbf {x} } to be determined, either exactly or with a high degree of certainty. A group-testing algorithm is said to make an error if it incorrectly labels an item (that is, labels any defective item as non-defective or vice


id: group-testing-20260505-4
file: data/en.wikipedia.org/wiki/Group_testing-4.md
category: reference
title: Group testing
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Group_testing
date_saved: 2026-05-05T09:50:23.496143+00:00
instance: kb-cron
parent: group-testing-20260505-0
summary:
Suppose a non-adaptive group testing procedure for n {\displaystyle n} items consists of the tests S 1 , S 2 , … , S t {\displaystyle S_{1},S_{2},\dots ,S_{t}} for some t ∈ N ≥ 0 {\displaystyle t\in \mathbb {N} _{\geq 0}} . The testing matrix for this scheme is the t × n {\displaystyle t\times n} binary matrix, M {\displaystyle M} , where ( M ) i j = 1 {\displaystyle (M)_{ij}=1} if and only if j ∈ S i {\displaystyle j\in S_{i}} (and is zero otherwise). Thus each column of M {\displaystyle M} represents an item and each row represents a test, with a 1 {\displaystyle 1} in the ( i , j ) -th {\displaystyle (i,j){\textrm {-th}}} entry indicating that the i -th {\displaystyle i{\textrm {-th}}} test included the j -th {\displaystyle j{\textrm {-th}}} item and a 0 {\displaystyle 0} indicating otherwise. As well as the vector x {\displaystyle \mathbf {x} } (of length n {\displaystyle n} ) that describes the unknown defective set, it is common to introduce the result vector, which describes the results of each test. Let t {\displaystyle t} be the number of tests performed by a non-adaptive algorithm. The result vector, y =


id: group-testing-20260505-5
file: data/en.wikipedia.org/wiki/Group_testing-5.md
category: reference
title: Group testing
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Group_testing
date_saved: 2026-05-05T09:50:23.496143+00:00
instance: kb-cron
parent: group-testing-20260505-0
summary:
If n ≤ 2 d − 2 {\displaystyle n\leq 2d-2} , test the n {\displaystyle n} items individually. Otherwise, set l = n − d + 1 {\displaystyle l=n-d+1} and α = ⌊ log 2 ⁡ l / d ⌋ {\displaystyle \alpha =\lfloor \log _{2}{l/d}\rfloor } . Test a group of size 2 α {\displaystyle 2^{\alpha }} . If the outcome is negative, every item in the group is declared to be non-defective; set n := n − 2 α {\displaystyle n:=n-2^{\alpha }} and go to step 1. Otherwise, use a binary search to identify one defective and an unspecified number, called x {\displaystyle x} , of non-defective items; set n := n − 1 − x {\displaystyle n:=n-1-x} and d := d − 1 {\displaystyle d:=d-1} . Go to step 1. The generalised binary-splitting algorithm requires no more than T {\displaystyle T} tests where T = { n n ≤ 2 d − 2 ( α + 2 ) d + p − 1 n ≥ 2 d − 1 {\displaystyle T={\begin{cases}n&n\leq 2d-2\\(\alpha +2)d+p-1&n\geq 2d-1\end{cases}}} . For n / d {\displaystyle n/d} large, it can be shown that T → d log 2 ⁡ ( n / d )


id: group-testing-20260505-6
file: data/en.wikipedia.org/wiki/Group_testing-6.md
category: reference
title: Group testing
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Group_testing
date_saved: 2026-05-05T09:50:23.496143+00:00
instance: kb-cron
parent: group-testing-20260505-0
summary:
=== Sequential COMP (SCOMP) === SCOMP (Sequential COMP) is an algorithm that makes use of the fact that DD makes no mistakes until the last step, where it is assumed that the remaining items are non-defective. Let the set of declared defectives be K {\displaystyle K} . A positive test is called explained by K {\displaystyle K} if it contains at least one item in K {\displaystyle K} . The key observation with SCOMP is that the set of defectives found by DD may not explain every positive test, and that every unexplained test must contain a hidden defective. The algorithm proceeds as follows. Carry out steps 1 and 2 of the DD algorithm to obtain K {\displaystyle K} , an initial estimate for the set of defectives. If K {\displaystyle K} explains every positive test, terminate the algorithm: K {\displaystyle K} is the final estimate for the set of defectives. If there are any unexplained tests, find the "possible defective" that appears in the largest number of unexplained tests, and declare it to be defective (that is, add it to the set K {\displaystyle K} ). Go to step 2. In simulations, SCOMP has been shown to perform close


id: group-testing-20260505-7
file: data/en.wikipedia.org/wiki/Group_testing-7.md
category: reference
title: Group testing
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Group_testing
date_saved: 2026-05-05T09:50:23.496143+00:00
instance: kb-cron
parent: group-testing-20260505-0
summary:
This method uses q ( c − 1 ) ( d + 1 ) {\displaystyle q(c-1)(d+1)} tests to exactly identify up to d {\displaystyle d} positives among n = q c {\displaystyle n=q^{c}} samples. Because of this PP is particularly effective for large sample sizes, since the number of tests grows only linearly with respect to c {\displaystyle c} while the samples grow exponentially with this parameter. However PP can also be effective for small sample sizes. == Example applications == The generality of the theory of group testing lends it to many diverse applications, including clone screening, locating electrical shorts; high speed computer networks; medical examination, quantity searching, statistics; machine learning, DNA sequencing; cryptography; and data forensics. This section provides a brief overview of a small selection of these applications. === Multiaccess channels === A multiaccess channel is a communication channel that connects many users at once. Every user can listen and transmit on the channel, but if more than one user transmits at the same time, the signals collide, and are reduced to unintelligible noise. Multiaccess channels are important for various real-world applications, notably wireless computer networks and phone networks. A prominent problem with multiaccess channels is how


id: group-testing-20260505-8
file: data/en.wikipedia.org/wiki/Group_testing-8.md
category: reference
title: Group testing
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Group_testing
date_saved: 2026-05-05T09:50:23.496143+00:00
instance: kb-cron
parent: group-testing-20260505-0
summary:
=== Machine learning and compressed sensing === Machine learning is a field of computer science that has many software applications such as DNA classification, fraud detection and targeted advertising. One of the main subfields of machine learning is the 'learning by examples' problem, where the task is to approximate some unknown function when given its value at a number of specific points. As outlined in this section, this function learning problem can be tackled with a group-testing approach. In a simple version of the problem, there is some unknown function, f : { 0 , 1 } N → { 0 , 1 } {\displaystyle f:\{0,1\}^{N}\to \{0,1\}} where f ( x ) = a ⋅ x {\displaystyle f({\textbf {x}})={\textbf {a}}\cdot {\textbf {x}}} , and a ∈ { 0 , 1 } N {\displaystyle {\textbf {a}}\in \{0,1\}^{N}} (using logical arithmetic: addition is logical OR and multiplication is logical AND). Here a {\displaystyle {\textbf {a}}} is ' d {\displaystyle d} sparse', which means that at most d ≪ N {\displaystyle d\ll N} of its entries are 1 {\displaystyle 1} . The aim is to construct an approximation to f {\displaystyle f} using t {\displaystyle t} point evaluations, where t {\displaystyle t} is


id: group-testing-20260505-9
file: data/en.wikipedia.org/wiki/Group_testing-9.md
category: reference
title: Group testing
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Group_testing
date_saved: 2026-05-05T09:50:23.496143+00:00
instance: kb-cron
parent: group-testing-20260505-0
summary:
=== Data forensics === Data forensics is a field dedicated to finding methods for compiling digital evidence of a crime. Such crimes typically involve an adversary modifying the data, documents or databases of a victim, with examples including the altering of tax records, a virus hiding its presence, or an identity thief modifying personal data. A common tool in data forensics is the one-way cryptographic hash. This is a function that takes the data, and through a difficult-to-reverse procedure, produces a unique number called a hash. Hashes, which are often much shorter than the data, allow us to check if the data has been changed without having to wastefully store complete copies of the information: the hash for the current data can be compared with a past hash to determine if any changes have occurred. An unfortunate property of this method is that, although it is easy to tell if the data has been modified, there is no way of determining how: that is, it is impossible to recover which part of the data has changed. One way to get around this limitation is to store more hashes – now of subsets of the data structure – to narrow down


id: ignorability-20260505-0
file: data/en.wikipedia.org/wiki/Ignorability-0.md
category: reference
title: Ignorability
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Ignorability
date_saved: 2026-05-05T09:50:24.772348+00:00
instance: kb-cron
parent: ignorability-20260505-0
summary:
In statistics, ignorability is a feature of an experiment design whereby the method of data collection (and the nature of missing data) does not depend on the missing data. A missing data mechanism such as a treatment assignment or survey sampling strategy is "ignorable" if the missing data matrix, which indicates which variables are observed or missing, is independent of the missing data conditional on the observed data. It has also been called unconfoundedness, selection on the observables, or no omitted variable bias. This idea is part of the Rubin Causal Inference Model, developed by Donald Rubin in collaboration with Paul Rosenbaum in the early 1970s. The exact definition differs between their articles in that period. In a 1978 article, Rubin discusses ignorable assignment mechanisms, which can be understood as the way individuals are assigned to treatment groups being irrelevant for the data analysis, given everything that is recorded about that individual. In a 1983 paper on propensity score matching, Rubin and Rosenbaum define the stronger condition of a treatment assignment being strongly ignorable, mathematically formulated as ( r 1 , r 0 ) ⊥ ⊥ z ∣ v , 0 < pr ⁡ ( z = 1 ) <


id: institutional-review-board-20260505-0
file: data/en.wikipedia.org/wiki/Institutional_review_board-0.md
category: reference
title: Institutional review board
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Institutional_review_board
date_saved: 2026-05-05T09:50:26.001945+00:00
instance: kb-cron
parent: institutional-review-board-20260505-0
summary:
An institutional review board (IRB), also known as an independent ethics committee (IEC), ethical review board (ERB), or research ethics board (REB), is a committee at an institution that applies research ethics by reviewing the methods proposed for research involving human subjects, to ensure that the projects are ethical. The main goal of IRB reviews is to ensure that study participants are not harmed (or that harms are minimal and outweighed by research benefits). Such boards are formally designated to approve (or reject), monitor, and review biomedical and behavioral research involving humans, and they are legally required in some countries under certain specified circumstances. Most countries use some form of IRB to safeguard ethical conduct of research so that it complies with national and international norms, regulations or codes. The purpose of the IRB is to assure that appropriate steps are taken to protect the rights and welfare of people participating in a research study. A key goal of IRBs is to protect human subjects from physical or psychological harm, which they attempt to do by reviewing research protocols and related materials. The protocol review assesses the ethics of the research and its methods, promotes fully informed and voluntary participation


id: institutional-review-board-20260505-1
file: data/en.wikipedia.org/wiki/Institutional_review_board-1.md
category: reference
title: Institutional review board
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Institutional_review_board
date_saved: 2026-05-05T09:50:26.001945+00:00
instance: kb-cron
parent: institutional-review-board-20260505-0
summary:
Research in conventional educational settings, such as those involving the study of instructional strategies or effectiveness of various techniques, curricula, or classroom management methods. In the case of studies involving the use of educational tests, there are specific provisions in the exemption to ensure that subjects cannot be identified or exposed to risks or liabilities. Research involving the analysis of existing data and other materials, where the data is either already publicly available or will be analyzed such that individual subjects cannot be identified. Studies intended to assess the performance or effectiveness of public benefit or service programs, or to evaluate food taste, quality, or consumer acceptance. Generally, human research ethics guidelines require that decisions about exemption are made by an IRB representative, not by the investigators themselves. Additionally, research projects conducted outside of a federal government agency or government-funded institution, such as a citizen science project conducted by a private individual or a group of private individuals, are generally not required to be approved by any institutional review board, unless the project is funded by the US federal government. == International ethics review committees == Numerous other countries have equivalent regulations or guidelines governing human subject studies and the


id: institutional-review-board-20260505-2
file: data/en.wikipedia.org/wiki/Institutional_review_board-2.md
category: reference
title: Institutional review board
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Institutional_review_board
date_saved: 2026-05-05T09:50:26.001945+00:00
instance: kb-cron
parent: institutional-review-board-20260505-0
summary:
Safeguard the rights, safety, and well-being of all trial subjects. Special attention should be paid to trials that may include vulnerable subjects, such as pregnant women, children, prisoners, the elderly, or persons with diminished comprehension. Obtain trial protocol(s)/amendment(s), written Informed Consent Form(s) (ICFs) and consent form updates the investigator proposes for use in the trial, subject recruitment procedures (e.g., advertisements), written information to be provided to subjects, investigator's brochure, available safety information, information about payments and compensation available to subjects, the investigator's current curriculum vitae and/or other documentation evidencing qualifications, and any other documents the IRB may need to fulfill its responsibilities. Review both the amount and method of payment to subjects to assure neither presents problems of coercion or undue influence on the trial subjects. Payments to a subject should be prorated and not wholly contingent on completion of the trial by the subject. Information regarding payment to subjects, including the methods, amounts, and schedule of payment to trial subjects, should be set forth in the written informed consent form and any other written information to be provided to subjects. The way payment will be prorated should be specified. Ensure that the proposed trial is reviewed within a reasonable


id: institutional-review-board-20260505-3
file: data/en.wikipedia.org/wiki/Institutional_review_board-3.md
category: reference
title: Institutional review board
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Institutional_review_board
date_saved: 2026-05-05T09:50:26.001945+00:00
instance: kb-cron
parent: institutional-review-board-20260505-0
summary:
== Relationship with citizen science == Generally speaking, citizen science, whether conducted by a single private individual or a group of individuals, is not required to follow the IRB process. This is true even if some of the individuals involved are professional researchers or are also employed at institutions that normally review all research conducted by the institution. However, many academic journals require proof of IRB approval for all human-subject research, even when it is not legally required, which means that citizen scientists may be unable to publish scientific papers describing their findings. Citizen scientists who expect to need IRB approval for publication or to comply with the terms of a research grant can pay a commercial IRB company. In the US, a standard initial review often costs a few thousand dollars; a review to determine that the project is less expensive. The IRB-based approach to ethics assumes that human-subject research is conducted by an institution employing researchers, and that the institution and researchers have far more power and knowledge than the participants. The researchers and the participants are seen as distinct groups, and the concern is to prevent the researchers from exploiting the participants as a means to an


id: interaction-statistics-20260505-0
file: data/en.wikipedia.org/wiki/Interaction_(statistics)-0.md
category: reference
title: Interaction (statistics)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Interaction_(statistics)
date_saved: 2026-05-05T09:50:27.222240+00:00
instance: kb-cron
parent: interaction-statistics-20260505-0
summary:
In statistics, an interaction may arise when considering the relationship among three or more variables, and describes a situation in which the effect of one causal variable on an outcome depends on the state of a second causal variable (that is, when effects of the two causes are not additive). Although commonly thought of in terms of causal relationships, the concept of an interaction can also describe non-causal associations (then also called moderation or effect modification). Interactions are often considered in the context of regression analyses or factorial experiments. The presence of interactions can have important implications for the interpretation of statistical models. If two variables of interest interact, the relationship between each of the interacting variables and a third "dependent variable" depends on the value of the other interacting variable. In practice, this makes it more difficult to predict the consequences of changing the value of a variable, particularly if the variables it interacts with are hard to measure or difficult to control. The notion of "interaction" is closely related to that of moderation that is common in social and health science research: the interaction between an explanatory variable and an environmental variable suggests that the effect of the


id: interaction-statistics-20260505-1
file: data/en.wikipedia.org/wiki/Interaction_(statistics)-1.md
category: reference
title: Interaction (statistics)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Interaction_(statistics)
date_saved: 2026-05-05T09:50:27.222240+00:00
instance: kb-cron
parent: interaction-statistics-20260505-0
summary:
The distinction between qualitative and quantitative interactions depends on the order in which the variables are considered (in contrast, the property of additivity is invariant to the order of the variables). In the following table, if we focus on the effect of treatment A, there is a quantitative interaction — giving treatment A will improve the outcome on average regardless of whether treatment B is or is not already being given (although the benefit is greater if treatment A is given alone). However, if we focus on the effect of treatment B, there is a qualitative interaction — giving treatment B to a subject who is already receiving treatment A will (on average) make things worse, whereas giving treatment B to a subject who is not receiving treatment A will improve the outcome on average. === Unit treatment additivity === In its simplest form, the assumption of treatment unit additivity states that the observed response yij from experimental unit i when receiving treatment j can be written as the sum yij = yi + tj. The assumption of unit treatment additivity implies that every treatment has exactly the same additive effect on each experimental unit. Since any given experimental unit


id: interaction-statistics-20260505-2
file: data/en.wikipedia.org/wiki/Interaction_(statistics)-2.md
category: reference
title: Interaction (statistics)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Interaction_(statistics)
date_saved: 2026-05-05T09:50:27.222240+00:00
instance: kb-cron
parent: interaction-statistics-20260505-0
summary:
=== In regression === The most general approach to modeling interaction effects involves regression, starting from the elementary version given above: Y = c + a x 1 + b x 2 + d ( x 1 × x 2 ) + error {\displaystyle Y=c+ax_{1}+bx_{2}+d(x_{1}\times x_{2})+{\text{error}}\,} where the interaction term ( x 1 × x 2 ) {\displaystyle (x_{1}\times x_{2})} could be formed explicitly by multiplying two (or more) variables, or implicitly using factorial notation in modern statistical packages such as Stata. The components x1 and x2 might be measurements or {0,1} dummy variables in any combination. Interactions involving a dummy variable multiplied by a measurement variable are termed slope dummy variables, because they estimate and test the difference in slopes between groups 0 and 1. When measurement variables are employed in interactions, it is often desirable to work with centered versions, where the variable's mean (or some other reasonably central value) is set as zero. Centering can make the main effects in interaction models more interpretable, as it reduces the multicollinearity between the interaction term and the main effects. The coefficient a in the equation above, for example, represents the effect of x1 when x2 equals zero. Regression approaches


id: interaction-statistics-20260505-3
file: data/en.wikipedia.org/wiki/Interaction_(statistics)-3.md
category: reference
title: Interaction (statistics)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Interaction_(statistics)
date_saved: 2026-05-05T09:50:27.222240+00:00
instance: kb-cron
parent: interaction-statistics-20260505-0
summary:
Interaction between adding sugar to coffee and stirring the coffee. Neither of the two individual variables has much effect on sweetness but a combination of the two does. Interaction between adding carbon to steel and quenching. Neither of the two individually has much effect on strength but a combination of the two has a dramatic effect. Interaction between smoking and inhaling asbestos fibres: Both raise lung carcinoma risk, but exposure to asbestos multiplies the cancer risk in smokers and non-smokers. Here, the joint effect of inhaling asbestos and smoking is higher than the sum of both effects. Interaction between genetic risk factors for type 2 diabetes and diet (specifically, a "western" dietary pattern). The western dietary pattern was shown to increase diabetes risk for subjects with a high "genetic risk score", but not for other subjects. Interaction between education and political orientation, affecting general-public perceptions about climate change. For example, US surveys often find that acceptance of the reality of anthropogenic climate change rises with education among moderate or liberal survey respondents, but declines with education among the most conservative. Similar interactions have been observed to affect some non-climate science or environmental perceptions, and to operate with science literacy or


id: interrupted-time-series-20260505-0
file: data/en.wikipedia.org/wiki/Interrupted_time_series-0.md
category: reference
title: Interrupted time series
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Interrupted_time_series
date_saved: 2026-05-05T09:50:28.536522+00:00
instance: kb-cron
parent: interrupted-time-series-20260505-0
summary:
Interrupted time series analysis (ITS), sometimes known as quasi-experimental time series analysis, is a method of statistical analysis involving tracking a long-term period before and after a point of intervention to assess the intervention's effects. The time series refers to the data over the period, while the interruption is the intervention, which is a controlled external influence or set of influences. Effects of the intervention are evaluated by changes in the level and slope of the time series and statistical significance of the intervention parameters. Interrupted time series design is the design of experiments based on the interrupted time series approach. The method is used in various areas of research, such as: political science: impact of changes in laws on the behavior of people; (e.g., Effectiveness of sex offender registration policies in the United States) economics: impact of changes in credit controls on borrowing behavior; sociology: impact of experiments in income maintenance on the behavior of participants in welfare programs; history: impact of major historical events on the behavior of those affected by the events; psychology: impact of expressing emotional experiences on online content; medicine: in medical research, medical treatment is an intervention whose effect are to be studied; marketing


id: jadad-scale-20260505-0
file: data/en.wikipedia.org/wiki/Jadad_scale-0.md
category: reference
title: Jadad scale
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Jadad_scale
date_saved: 2026-05-05T09:50:29.828460+00:00
instance: kb-cron
parent: jadad-scale-20260505-0
summary:
The Jadad scale, sometimes known as Jadad scoring or the Oxford quality scoring system, is a procedure to assess the methodological quality of a clinical trial by objective criteria. It is named after Canadian-Colombian physician Alex Jadad who in 1996 described a system for allocating such trials a score of between zero (very poor) and five (rigorous). It is the most widely used such assessment in the world, and as of May 2024, its seminal paper has been cited in over 25,000 scientific works. == Description == The Jadad scale independently assesses the methodological quality of a clinical trial judging the effectiveness of blinding. Alejandro "Alex" Jadad Bechara, a Colombian physician who worked as a Research Fellow at the Oxford Pain Relief Unit, Nuffield Department of Anaesthetics at the University of Oxford described the allocating trials a score of between zero (very poor) and five (rigorous) in an appendix to a 1996 paper. In a 2007 book, Jadad described the randomised controlled trial as "one of the simplest, most powerful and revolutionary forms of research". == Background == Clinical trials are conducted for the purpose of collecting data on the efficacy of medical treatments. The treatment might be, for example,


id: lack-of-fit-sum-of-squares-20260505-0
file: data/en.wikipedia.org/wiki/Lack-of-fit_sum_of_squares-0.md
category: reference
title: Lack-of-fit sum of squares
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Lack-of-fit_sum_of_squares
date_saved: 2026-05-05T09:50:31.030292+00:00
instance: kb-cron
parent: lack-of-fit-sum-of-squares-20260505-0
summary:
In statistics, a sum of squares due to lack of fit, or more tersely a lack-of-fit sum of squares, is one of the components of a partition of the sum of squares of residuals in an analysis of variance, used in the numerator in an F-test of the null hypothesis that says that a proposed model fits well. The other component is the pure-error sum of squares. The pure-error sum of squares is the sum of squared deviations of each value of the dependent variable from the average value over all observations sharing its independent variable value(s). These are errors that could never be avoided by any predictive equation that assigned a predicted value for the dependent variable as a function of the value(s) of the independent variable(s). The remainder of the residual sum of squares is attributed to lack of fit of the model since it would be mathematically possible to eliminate these errors entirely. == Principle == In order for the lack-of-fit sum of squares to differ from the sum of squares of residuals, there must be more than one value of the response variable for at least one of the values of the set of predictor variables.


id: lack-of-fit-sum-of-squares-20260505-1
file: data/en.wikipedia.org/wiki/Lack-of-fit_sum_of_squares-1.md
category: reference
title: Lack-of-fit sum of squares
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Lack-of-fit_sum_of_squares
date_saved: 2026-05-05T09:50:31.030292+00:00
instance: kb-cron
parent: lack-of-fit-sum-of-squares-20260505-0
summary:
=== Sums of squares === Suppose the error terms ε i j are independent and normally distributed with expected value 0 and variance σ2. We treat x i as constant rather than random. Then the response variables Y i j are random only because the errors ε i j are random. It can be shown to follow that if the straight-line model is correct, then the sum of squares due to error divided by the error variance, 1 σ 2 ∑ i = 1 n ∑ j = 1 n i ε ^ i j 2 {\displaystyle {\frac {1}{\sigma ^{2}}}\sum _{i=1}^{n}\sum _{j=1}^{n_{i}}{\widehat {\varepsilon }}_{ij}^{\,2}} has a chi-squared distribution with N − 2 degrees of freedom. Moreover, given the total number of observations N, the number of levels of the independent variable n, and the number of parameters in the model p: The sum of squares due to pure error, divided by the error variance σ2, has a chi-squared distribution with N − n degrees of freedom; The sum of squares due to lack of fit, divided by the error variance σ2, has a chi-squared distribution with n − p degrees of freedom (here p = 2 as there are two


id: latin-hypercube-sampling-20260505-0
file: data/en.wikipedia.org/wiki/Latin_hypercube_sampling-0.md
category: reference
title: Latin hypercube sampling
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Latin_hypercube_sampling
date_saved: 2026-05-05T09:50:33.795501+00:00
instance: kb-cron
parent: latin-hypercube-sampling-20260505-0
summary:
Latin hypercube sampling (LHS) is a statistical method for generating a near-random sample of parameter values from a multidimensional distribution. The sampling method is often used to construct computer experiments or for Monte Carlo integration. LHS was described by Michael McKay of Los Alamos National Laboratory in 1979. An equivalent technique was independently proposed by Vilnis Eglājs in 1977. It was further elaborated by Ronald L. Iman and coauthors in 1981. Detailed computer codes and manuals were later published. In the context of statistical sampling, a square grid containing sample positions is a Latin square if (and only if) there is only one sample in each row and each column. A Latin hypercube is the generalisation of this concept to an arbitrary number of dimensions, whereby each sample is the only one in each axis-aligned hyperplane containing it. When sampling a function of N {\displaystyle N} variables, the range of each variable is divided into M {\displaystyle M} equally probable intervals. M {\displaystyle M} sample points are then placed to satisfy the Latin hypercube requirements; this forces the number of divisions, M {\displaystyle M} , to be equal for each variable. This sampling scheme does not require more samples for


id: latin-rectangle-20260505-0
file: data/en.wikipedia.org/wiki/Latin_rectangle-0.md
category: reference
title: Latin rectangle
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Latin_rectangle
date_saved: 2026-05-05T09:50:34.999456+00:00
instance: kb-cron
parent: latin-rectangle-20260505-0
summary:
In combinatorial mathematics, a Latin rectangle is an r × n matrix (where r ≤ n), using n symbols, usually the numbers 1, 2, 3, ..., n or 0, 1, ..., n − 1 as its entries, with no number occurring more than once in any row or column. An n × n Latin rectangle is called a Latin square. Latin rectangles and Latin squares may also be described as the optimal colorings of rook's graphs, or as optimal edge colorings of complete bipartite graphs. An example of a 3 × 5 Latin rectangle is: == Normalization == A Latin rectangle is called normalized (or reduced) if its first row is in natural order and so is its first column. The example above is not normalized. == Enumeration == Let L(k, n) denote the number of normalized k × n Latin rectangles. Then the total number of k × n Latin rectangles is n ! ( n − 1 ) ! L ( k , n ) ( n − k ) ! . {\displaystyle {\frac {n!(n-1)!L(k,n)}{(n-k)!}}.} A 2 × n Latin rectangle corresponds to a permutation with no fixed points. Such permutations have been called discordant permutations. An enumeration


id: latin-square-20260505-0
file: data/en.wikipedia.org/wiki/Latin_square-0.md
category: reference
title: Latin square
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Latin_square
date_saved: 2026-05-05T09:50:36.263147+00:00
instance: kb-cron
parent: latin-square-20260505-0
summary:
In combinatorics and in experimental design, a Latin square is an n × n array filled with n different symbols, each occurring exactly once in each row and exactly once in each column. An example of a 3 × 3 Latin square is The name "Latin square" was inspired by mathematical papers by Leonhard Euler (1707–1783), who used Latin characters as symbols, but any set of symbols can be used: in the above example, the alphabetic sequence A, B, C can be replaced by the integer sequence 1, 2, 3. Euler began the general theory of Latin squares. == History == The Korean mathematician Choi Seok-jeong was the first to publish an example of Latin squares of order nine, in order to construct a magic square in 1700, predating Leonhard Euler by 67 years. === Counting === This account follows McKay, Meynert & Myrvold (2007, p. 100). The counting of Latin squares has a long history, but the published accounts contain many errors. Euler in 1782, and Cayley in 1890, both knew the number of reduced Latin squares up to order five. In 1915, MacMahon approached the problem in a different way, but initially obtained the wrong value for order


id: latin-square-20260505-1
file: data/en.wikipedia.org/wiki/Latin_square-1.md
category: reference
title: Latin square
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Latin_square
date_saved: 2026-05-05T09:50:36.263147+00:00
instance: kb-cron
parent: latin-square-20260505-0
summary:
Many operations on a Latin square produce another Latin square (for example, turning it upside down). If we permute the rows, permute the columns, or permute the names of the symbols of a Latin square, we obtain a new Latin square said to be isotopic to the first. Isotopism is an equivalence relation, so the set of all Latin squares is divided into subsets, called isotopy classes, such that two squares in the same class are isotopic and two squares in different classes are not isotopic. A stronger form of equivalence exists. Two Latin squares L1 and L2 of side n with common symbol set S that is also the index set for the rows and columns of each square are isomorphic if there is a bijection g: S → S such that g(L1(i, j)) = L2(g(i), g(j)) for all i, j in S. An alternate way to define isomorphic Latin squares is to say that a pair of isotopic Latin squares are isomorphic if the three bijections used to show that they are isotopic are, in fact, equal. Isomorphism is also an equivalence relation and its equivalence classes are called isomorphism classes. Another type of operation is easiest to


id: latin-square-20260505-2
file: data/en.wikipedia.org/wiki/Latin_square-2.md
category: reference
title: Latin square
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Latin_square
date_saved: 2026-05-05T09:50:36.263147+00:00
instance: kb-cron
parent: latin-square-20260505-0
summary:
A transversal in a Latin square is a choice of n cells, where each row contains one cell, each column contains one cell, and there is one cell containing each symbol. One can consider a Latin square as a complete bipartite graph in which the rows are vertices of one part, the columns are vertices of the other part, each cell is an edge (between its row and its column), and the symbols are colors. The rules of the Latin squares imply that this is a proper edge coloring. With this definition, a Latin transversal is a matching in which each edge has a different color; such a matching is called a rainbow matching. Therefore, many results on Latin squares/rectangles are contained in papers with the term "rainbow matching" in their title, and vice versa. Some Latin squares have no transversal. For example, when n is even, an n-by-n Latin square in which the value of cell i,j is (i+j) mod n has no transversal. Here are two examples: [ 1 2 2 1 ] [ 1 2 3 4 2 3 4 1 3 4 1 2 4 1 2 3 ] {\displaystyle {\begin{bmatrix}1&2\\2&1\end{bmatrix}}\quad {\begin{bmatrix}1&2&3&4\\2&3&4&1\\3&4&1&2\\4&1&2&3\end{bmatrix}}} In 1967, H. J.


id: latin-square-20260505-3
file: data/en.wikipedia.org/wiki/Latin_square-3.md
category: reference
title: Latin square
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Latin_square
date_saved: 2026-05-05T09:50:36.263147+00:00
instance: kb-cron
parent: latin-square-20260505-0
summary:
A Latin rectangle is a generalization of a Latin square in which there are n columns and n possible values, but the number of rows may be smaller than n. Each value still appears at most once in each row and column. A Graeco-Latin square is a pair of two Latin squares such that, when one is laid on top of the other, each ordered pair of symbols appears exactly once. A Latin hypercube is a generalization of a Latin square from two dimensions to multiple dimensions. == See also == Block design Combinatorial design Eight queens puzzle Futoshiki Magic square Problems in Latin squares Rook's graph, a graph that has Latin squares as its colorings Sator Square Vedic square Word square == Notes == == References == Bailey, R.A. (2008). "6 Row-Column designs and 9 More about Latin squares". Design of Comparative Experiments. Cambridge University Press. ISBN 978-0-521-68357-9. MR 2422352. Dénes, J.; Keedwell, A. D. (1974). Latin squares and their applications. New York-London: Academic Press. p. 547. ISBN 0-12-209350-X. MR 0351850. Shah, Kirti R.; Sinha, Bikas K. (1989). "4 Row-Column Designs". Theory of Optimal Designs. Lecture Notes in Statistics. Vol. 54. Springer-Verlag. pp. 66–84. ISBN 0-387-96991-8. MR 1016151. van


id: limited-dependent-variable-20260505-0
file: data/en.wikipedia.org/wiki/Limited_dependent_variable-0.md
category: reference
title: Limited dependent variable
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Limited_dependent_variable
date_saved: 2026-05-05T09:50:37.460068+00:00
instance: kb-cron
parent: limited-dependent-variable-20260505-0
summary:
A limited dependent variable is a variable whose range of possible values is "restricted in some important way." In econometrics, the term is often used when estimation of the relationship between the limited dependent variable of interest and other variables requires methods that take this restriction into account. For example, this may arise when the variable of interest is constrained to lie between zero and one, as in the case of a probability, or is constrained to be positive, as in the case of wages or hours worked. Limited dependent variable models include: Censoring, where for some individuals in a data set, some data are missing but other data are present; Truncation, where some individuals are systematically excluded from observation (failure to take this phenomenon into account can result in selection bias); Discrete outcomes, such as binary decisions or qualitative data restricted to a small number of categories. Discrete choice models may have either unordered or ordered alternatives; ordered alternatives may take the form of count data or ordered rating responses (such as a Likert scale). == See also == Logit, logit model, ordered logit Multivariate probit models Probit, probit model, ordered probit Tobit model == See also == Censored


id: list-of-countries-implementing-pool-testing-strategy-against-covid-19-20260505-0
category: reference
title: List of countries implementing pool testing strategy against COVID-19
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/List_of_countries_implementing_pool_testing_strategy_against_COVID-19
date_saved: 2026-05-05T09:50:38.731783+00:00
instance: kb-cron
parent: list-of-countries-implementing-pool-testing-strategy-against-covid-19-20260505-0
file:
data/en.wikipedia.org/wiki/List_of_countries_implementing_pool_testing_strategy_against_COVID-19-0.md

summary:
If not otherwise stated, the virological status of sample pools is evaluated through PCR tests. == Screening strategies == === General population === === Campus screening strategies === SUNY campuses are implementing a pool testing strategy based on saliva samples, including Cornell, SUNY Fredonia or SUNY Oneonta. The University of Tennessee is also implementing a saliva pool testing strategy. Researchers from Yale University, Georgia Augusta University, the University of Illinois, are also considering or implementing pool testing strategies. == Small-scale screening or experimental protocols == == References == == See also == Group testing COVID-19 testing


id: manipulation-check-20260505-0
file: data/en.wikipedia.org/wiki/Manipulation_check-0.md
category: reference
title: Manipulation check
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Manipulation_check
date_saved: 2026-05-05T09:50:41.099667+00:00
instance: kb-cron
parent: manipulation-check-20260505-0
summary:
Manipulation check is a term in experimental research in the social sciences which refers to certain kinds of secondary evaluations of an experiment. == Overview == Manipulation checks are measured variables that show what the manipulated variables concurrently affect besides the dependent variable of interest. In experiments, an experimenter manipulates some aspect of a process or task and randomly assigns subjects to different levels of the manipulation ("experimental conditions"). The experimenter then observes whether variation in the manipulated variables cause differences in the dependent variable. Manipulation checks are targeted at variables beside the dependent variable of interest. Manipulations are not intended to verify that the manipulated factor caused variation in the dependent variable. This is verified by random assignment, manipulation before measurement of the dependent variable, and statistical tests of effect of the manipulated variable on the dependent variable. Thus, a failed manipulation check does not refute the hypothesis that the manipulation caused variation in the dependent variable. In contrast, a successful manipulation check can help an experimenter rule out reasons that a manipulation may have failed to influence a dependent variable. When a manipulation creates significant differences between experimental conditions in both (1) the dependent variable and (2) the


id: matching-statistics-20260505-0
file: data/en.wikipedia.org/wiki/Matching_(statistics)-0.md
category: reference
title: Matching (statistics)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Matching_(statistics)
date_saved: 2026-05-05T09:50:42.337218+00:00
instance: kb-cron
parent: matching-statistics-20260505-0
summary:
Matching is a statistical technique that evaluates the effect of a treatment by comparing the treated and the non-treated units in an observational study or quasi-experiment (i.e. when the treatment is not randomly assigned). The goal of matching is to reduce bias for the estimated treatment effect in an observational-data study, by finding, for every treated unit, one (or more) non-treated unit(s) with similar observable characteristics against which the covariates are balanced out (similar to the K-nearest neighbors algorithm). By matching treated units to similar non-treated units, matching enables a comparison of outcomes among treated and non-treated units to estimate the effect of the treatment reducing bias due to confounding. Propensity score matching, an early matching technique, was developed as part of the Rubin causal model, but has been shown to increase model dependence, bias, inefficiency, and power and is no longer recommended compared to other matching methods. A simple, easy-to-understand, and statistically powerful method of matching is known as Coarsened Exact Matching or CEM. Matching has been promoted by Donald Rubin. It was prominently criticized in economics by Robert LaLonde (1986), who compared estimates of treatment effects from an experiment to comparable estimates produced with matching methods and showed


id: minimisation-clinical-trials-20260505-0
file: data/en.wikipedia.org/wiki/Minimisation_(clinical_trials)-0.md
category: reference
title: Minimisation (clinical trials)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Minimisation_(clinical_trials)
date_saved: 2026-05-05T09:50:43.477942+00:00
instance: kb-cron
parent: minimisation-clinical-trials-20260505-0
summary:
Minimisation is a method of adaptive stratified sampling that is used in clinical trials, as described by Pocock and Simon. The aim of minimisation is to minimise the imbalance between the number of patients in each treatment group over a number of factors. Normally patients would be allocated to a treatment group randomly and while this maintains a good overall balance, it can lead to imbalances within sub-groups. For example, if a majority of the patients who were receiving the active drug happened to be male, or smokers, the statistical usefulness of the study would be reduced. The traditional method to avoid this problem, known as blocked randomisation, is to stratify patients according to a number of factors (e.g. male and female, or smokers and non-smokers) and to use a separate randomisation list for each group. Each randomisation list would be created such that after every block of x patients, there would be an equal number in each treatment group. The problem with this method is that the number of lists increases exponentially with the number of stratification factors. Minimisation addresses this problem by calculating the imbalance within each factor should the patient be allocated to a particular treatment group.


id: multi-attribute-global-inference-of-quality-20260505-0
file: data/en.wikipedia.org/wiki/Multi-attribute_global_inference_of_quality-0.md
category: reference
title: Multi-attribute global inference of quality
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Multi-attribute_global_inference_of_quality
date_saved: 2026-05-05T09:50:44.654885+00:00
instance: kb-cron
parent: multi-attribute-global-inference-of-quality-20260505-0
summary:
Multi-attribute global inference of quality (MAGIQ) is a multi-criteria decision analysis technique. MAGIQ is based on a hierarchical decomposition of comparison attributes and rating assignment using rank order centroids. == Description == The MAGIQ technique is used to assign a single, overall measure of quality to each member of a set of systems where each system has an arbitrary number of comparison attributes. The MAGIQ technique has features similar to the analytic hierarchy process and the simple multi-attribute rating technique exploiting ranks (SMARTER) technique. The MAGIQ technique was first published by James D. McCaffrey. The MAGIQ process begins with an evaluator determining which system attributes are to be used as the basis for system comparison. These attributes are ranked by importance to the particular problem domain, and the ranks are converted to ratings using rank order centroids. Each system under analysis is ranked against each comparison attribute and the ranks are transformed into rank order centroids. The final overall quality metric for each system is the weighted (by comparison attribute importance) sum of each attribute rating. The references provide specific examples of the process. There is little direct research on the theoretical soundness and effectiveness of the MAGIQ technique as


id: multicollinearity-20260505-0
file: data/en.wikipedia.org/wiki/Multicollinearity-0.md
category: reference
title: Multicollinearity
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Multicollinearity
date_saved: 2026-05-05T09:50:45.853835+00:00
instance: kb-cron
parent: multicollinearity-20260505-0
summary:
In statistics, multicollinearity or collinearity is a situation where the predictors in a regression model are linearly dependent. Perfect multicollinearity refers to a situation where the predictive variables have an exact linear relationship. When there is perfect collinearity, the design matrix X {\displaystyle X} has less than full rank, and therefore the moment matrix X T X {\displaystyle X^{\mathsf {T}}X} cannot be inverted. In this situation, the parameter estimates of the regression are not well-defined, as the system of equations has infinitely many solutions. Imperfect multicollinearity refers to a situation where the predictive variables have a nearly exact linear relationship. The Gauss–Markov theorem assumes absence of perfect multicollinearity. The adverse effect of highly correlated variables is widely known and proven many times. To address the high collinearity of a dataset, variance inflation factor can be used to identify the collinearity of the predictor variables. == Perfect multicollinearity == Perfect multicollinearity refers to a situation where the predictors are linearly dependent (one can be written as an exact linear function of the others). Ordinary least squares requires inverting the matrix X T X {\displaystyle X^{\mathsf {T}}X} , where X = [ 1 X 11 ⋯ X k 1 ⋮ ⋮ ⋮


id: multicollinearity-20260505-1
file: data/en.wikipedia.org/wiki/Multicollinearity-1.md
category: reference
title: Multicollinearity
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Multicollinearity
date_saved: 2026-05-05T09:50:45.853835+00:00
instance: kb-cron
parent: multicollinearity-20260505-0
summary:
Using prior information or theory. For example, if we notice Alice never steps in puddles, we can reasonably argue puddles are not why she wears boots, as she does not need the boots to avoid puddles. Collecting more data. If we observe Alice enough times, we will eventually see her on days where there are puddles but not rain (e.g. because the rain stops before she leaves home). This confounding becomes substantially worse when researchers attempt to ignore or suppress it by excluding these variables from the regression (see #Misuse). Excluding multicollinear variables from regressions will invalidate causal inference and produce worse estimates by removing important confounders. Multicollinearity can significantly affect the stability and interpretability of regression models. When predictor variables are highly correlated, the variance of the estimated regression coefficients increases, which may lead to large standard errors and unreliable statistical inference. As a result, coefficients may appear statistically insignificant even when the overall model has strong explanatory power. This issue does not necessarily reduce predictive performance, but it complicates the interpretation of individual predictors. === Remedies === There are many ways to prevent multicollinearity from affecting results by planning ahead of time. However, these methods all require a


id: multicollinearity-20260505-2
file: data/en.wikipedia.org/wiki/Multicollinearity-2.md
category: reference
title: Multicollinearity
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Multicollinearity
date_saved: 2026-05-05T09:50:45.853835+00:00
instance: kb-cron
parent: multicollinearity-20260505-0
summary:
== Further reading == Belsley, David A.; Kuh, Edwin; Welsch, Roy E. (1980). Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. New York: Wiley. ISBN 978-0-471-05856-4. Goldberger, Arthur S. (1991). "Multicollinearity". A Course in Econometrics. Cambridge: Harvard University Press. pp. 245–53. ISBN 9780674175440. Hill, R. Carter; Adkins, Lee C. (2001). "Collinearity". In Baltagi, Badi H. (ed.). A Companion to Theoretical Econometrics. Blackwell. pp. 256–278. doi:10.1002/9780470996249.ch13. ISBN 978-0-631-21254-6. Johnston, John (1972). Econometric Methods (Second ed.). New York: McGraw-Hill. pp. 159–168. ISBN 9780070326798. Kalnins, Arturs (2022). "When does multicollinearity bias coefficients and cause type 1 errors? A reconciliation of Lindner, Puck, and Verbeke (2020) with Kalnins (2018)". Journal of International Business Studies. 53 (7): 1536–1548. doi:10.1057/s41267-022-00531-9. S2CID 249323519. Kmenta, Jan (1986). Elements of Econometrics (Second ed.). New York: Macmillan. pp. 430–442. ISBN 978-0-02-365070-3. Maddala, G. S.; Lahiri, Kajal (2009). Introduction to Econometrics (Fourth ed.). Chichester: Wiley. pp. 279–312. ISBN 978-0-470-01512-4. Tomaschek, Fabian; Hendrix, Peter; Baayen, R. Harald (2018). "Strategies for addressing collinearity in multivariate linguistic data". Journal of Phonetics. 71: 249–267. doi:10.1016/j.wocn.2018.09.004. == External links == Thoma, Mark (2 March 2011). "Econometrics Lecture (topic: multicollinearity)". University of Oregon. Archived from the original on 12 December 2021 – via YouTube. Earliest Uses:


id: multifactor-design-of-experiments-software-20260505-0
file: data/en.wikipedia.org/wiki/Multifactor_design_of_experiments_software-0.md
category: reference
title: Multifactor design of experiments software
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Multifactor_design_of_experiments_software
date_saved: 2026-05-05T09:50:47.021513+00:00
instance: kb-cron
parent: multifactor-design-of-experiments-software-20260505-0
summary:
Software that is used for designing factorial experiments plays an important role in scientific experiments and represents a route to the implementation of design of experiments procedures that derive from statistical and combinatorial theory. In principle, easy-to-use design of experiments (DOE) software should be available to all experimenters to foster use of DOE. == Background == == Use of software == Factorial experimental design software drastically simplifies previously laborious hand calculations needed before the use of computers. During World War II, a more sophisticated form of DOE, called factorial design, became a big weapon for speeding up industrial development for the Allied forces. These designs can be quite compact, involving as few as two levels of each factor and only a fraction of all the combinations, and yet they are quite powerful for screening purposes. After the war, a statistician at Imperial Chemical, George Box, described how to generate response surfaces for process optimization. From this point forward, DOE took hold in the chemical process industry, where factors such as time, temperature, pressure, concentration, flow rate and agitation are easily manipulated. DOE results, when discovered accurately with DOE software, strengthen the capability to discern truths about sample populations being tested:


id: multiple-baseline-design-20260505-0
file: data/en.wikipedia.org/wiki/Multiple_baseline_design-0.md
category: reference
title: Multiple baseline design
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Multiple_baseline_design
date_saved: 2026-05-05T09:50:48.236349+00:00
instance: kb-cron
parent: multiple-baseline-design-20260505-0
summary:
A multiple baseline design is used in medical, psychological, and biological research. The multiple baseline design was first reported in 1960 as used in basic operant research. It was applied in the late 1960s to human experiments in response to practical and ethical issues that arose in withdrawing apparently successful treatments from human subjects. In it two or more (often three) behaviors, people or settings are plotted in a staggered graph where a change is made to one, but not the other two, and then to the second, but not the third behavior, person or setting. Differential changes that occur to each behavior, person or in each setting help to strengthen what is essentially an AB design with its problematic competing hypotheses. Because treatment is started at different times, changes are attributable to the treatment rather than to a chance factor. By gathering data from many subjects (instances), inferences can be made about the likeliness that the measured trait generalizes to a greater population. In multiple baseline designs, the experimenter starts by measuring a trait of interest, then applies a treatment before measuring that trait again. Treatment does not begin until a stable baseline has been recorded, and does not


id: multiple-treatments-20260505-0
file: data/en.wikipedia.org/wiki/Multiple_treatments-0.md
category: reference
title: Multiple treatments
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Multiple_treatments
date_saved: 2026-05-05T09:50:49.491929+00:00
instance: kb-cron
parent: multiple-treatments-20260505-0
summary:
Multiple treatments, like multivalued treatments, generalize the binary treatment effects framework. But rather than focusing on a treatment effect that can take on different values, the focus now is on different types of treatment. One example could be a job training program, where different types of job training are offered to the participants. The case of multiple treatments is relatively difficult to handle, as it can require additional functional form restrictions, especially when addressing the counterfactual or potential outcomes framework. Nevertheless, the general instrumental variable framework used to analyze binary treatment effects has been extended to allow for multiple treatments. There are different approaches available to analyze multiple treatment effects. One can think of treatment effects within this framework as the difference in the counterfactual outcomes that would have been observed if the agent faced different general choice sets, with multinomial choices being a natural way to analyze multiple treatments. More formally, assume there are J options available and the value to the agent of choosing option j is Rj (Zj )=vj (Zj ) -ϵj where εj is some unobserved random shock. Then the agent will choose alternative j such that Rj ≥ Rk for all k≠j. There is a


id: multivalued-treatment-20260505-0
file: data/en.wikipedia.org/wiki/Multivalued_treatment-0.md
category: reference
title: Multivalued treatment
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Multivalued_treatment
date_saved: 2026-05-05T09:50:50.722035+00:00
instance: kb-cron
parent: multivalued-treatment-20260505-0
summary:
In statistics, in particular in the design of experiments, a multi-valued treatment is a treatment that can take on more than two values. It is related to the dose-response model in the medical literature. == Description == Generally speaking, treatment levels may be finite or infinite as well as ordinal or cardinal, which leads to a large collection of possible treatment effects to be studied in applications. One example is the effect of different levels of program participation (e.g. full-time and part-time) in a job training program. Assume there exists a finite collection of multi-valued treatment status T = { 0 , 1 , 2 , … , J , } {\displaystyle T=\{0,1,2,\ldots ,J,\}} with J some fixed integer. As in the potential outcomes framework, denote Y ( j ) ⊂ R {\displaystyle Y(j)\subset R} the collection of potential outcomes under the treatment J, and Y = ∑ j = 0 J D j Y ( j ) {\displaystyle Y=\textstyle \sum _{j=0}^{J}\displaystyle D_{j}Y(j)} denotes the observed outcome and D j {\displaystyle D_{j}} is an indicator that equals 1 when the treatment equals j and 0 when it does not equal j, leading to a fundamental problem of causal inference. A


id: multivariate-analysis-of-variance-20260505-0
file: data/en.wikipedia.org/wiki/Multivariate_analysis_of_variance-0.md
category: reference
title: Multivariate analysis of variance
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Multivariate_analysis_of_variance
date_saved: 2026-05-05T09:50:51.939099+00:00
instance: kb-cron
parent: multivariate-analysis-of-variance-20260505-0
summary:
In statistics, multivariate analysis of variance (MANOVA) is a procedure for comparing multivariate sample means. As a multivariate procedure, it is used when there are two or more dependent variables, and is often followed by significance tests involving individual dependent variables separately. Without relation to the image, the dependent variables may be k life satisfactions scores measured at sequential time points and p job satisfaction scores measured at sequential time points. In this case there are k+p dependent variables whose linear combination follows a multivariate normal distribution, multivariate variance-covariance matrix homogeneity, and linear relationship, no multicollinearity, and each without outliers. == Model == Assume n {\textstyle n} q {\textstyle q} -dimensional observations, where the i {\textstyle i} ’th observation y i {\textstyle y_{i}} is assigned to the group g ( i ) ∈ { 1 , … , m } {\textstyle g(i)\in \{1,\dots ,m\}} and is distributed around the group center μ ( g ( i ) ) ∈ R q {\textstyle \mu ^{(g(i))}\in \mathbb {R} ^{q}} with multivariate Gaussian noise: y i = μ ( g ( i ) ) + ε i ε i ∼ i.i.d. N q ( 0 , Σ ) for i = 1 ,


id: n-of-1-trial-20260505-0
file: data/en.wikipedia.org/wiki/N-of-1_trial-0.md
category: reference
title: N-of-1 trial
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/N-of-1_trial
date_saved: 2026-05-05T09:50:53.125257+00:00
instance: kb-cron
parent: n-of-1-trial-20260505-0
summary:
An N-of-1 (N=1) trial is a multiple crossover clinical trial, conducted in a single patient. A trial in which random allocation is used to determine the order in which an experimental and a control intervention are given to a single patient is an N-of-1 randomized controlled trial. Some N-of-1 trials involve randomized assignment and blinding, but the order of experimental and control interventions can also be fixed by the researcher. This type of study has enabled practitioners to achieve experimental progress without the work of designing a group comparison study. This design, especially if including blinding and wash-out periods, can be effective in confirming causality. N-of-1 trials, if used in clinical practice to inform therapeutic decisions concerned with the patient participating in the trial, can be a source of evidence about individual treatment responses, fulfilling the promise of personalized medicine. == Design == The N-of-1 trials can be designed in many ways. For example, Single-Patient Open Trials (SPOTs) are located somewhere in between the formal (explanatory) N-of-1 trials and the trial and error approach used in clinical practice and are characterized by at least one crossover period with washout in between. One of the most common procedures is the ABA


id: national-research-ethics-service-20260505-0
file: data/en.wikipedia.org/wiki/National_Research_Ethics_Service-0.md
category: reference
title: National Research Ethics Service
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/National_Research_Ethics_Service
date_saved: 2026-05-05T09:50:54.306375+00:00
instance: kb-cron
parent: national-research-ethics-service-20260505-0
summary:
The National Research Ethics Service (NRES) is a UK medical quango which deals with research ethics. Principal Investigators must describe the experiment they intend to pursue to the NRES for its approval, failing which the study is prohibited. == History == The NRES was launched on 1 April 2007. The adjective "National" was omitted from the name at some unknown point in time. == Purpose == In 2009, the NRES issued a leaflet in which it described its purpose: The National Research Ethics Service (NRES) reviews research proposals to protect the rights and safety of research participants and enables ethical research which is of potential benefit to science and society. == Substance of reports == The substance of the NRES reports can be gleaned from an approval obtained in 2011 by Stephanie Taylor, who was then Professor of Public Health and Primary Care at Queen Mary University of London. == References ==


id: nested-case-control-study-20260505-0
file: data/en.wikipedia.org/wiki/Nested_case–control_study-0.md
category: reference
title: Nested case–control study
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Nested_case–control_study
date_saved: 2026-05-05T09:50:55.486311+00:00
instance: kb-cron
parent: nested-case-control-study-20260505-0
summary:
A nested case–control (NCC) study is a variation of a case–control study in which cases and controls are drawn from the population in a fully enumerated cohort. Usually, the exposure of interest is only measured among the cases and the selected controls. Thus the nested case–control study is more efficient than the full cohort design. The nested case–control study can be analyzed using methods for missing covariates. The NCC design is often used when the exposure of interest is difficult or expensive to obtain and when the outcome is rare. By utilizing data previously collected from a large cohort study, the time and cost of beginning a new case–control study is avoided. By only measuring the covariate in as many participants as necessary, the cost and effort of exposure assessment is reduced. This benefit is pronounced when the covariate of interest is biological, since assessments such as gene expression profiling are expensive, and because the quantity of blood available for such analysis is often limited, making it a valuable resource that should not be used unnecessarily. == Example == As an example, of the 91,523 women in the Nurses' Health Study who did not have cancer at baseline and who


id: null-hypothesis-20260505-0
file: data/en.wikipedia.org/wiki/Null_hypothesis-0.md
category: reference
title: Null hypothesis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Null_hypothesis
date_saved: 2026-05-05T09:50:56.623806+00:00
instance: kb-cron
parent: null-hypothesis-20260505-0
summary:
The null hypothesis (often denoted H 0 {\textstyle H_{0}} ) is the claim in scientific research that the effect being studied does not exist. The null hypothesis can also be described as the hypothesis in which no relationship exists between two sets of data or variables being analyzed. If the null hypothesis is true, any experimentally observed effect is due to chance alone, hence the term "null". In contrast with the null hypothesis, an alternative hypothesis (often denoted H A {\displaystyle H_{A}} or H 1 {\displaystyle H_{1}} ) is developed, which claims that a relationship does exist between two variables. == Basic definitions == The null hypothesis and the alternative hypothesis are types of conjectures used in statistical tests to make statistical inferences, which are formal methods of reaching conclusions and separating scientific claims from statistical noise. The statement being tested in a test of statistical significance is called the null hypothesis. The test of significance is designed to assess the strength of the evidence against the null hypothesis, or a statement of 'no effect' or 'no difference'. It is often symbolized as H 0 {\displaystyle H_{0}} . The statement that is being tested against the null hypothesis is the


id: null-hypothesis-20260505-1
file: data/en.wikipedia.org/wiki/Null_hypothesis-1.md
category: reference
title: Null hypothesis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Null_hypothesis
date_saved: 2026-05-05T09:50:56.623806+00:00
instance: kb-cron
parent: null-hypothesis-20260505-0
summary:
== Technical description == The null hypothesis is a default hypothesis that a quantity to be measured is zero (null). Typically, the quantity to be measured is the difference between two situations. For instance, trying to determine if there is a positive proof that an effect has occurred or that samples derive from different batches. The null hypothesis is generally assumed to remain possibly true. Multiple analyses can be performed to show how the hypothesis should either be rejected or excluded e.g. having a high confidence level, thus demonstrating a statistically significant difference. This is demonstrated by showing that zero is outside of the specified confidence interval of the measurement on either side, typically within the real numbers. Failure to exclude the null hypothesis (with any confidence) does not logically confirm or support the (unprovable) null hypothesis. (When it is proven that something is e.g. bigger than x, it does not necessarily imply it is plausible that it is smaller or equal than x; it may instead be a poor quality measurement with low accuracy. Confirming the null hypothesis is two-sided would amount to positively proving it is bigger or equal than 0 and to positively proving it is smaller


id: null-hypothesis-20260505-2
file: data/en.wikipedia.org/wiki/Null_hypothesis-2.md
category: reference
title: Null hypothesis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Null_hypothesis
date_saved: 2026-05-05T09:50:56.623806+00:00
instance: kb-cron
parent: null-hypothesis-20260505-0
summary:
== Principle == Hypothesis testing requires constructing a statistical model of what the data would look like if chance or random processes alone were responsible for the results. The hypothesis that chance alone is responsible for the results is called the null hypothesis. The model of the result of the random process is called the distribution under the null hypothesis. The obtained results are compared with the distribution under the null hypothesis, and the likelihood of finding the obtained results is thereby determined. Hypothesis testing works by collecting data and measuring how likely the particular set of data is (assuming the null hypothesis is true), when the study is on a randomly selected representative sample. The null hypothesis assumes no relationship between variables in the population from which the sample is selected. If the data-set of a randomly selected representative sample is very unlikely relative to the null hypothesis (defined as being part of a class of sets of data that are rarely observed), the experimenter rejects the null hypothesis, concluding it (probably) is false. This class of data-sets is usually specified via a test statistic, which is designed to measure the extent of apparent departure from the null hypothesis.


id: null-hypothesis-20260505-3
file: data/en.wikipedia.org/wiki/Null_hypothesis-3.md
category: reference
title: Null hypothesis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Null_hypothesis
date_saved: 2026-05-05T09:50:56.623806+00:00
instance: kb-cron
parent: null-hypothesis-20260505-0
summary:
== Choice of the null hypothesis == The choice of the null hypothesis is associated with sparse and inconsistent advice. Fisher mentioned few constraints on the choice and stated that many null hypotheses should be considered and that many tests are possible for each. The variety of applications and the diversity of goals suggests that the choice can be complicated. In many applications the formulation of the test is traditional. A familiarity with the range of tests available may suggest a particular null hypothesis and test. Formulating the null hypothesis is not automated (though the calculations of significance testing usually are). David Cox said, "How [the] translation from subject-matter problem to statistical model is done is often the most critical part of an analysis". A statistical significance test is intended to test a hypothesis. If the hypothesis summarizes a set of data, there is no value in testing the hypothesis on that set of data. Example: If a study of last year's weather reports indicates that rain in a region falls primarily on weekends, it is only valid to test that null hypothesis on weather reports from any other year. Testing hypotheses suggested by the data is circular reasoning that


id: null-hypothesis-20260505-4
file: data/en.wikipedia.org/wiki/Null_hypothesis-4.md
category: reference
title: Null hypothesis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Null_hypothesis
date_saved: 2026-05-05T09:50:56.623806+00:00
instance: kb-cron
parent: null-hypothesis-20260505-0
summary:
==== Discussion ==== Fisher said, "the null hypothesis must be exact, that is free of vagueness and ambiguity, because it must supply the basis of the 'problem of distribution,' of which the test of significance is the solution", implying a more restrictive domain for H0. According to this view, the null hypothesis must be numerically exact—it must state that a particular quantity or difference is equal to a particular number. In classical science, it is most typically the statement that there is no effect of a particular treatment; in observations, it is typically that there is no difference between the value of a particular measured variable and that of a prediction. Most statisticians believe that it is valid to state direction as a part of null hypothesis, or as part of a null hypothesis/alternative hypothesis pair. However, the results are not a full description of all the results of an experiment, merely a single result tailored to one particular purpose. For example, consider an H0 that claims the population mean for a new treatment is an improvement on a well-established treatment with population mean = 10 (known from long experience), with the one-tailed alternative being that the new treatment's mean


id: null-hypothesis-20260505-5
file: data/en.wikipedia.org/wiki/Null_hypothesis-5.md
category: reference
title: Null hypothesis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Null_hypothesis
date_saved: 2026-05-05T09:50:56.623806+00:00
instance: kb-cron
parent: null-hypothesis-20260505-0
summary:
Before 1925: There are occasional transient traces of statistical tests in past centuries, which were early examples of null hypotheses. In the late 19th century statistical significance was defined. In the early 20th century important probability distributions were defined. Gossett and Pearson worked on specific cases of significance testing. 1925: Fisher published the first edition of Statistical Methods for Research Workers, which defined the statistical significance test and made it a mainstream method of analysis for much of experimental science. The text was devoid of proofs and weak on explanations, but was filled with real examples. It placed statistical practice in the sciences well in advance of published statistical theory. 1933: In a series of papers (published over a decade starting in 1928) Neyman & Pearson defined the statistical hypothesis test as a proposed improvement on Fisher's test. The papers provided much of the terminology for statistical tests including alternative hypothesis and H0 as a hypothesis to be tested using observational data (with H1, H2... as alternatives). 1935: Fisher published the first edition of the book The Design of Experiments, which introduced the null hypothesis (by example rather than by definition) and carefully explained the rationale for significance tests in


id: null-result-20260505-0
file: data/en.wikipedia.org/wiki/Null_result-0.md
category: reference
title: Null result
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Null_result
date_saved: 2026-05-05T09:50:57.839258+00:00
instance: kb-cron
parent: null-result-20260505-0
summary:
In science, a null result is a result without the expected content: that is, the proposed result is absent. It is an experimental outcome which does not show an otherwise expected effect. This does not imply a result of zero or nothing, simply a result that does not support the hypothesis. In statistical hypothesis testing, a null result occurs when an experimental result is not significantly different from what is to be expected under the null hypothesis; its probability (under the null hypothesis) does not exceed the significance level, i.e., the threshold set prior to testing for rejection of the null hypothesis. The significance level varies, but common choices include 0.10, 0.05, and 0.01. However, a non-significant result does not necessarily mean that an effect is absent. As an example in physics, the results of the Michelson–Morley experiment were of this type, as it did not detect the expected velocity relative to the postulated luminiferous aether. This experiment's famous failed detection, commonly referred to as the null result, contributed to the development of special relativity. The experiment did appear to measure a non-zero "drift", but the value was far too small to account for the theoretically expected results; it is


id: nuremberg-code-20260505-0
file: data/en.wikipedia.org/wiki/Nuremberg_Code-0.md
category: reference
title: Nuremberg Code
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Nuremberg_Code
date_saved: 2026-05-05T09:50:59.021660+00:00
instance: kb-cron
parent: nuremberg-code-20260505-0
summary:
The Nuremberg Code (German: Nürnberger Kodex) is a set of ethical research principles for human experimentation created by the court in U.S. v Brandt, one of the Subsequent Nuremberg trials that were held after the Second World War. Though it was articulated as part of the court's verdict in the trial, the Code would later become significant beyond its original context; in a review written on the 50th anniversary of the Brandt verdict, Jay Katz writes that "a careful reading of the judgment suggests that [the authors] wrote the Code for the practice of human experimentation whenever it is being conducted." == Background == The origin of the Code began in pre–World War II German politics, particularly during the 1930s and 1940s. Starting in the mid-1920s, German physicians, usually proponents of racial hygiene, were accused by the public and the medical society of unethical medical practices. The use of racial hygiene was supported by the German government in order to promote an Aryan race. Racial hygiene extremists merged with National Socialism to promote the use of biology to accomplish their goals of racial purity, a core concept in the Nationalist ideology. Physicians were attracted to the scientific ideology and aided


id: nuremberg-code-20260505-1
file: data/en.wikipedia.org/wiki/Nuremberg_Code-1.md
category: reference
title: Nuremberg Code
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Nuremberg_Code
date_saved: 2026-05-05T09:50:59.021660+00:00
instance: kb-cron
parent: nuremberg-code-20260505-0
summary:
The voluntary consent of the human subject is absolutely essential. This means that the person involved should have legal capacity to give consent; should be so situated as to be able to exercise free power of choice, without the intervention of any element of force, fraud, deceit, duress, overreaching, or other ulterior form of constraint or coercion; and should have sufficient knowledge and comprehension of the elements of the subject matter involved as to enable him to make an understanding and enlightened decision. This latter element requires that before the acceptance of an affirmative decision by the experimental subject there should be made known to him the nature, duration, and purpose of the experiment; the method and means by which it is to be conducted; all inconveniences and hazards reasonably to be expected; and the effects upon his health or person which may possibly come from his participation in the experiment. The duty and responsibility for ascertaining the quality of the consent rests upon each individual who initiates, directs, or engages in the experiment. It is a personal duty and responsibility which may not be delegated to another with impunity. The experiment should be such as to yield fruitful results


id: o-brien-fleming-boundary-20260505-0
file: data/en.wikipedia.org/wiki/O'Brien–Fleming_boundary-0.md
category: reference
title: O'Brien–Fleming boundary
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/O'Brien–Fleming_boundary
date_saved: 2026-05-05T09:51:00.170438+00:00
instance: kb-cron
parent: o-brien-fleming-boundary-20260505-0
summary:
The O'Brien–Fleming (OBF) boundary is a set of statistical thresholds used in group-sequential clinical trial designs. It allows analyses (typically by a data monitoring committee) of accumulating data at several pre-specified interim points while still preserving the overall Type I error rate (the chance of a false-positive result) that was specified for the final analysis. The boundary is conservative at early interim analyses—requiring very strong evidence to stop a trial early—and becomes less stringent as the study progresses. At the first interim looks the rule requires a very high test statistic (or equivalently a very low p-value) to declare significance. This protects against premature stopping on random fluctuations. As the trial progresses, the required statistic becomes less extreme. By the final analysis the boundary is essentially the nominal significance level (e.g., 0.05). The method is a special case of the general parameterized method of Wang & Tsiatis. One disadvantage of the OBF boundary is that it requires prespecification of the number of interim analyses and the proportion of total information used at each analysis. More recent methods, such as the boundary method proposed by Lan and DeMets, allow dynamic allocation of the boundary values as the study progresses. Despite this


id: observational-study-20260505-0
file: data/en.wikipedia.org/wiki/Observational_study-0.md
category: reference
title: Observational study
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Observational_study
date_saved: 2026-05-05T09:51:01.318282+00:00
instance: kb-cron
parent: observational-study-20260505-0
summary:
In fields such as epidemiology, social sciences, psychology and statistics, an observational study draws conclusions without controlling the independent variable due to ethical or practical limitations. One common example studies the effect of a treatment, where the researcher does not assign subjects to treatment or control group. This is in contrast with experiments, such as randomized controlled trials, where each subject is randomly assigned to a treated group or a control group. Observational studies, for lacking an assignment mechanism, naturally present difficulties for inferential analysis. == Motivation == The independent variable may be beyond the control of the investigator for a variety of reasons: A randomized experiment would violate ethical standards. Suppose one wanted to investigate the abortion – breast cancer hypothesis, which postulates a causal link between induced abortion and the incidence of breast cancer. In a hypothetical controlled experiment, one would start with a large subject pool of pregnant women and divide them randomly into a treatment group (receiving induced abortions) and a control group (not receiving abortions), and then conduct regular cancer screenings for women from both groups. Such an experiment would violate ethical principles. (It would also suffer from various confounds and sources of bias, e.g.


id: observational-study-20260505-1
file: data/en.wikipedia.org/wiki/Observational_study-1.md
category: reference
title: Observational study
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Observational_study
date_saved: 2026-05-05T09:51:01.318282+00:00
instance: kb-cron
parent: observational-study-20260505-0
summary:
=== Omitted variable bias === An observer of an uncontrolled experiment (or process) records potential factors and the data output: the goal is to determine the effects of the factors. Sometimes the recorded factors may not be directly causing the differences in the output. There may be more important factors which were not recorded but are, in fact, causal. Also, recorded or unrecorded factors may be correlated which may yield incorrect conclusions. === Selection bias === Another difficulty with observational studies is that researchers may themselves be biased in their observational skills. This would allow for researchers to (either consciously or unconsciously) seek out the information they're looking for while conducting their research. For example, researchers may exaggerate the effect of one variable, or downplay the effect of another: researchers may even select in subjects that fit their conclusions. This selection bias can happen at any stage of the research process. This introduces bias into the data where certain variables are systematically incorrectly measured. == Quality == A 2014 (updated in 2024) Cochrane review concluded that observational studies produce results similar to those conducted as randomized controlled trials. The review reported little evidence for significant effect differences between observational studies


id: observer-expectancy-effect-20260505-0
file: data/en.wikipedia.org/wiki/Observer-expectancy_effect-0.md
category: reference
title: Observer-expectancy effect
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Observer-expectancy_effect
date_saved: 2026-05-05T09:51:02.509827+00:00
instance: kb-cron
parent: observer-expectancy-effect-20260505-0
summary:
The observer-expectancy effect is a form of reactivity in which a researcher's cognitive bias causes them to subconsciously influence the participants of an experiment. Confirmation bias can lead to the experimenter interpreting results incorrectly because of the tendency to look for information that affirms their hypothesis, and overlook information that conflicts with it. It is a significant threat to a study's internal validity, and is therefore typically controlled using a double-blind experimental design. The observer-expectancy effect is distinct from related phenomena such as the subject-expectancy effect and demand characteristics. In observer-expectancy effects, the researcher’s expectations influence participant behavior or data interpretation through subtle cues, whereas subject-expectancy effects arise from participants’ own beliefs about the study, and demand characteristics refer more broadly to situational cues that signal expected responses. It may include conscious or unconscious influences on subject behavior including creation of demand characteristics that influence subjects, and altered or selective recording of experimental results themselves. == Overview == The experimenter may introduce cognitive bias into a study in several ways‍—‍in the observer-expectancy effect, the experimenter may subtly communicate their expectations for the outcome of the study to the participants, causing them to alter their behavior to conform to those expectations.


id: one-factor-at-a-time-method-20260505-0
file: data/en.wikipedia.org/wiki/One-factor-at-a-time_method-0.md
category: reference
title: One-factor-at-a-time method
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/One-factor-at-a-time_method
date_saved: 2026-05-05T09:51:03.967480+00:00
instance: kb-cron
parent: one-factor-at-a-time-method-20260505-0
summary:
The one-factor-at-a-time method, also known as one-variable-at-a-time, OFAT, OF@T, OFaaT, OVAT, OV@T, OVaaT, or monothetic analysis is a method of designing experiments involving the testing of factors, or causes, one at a time instead of multiple factors simultaneously. == Advantages == OFAT is favored by non-experts, especially in situations where the data is cheap and abundant. There exist cases where the mental effort required to conduct a complex multi-factor analysis exceeds the effort required to acquire extra data, in which case OFAT might make sense. Furthermore, some researchers have shown that OFAT can be more effective than fractional factorials under certain conditions (number of runs is limited, primary goal is to attain improvements in the system, and experimental error is not large compared to factor effects, which must be additive and independent of each other). == Disadvantages == In contrast, in situations where data is precious and must be analyzed with care, it is almost always better to change multiple factors at once. A middle-school-level example illustrating this point is the family of balance puzzles, which includes the Twelve Coins puzzle. At the undergraduate level, one could compare Bevington's GRIDLS versus GRADLS. The latter is far from optimal, but the


id: optimal-experimental-design-20260505-0
file: data/en.wikipedia.org/wiki/Optimal_experimental_design-0.md
category: reference
title: Optimal experimental design
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Optimal_experimental_design
date_saved: 2026-05-05T09:51:06.413636+00:00
instance: kb-cron
parent: optimal-experimental-design-20260505-0
summary:
In the design of experiments, optimal experimental designs (or optimum designs) are a class of experimental designs that are optimal with respect to some statistical criterion. The creation of this field of statistics has been credited to Danish statistician Kirstine Smith. In the design of experiments for estimating statistical models, optimal designs allow parameters to be estimated without bias and with minimum variance. A non-optimal design requires a greater number of experimental runs to estimate the parameters with the same precision as an optimal design. In practical terms, optimal experiments can reduce the costs of experimentation. The optimality of a design depends on the statistical model and is assessed with respect to a statistical criterion, which is related to the variance-matrix of the estimator. Specifying an appropriate model and specifying a suitable criterion function both require understanding of statistical theory and practical knowledge with designing experiments. == Advantages == Optimal designs offer three advantages over sub-optimal experimental designs: Optimal designs reduce the costs of experimentation by allowing statistical models to be estimated with fewer experimental runs. Optimal designs can accommodate multiple types of factors, such as process, mixture, and discrete factors. Designs can be optimized when the design-space is constrained,


id: optimal-experimental-design-20260505-1
file: data/en.wikipedia.org/wiki/Optimal_experimental_design-1.md
category: reference
title: Optimal experimental design
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Optimal_experimental_design
date_saved: 2026-05-05T09:51:06.413636+00:00
instance: kb-cron
parent: optimal-experimental-design-20260505-0
summary:
==== Flexible optimality criteria and convex analysis ==== High-quality statistical software provide a combination of libraries of optimal designs or iterative methods for constructing approximately optimal designs, depending on the model specified and the optimality criterion. Users may use a standard optimality-criterion or may program a custom-made criterion. All of the traditional optimality-criteria are convex (or concave) functions, and therefore optimal-designs are amenable to the mathematical theory of convex analysis and their computation can use specialized methods of convex minimization. The practitioner need not select exactly one traditional, optimality-criterion, but can specify a custom criterion. In particular, the practitioner can specify a convex criterion using the maxima of convex optimality-criteria and nonnegative combinations of optimality criteria (since these operations preserve convex functions). For convex optimality criteria, the Kiefer-Wolfowitz equivalence theorem allows the practitioner to verify that a given design is globally optimal. The Kiefer-Wolfowitz equivalence theorem is related with the Legendre-Fenchel conjugacy for convex functions. If an optimality-criterion lacks convexity, then finding a global optimum and verifying its optimality often are difficult. === Model uncertainty and Bayesian approaches === ==== Model selection ==== When scientists wish to test several theories, then a statistician can design an experiment that allows optimal


id: optimal-experimental-design-20260505-2
file: data/en.wikipedia.org/wiki/Optimal_experimental_design-2.md
category: reference
title: Optimal experimental design
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Optimal_experimental_design
date_saved: 2026-05-05T09:51:06.413636+00:00
instance: kb-cron
parent: optimal-experimental-design-20260505-0
summary:
Logic will not undertake to inform you what kind of experiments you ought to make in order best to determine the acceleration of gravity, or the value of the Ohm; but it will tell you how to proceed to form a plan of experimentation. [....] Unfortunately practice generally precedes theory, and it is the usual fate of mankind to get things done in some boggling way first, and find out afterward how they could have been done much more easily and perfectly. Kirstine Smith proposed optimal designs for polynomial models in 1918. (Kirstine Smith had been a student of the Danish statistician Thorvald N. Thiele and was working with Karl Pearson in London.) == See also == == Notes == == References == Atkinson, A. C.; Donev, A. N.; Tobias, R. D. (2007). Optimum experimental designs, with SAS. Oxford University Press. pp. 511+xvi. ISBN 978-0-19-929660-6. Chernoff, Herman (1972). Sequential analysis and optimal design. Society for Industrial and Applied Mathematics. ISBN 978-0-89871-006-9. Fedorov, V. V. (1972). Theory of Optimal Experiments. Academic Press. Fedorov, Valerii V.; Hackl, Peter (1997). Model-Oriented Design of Experiments. Lecture Notes in Statistics. Vol. 125. Springer-Verlag. Goos, Peter (2002). The Optimal Design of Blocked and Split-plot Experiments. Lecture


id: orthogonal-array-20260505-0
file: data/en.wikipedia.org/wiki/Orthogonal_array-0.md
category: reference
title: Orthogonal array
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Orthogonal_array
date_saved: 2026-05-05T09:51:07.576803+00:00
instance: kb-cron
parent: orthogonal-array-20260505-0
summary:
In mathematics, an orthogonal array (more specifically, a fixed-level orthogonal array) is a table ("array") whose entries come from a fixed finite set of symbols (for example, {1,2,...,v}), arranged in such a way that there is an integer t so that for every selection of t columns of the table, all ordered t-tuples of the symbols, formed by taking the entries in each row restricted to these columns, appear the same number of times. The number t is called the strength of the orthogonal array. Here are two examples: The example at left is that of an orthogonal array with symbol set {1,2} and strength 2. Notice that the four ordered pairs (2-tuples) formed by the rows restricted to the first and third columns, namely (1,1), (2,1), (1,2) and (2,2), are all the possible ordered pairs of the two element set and each appears exactly once. The second and third columns would give, (1,1), (2,1), (2,2) and (1,2); again, all possible ordered pairs each appearing once. The same statement would hold had the first and second columns been used. This is thus an orthogonal array of strength two. In the example on the right, the rows restricted to the first


id: orthogonal-array-20260505-1
file: data/en.wikipedia.org/wiki/Orthogonal_array-1.md
category: reference
title: Orthogonal array
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Orthogonal_array
date_saved: 2026-05-05T09:51:07.576803+00:00
instance: kb-cron
parent: orthogonal-array-20260505-0
summary:
== Terminology and notation == The terms symmetric and asymmetric are sometimes used for fixed-level and mixed-level. Here symmetry refers to the property that all factors have the same number of levels, not to the "shape" of the array: a symmetric orthogonal array is almost never a symmetric matrix. The notation OA(N, k, v, t) is sometimes contracted so that one may, for example, write simply OA(k, v), as long as the text makes clear the unstated parameter values. In the other direction, it may be expanded for mixed-level arrays. Here one would write OA(N, k, v1···vk, t), where column i has vi levels. This notation is usually shortened when values v are repeated, so that one writes OA(8, 5, 2441, 2) for the example at the end of the last section, rather than OA(8, 5, 2·2·2·2·4, 2). In similar fashion, one may shorten OA(N, k, v, t) to OA(N, vk, t) for fixed-level arrays. This OA notation does not explicitly include the index λ, but λ can be recovered from the other parameters via the relation N = λvt. This is effective when the parameters all have specific numerical values, but less so when a class of orthogonal arrays


id: orthogonal-array-20260505-2
file: data/en.wikipedia.org/wiki/Orthogonal_array-2.md
category: reference
title: Orthogonal array
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Orthogonal_array
date_saved: 2026-05-05T09:51:07.576803+00:00
instance: kb-cron
parent: orthogonal-array-20260505-0
summary:
=== Latin cubes === In the statistics literature, a Latin cube is a three-dimensional n × n × n matrix consisting of n layers, each having n rows and n columns such that the n distinct elements which appear are repeated n2 times and arranged so that in each layer parallel to each of the three pairs of opposite faces of the cube all the n distinct elements appear and each is repeated exactly n times in that layer. Note that with this definition a layer of a Latin cube need not be a Latin square. In fact, no row, column or file (the cells of a particular position in the different layers) need be a permutation of the n symbols. A Latin cube of order n is equivalent to an OA(n3, 4 ,n, 2). Two Latin cubes of order n are orthogonal if, among the n3 pairs of elements chosen from corresponding cells of the two cubes, each distinct ordered pair of the elements occurs exactly n times. A set of k − 3 mutually orthogonal Latin cubes of order n is equivalent to an OA(n3, k, n, 2). An example of a pair of mutually orthogonal Latin cubes


id: orthogonal-array-20260505-3
file: data/en.wikipedia.org/wiki/Orthogonal_array-3.md
category: reference
title: Orthogonal array
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Orthogonal_array
date_saved: 2026-05-05T09:51:07.576803+00:00
instance: kb-cron
parent: orthogonal-array-20260505-0
summary:
A factorial experiment is a statistically structured experiment in which several factors (watering levels, antibiotics, fertilizers, etc.) are applied to each experimental unit at finitely many levels, which may be quantitative or qualitative. In a full factorial experiment all combinations of levels of the factors need to be tested. In a fractional factorial design only a subset of treatment combinations are used. An orthogonal array can be used to design a fractional factorial experiment. The columns represent the various factors and the entries are the levels at which the factors are observed. An experimental run is a row of the orthogonal array, that is, a specific combination of factor levels. The strength of the array determines the resolution of the fractional design. When using one of these designs, the treatment units and trial order should be randomized as much as the design allows. For example, one recommendation is that an appropriately sized orthogonal array be randomly selected from those available, and that the run order then be randomized. Mixed-level designs occur naturally in the statistical setting. === Quality control === Orthogonal arrays played a central role in the development of Taguchi methods by Genichi Taguchi, which took place during his


id: orthogonal-array-testing-20260505-0
file: data/en.wikipedia.org/wiki/Orthogonal_array_testing-0.md
category: reference
title: Orthogonal array testing
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Orthogonal_array_testing
date_saved: 2026-05-05T09:51:23.219077+00:00
instance: kb-cron
parent: orthogonal-array-testing-20260505-0
summary:
Orthogonal array testing is a systematic and statistically-driven black-box testing technique employed in the field of software testing. This method is particularly valuable in scenarios where the number of inputs to a system is substantial enough to make exhaustive testing impractical. == Overview == Orthogonal array testing works on the premise of selecting a subset of test cases from a large pool of potential inputs. This selection is based on statistical methods to ensure that the chosen subset represents the whole input space. As a result, serious bugs can be identified while the number of tests necessary to do so is greatly reduced. === Benefits === Reduced testing cycle time: By strategically selecting test cases, the testing process becomes more efficient, leading to time savings. Simplified analysis: The structured nature of orthogonal array testing makes analysis straightforward and less complex. Balanced test cases: This technique ensures that test cases are well-balanced, making it easier to isolate defects and assess performance. Cost savings: It offers a significant cost advantage over pair-wise testing, making it an economical choice for testing large-scale software systems. === Cons === Limited applicability: This technique is most effective when the number of inputs is relatively small. In


id: paradigm-experimental-20260505-0
file: data/en.wikipedia.org/wiki/Paradigm_(experimental)-0.md
category: reference
title: Paradigm (experimental)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Paradigm_(experimental)
date_saved: 2026-05-05T09:51:25.533610+00:00
instance: kb-cron
parent: paradigm-experimental-20260505-0
summary:
In the behavioural sciences (e.g. psychology, biology, neurosciences), an experimental paradigm is an experimental setup or way of conducting a certain type of experiment (a protocol) that is defined by certain fine-tuned standards, and often has a theoretical background. A paradigm in this technical sense, however, is not a way of thinking as it is in the epistemological meaning (paradigm). In the social sciences empiricist experimentation has independent [and dependent] variables and control conditions...What is the origin of the hypotheses which are studied? Given the basic design, the hypothesis and the particular conditions for the experiment, an experimental paradigm must be made up. The paradigm typically includes factors such as experimental instructions for the subjects, the physical design of the experiment room, and the rules for process of the trial or trials to be carried out. The more paradigms which are attempted, and the more variables within a single paradigm are attempted, with the same results, the more sure one is of the results, that, "the effect is a true one and not merely a product of artifacts engendered by the use of a particular paradigm." The three core factors of paradigm design may be considered: "(a) ...the 'nuts and


id: phases-of-clinical-research-20260505-0
file: data/en.wikipedia.org/wiki/Phases_of_clinical_research-0.md
category: reference
title: Phases of clinical research
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Phases_of_clinical_research
date_saved: 2026-05-05T09:51:26.696563+00:00
instance: kb-cron
parent: phases-of-clinical-research-20260505-0
summary:
The phases of clinical research are the stages in which scientists conduct experiments with a health intervention to obtain sufficient evidence for a process considered effective as a medical treatment. For drug development, the clinical phases start with testing for drug safety in a few human subjects, then expand to many study participants (potentially tens of thousands) to determine if the treatment is effective. Clinical research is conducted on drug candidates, vaccine candidates, new medical devices, and new diagnostic assays. == Description == Clinical trials testing potential medical products are commonly classified into four phases. The drug development process will normally proceed through all four phases over many years. When expressed specifically, a clinical trial phase is capitalized both in name and Roman numeral, such as "Phase I" clinical trial. If the drug successfully passes through Phases I, II, and III, it will usually be approved by the national regulatory authority for use in the general population. Phase IV trials are 'post-marketing' or 'surveillance' studies conducted to monitor safety over several years. == Preclinical studies == Before clinical trials are undertaken for a candidate drug, vaccine, medical device, or diagnostic assay, the product candidate is tested extensively in preclinical studies.


id: phases-of-clinical-research-20260505-1
file: data/en.wikipedia.org/wiki/Phases_of_clinical_research-1.md
category: reference
title: Phases of clinical research
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Phases_of_clinical_research
date_saved: 2026-05-05T09:51:26.696563+00:00
instance: kb-cron
parent: phases-of-clinical-research-20260505-0
summary:
=== Phase Ia === Single ascending dose (Phase Ia): In single ascending dose studies, small groups of subjects are given a single dose of the drug while they are observed and tested for a period of time to confirm safety. Typically, a small number of participants, usually three, are entered sequentially at a particular dose. If they do not exhibit any adverse side effects, and the pharmacokinetic data are roughly in line with predicted safe values, the dose is escalated, and a new group of subjects is then given a higher dose. If unacceptable toxicity is observed in any of the three participants, an additional number of participants, usually three, are treated at the same dose. This is continued until pre-calculated pharmacokinetic safety levels are reached, or intolerable side effects start showing up (at which point the drug is said to have reached the maximum tolerated dose (MTD)). If an additional unacceptable toxicity is observed, then the dose escalation is terminated and that dose, or perhaps the previous dose, is declared to be the maximally tolerated dose. This particular design assumes that the maximally tolerated dose occurs when approximately one-third of the participants experience unacceptable toxicity. Variations of this design


id: phases-of-clinical-research-20260505-2
file: data/en.wikipedia.org/wiki/Phases_of_clinical_research-2.md
category: reference
title: Phases of clinical research
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Phases_of_clinical_research
date_saved: 2026-05-05T09:51:26.696563+00:00
instance: kb-cron
parent: phases-of-clinical-research-20260505-0
summary:
=== Failure rate === Phase I trials historically have experienced the lowest success, having about a 66% failure rate due mainly to adverse effects and other toxicity concerns. A 2022 review found that about 90% of drug candidates fail over the course of Phases I-III, mainly due to absence of therapeutic efficacy, toxicity, non-specific drug properties, poor strategic planning, and recognition that the compound will not succeed commercially. == Phase III == This phase is designed to assess the effectiveness of the new intervention and, thereby, its value in clinical practice. Phase III studies are randomized controlled multicenter trials on large patient groups (300–3,000 or more depending upon the disease/medical condition studied) and are aimed at being the definitive assessment of how effective the drug is, in comparison with current 'gold standard' treatment. Because of their size and comparatively long duration, Phase III trials are the most expensive, time-consuming and difficult trials to design and run, especially in therapies for chronic medical conditions. Phase III trials of chronic conditions or diseases often have a short follow-up period for evaluation, relative to the period of time the intervention might be used in practice. It is common practice that certain Phase III


id: phases-of-clinical-research-20260505-3
file: data/en.wikipedia.org/wiki/Phases_of_clinical_research-3.md
category: reference
title: Phases of clinical research
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Phases_of_clinical_research
date_saved: 2026-05-05T09:51:26.696563+00:00
instance: kb-cron
parent: phases-of-clinical-research-20260505-0
summary:
== Phase IV == A Phase IV trial is also known as a postmarketing surveillance trial or drug monitoring trial to assure long-term safety and effectiveness of the drug, vaccine, device or diagnostic test. Phase IV trials involve the safety surveillance (pharmacovigilance) and ongoing technical support of a drug after it receives regulatory approval to be sold. Phase IV studies may be required by regulatory authorities or may be undertaken by the sponsoring company for competitive (finding a new market for the drug) or other reasons (for example, the drug may not have been tested for interactions with other drugs, or on certain population groups such as pregnant women, who are unlikely to subject themselves to trials). The safety surveillance is designed to detect any rare or long-term adverse effects over a much larger patient population and longer time period than was possible during the Phase I-III clinical trials. Harmful effects discovered by Phase IV trials may result in a drug being withdrawn from the market or restricted to certain uses; examples include cerivastatin (brand names Baycol and Lipobay), troglitazone (Rezulin) and rofecoxib (Vioxx). == Overall cost == The entire process of developing a drug from preclinical research to marketing


id: placebo-controlled-study-20260505-0
file: data/en.wikipedia.org/wiki/Placebo-controlled_study-0.md
category: reference
title: Placebo-controlled study
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Placebo-controlled_study
date_saved: 2026-05-05T09:51:27.963028+00:00
instance: kb-cron
parent: placebo-controlled-study-20260505-0
summary:
Placebo-controlled studies are a way of testing a medical therapy in which, in addition to a group of subjects that receives the treatment to be evaluated, a separate control group receives a sham "placebo" treatment which is specifically designed to have no real effect. Placebos are most commonly used in blinded trials, where subjects do not know whether they are receiving real or placebo treatment. Often, there is also a further "natural history" group that does not receive any treatment at all. The purpose of the placebo group is to account for the placebo effect, that is, effects from treatment that do not depend on the treatment itself. Such factors include knowing one is receiving a treatment, attention from health care professionals, and the expectations of a treatment's effectiveness by those running the research study. Without a placebo group to compare against, it is not possible to know whether the treatment itself had any effect. Patients frequently show improvement even when given a sham or "fake" treatment. Such intentionally inert placebo treatments can take many forms, such as a pill containing only sugar, or a medical device (such as an ultrasound machine) that is not actually turned on. Also, due


id: placebo-controlled-study-20260505-1
file: data/en.wikipedia.org/wiki/Placebo-controlled_study-1.md
category: reference
title: Placebo-controlled study
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Placebo-controlled_study
date_saved: 2026-05-05T09:51:27.963028+00:00
instance: kb-cron
parent: placebo-controlled-study-20260505-0
summary:
the degree to which there is a considerably higher level of "placebo response" than one would expect is an index of the degree to which the drug's active ingredient is not efficacious. the degree to which there is a considerably lower level of "placebo response" than one would expect is an index of the degree to which, in some particular way, the placebo is not simulating the active drug in an appropriate way. However, in particular cases such as the use of Cimetidine to treat ulcers, a significant level of placebo response can also prove to be an index of how much the treatment has been directed at a wrong target. == Implementation issues == === Adherence === The Coronary Drug Project was intended to study the safety and effectiveness of drugs for long-term treatment of coronary heart disease in men. Those in the placebo group who adhered to the placebo treatment (took the placebo regularly as instructed) showed nearly half the mortality rate as those who were not adherent. A similar study of women similarly found survival was nearly 2.5 times greater for those who adhered to their placebo. This apparent placebo effect may have occurred because: Adhering to


id: placebo-controlled-study-20260505-2
file: data/en.wikipedia.org/wiki/Placebo-controlled_study-2.md
category: reference
title: Placebo-controlled study
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Placebo-controlled_study
date_saved: 2026-05-05T09:51:27.963028+00:00
instance: kb-cron
parent: placebo-controlled-study-20260505-0
summary:
=== Jellinek and headache remedy ingredients === Jellinek in 1946 was asked to test whether or not the headache drug's overall efficacy would be reduced if certain ingredients were removed. In post-World War II 1946, pharmaceutical chemicals were restricted, and one U.S. headache remedy manufacturer sold a drug composed of three ingredients: a, b, and c, and chemical b was in particular short supply. Jellinek set up a complex trial involving 199 subjects, all of whom had "frequent headaches". The subjects were randomly divided into four test groups. He prepared four test drugs, involving various permutations of the three drug constituents, with a placebo as a scientific control. The structure of this trial is significant because, in those days, the only time placebos were ever used "was to express the efficacy or non-efficacy of a drug in terms of "how much better" the drug was than the "placebo". (Note that the trial conducted by Austin Flint is an example of such a drug efficacy vs. placebo efficacy trial.) The four test drugs were identical in shape, size, colour and taste: Drug A: contained a, b, and c. Drug B: contained a and c. Drug C: contained a and b. Drug


id: placebo-controlled-study-20260505-3
file: data/en.wikipedia.org/wiki/Placebo-controlled_study-3.md
category: reference
title: Placebo-controlled study
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Placebo-controlled_study
date_saved: 2026-05-05T09:51:27.963028+00:00
instance: kb-cron
parent: placebo-controlled-study-20260505-0
summary:
== Declaration of Helsinki == From the time of the Hippocratic Oath questions of the ethics of medical practice have been widely discussed, and codes of practice have been gradually developed as a response to advances in scientific medicine. The Nuremberg Code, which was issued in August 1947, as a consequence of the so-called Doctors' Trial which examined the human experimentation conducted by Nazi doctors during World War II, offers ten principles for legitimate medical research, including informed consent, absence of coercion, and beneficence towards experiment participants. In 1964, the World Medical Association issued the Declaration of Helsinki, which specifically limited its directives to health research by physicians, and emphasized a number of additional conditions in circumstances where "medical research is combined with medical care". The significant difference between the 1947 Nuremberg Code and the 1964 Declaration of Helsinki is that the first was a set of principles that was suggested to the medical profession by the "Doctors' Trial" judges, whilst the second was imposed by the medical profession upon itself. Paragraph 29 of the Declaration makes specific mention of placebos: 29. The benefits, risks, burdens and effectiveness of a new method should be tested against those of the best


id: plackett-burman-design-20260505-0
file: data/en.wikipedia.org/wiki/Plackett–Burman_design-0.md
category: reference
title: Plackett–Burman design
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Plackett–Burman_design
date_saved: 2026-05-05T09:51:29.193177+00:00
instance: kb-cron
parent: plackett-burman-design-20260505-0
summary:
Plackett–Burman designs are experimental designs presented in 1946 by Robin L. Plackett and J. P. Burman while working in the British Ministry of Supply. Their goal was to find experimental designs for investigating the dependence of some measured quantity on a number of independent variables (factors), each taking L levels, in such a way as to minimize the variance of the estimates of these dependencies using a limited number of experiments. Interactions between the factors were considered negligible. The solution to this problem is to find an experimental design where each combination of levels for any pair of factors appears the same number of times, throughout all the experimental runs (refer to table). A complete factorial design would satisfy this criterion, but the idea was to find smaller designs. For the case of two levels (L = 2), Plackett and Burman used the method found in 1933 by Raymond Paley for generating orthogonal matrices whose elements are all either 1 or −1 (Hadamard matrices). Paley's method could be used to find such matrices of size N for most N equal to a multiple of 4. In particular, it worked for all such N up to 100 except N = 92.


id: plackett-burman-design-20260505-1
file: data/en.wikipedia.org/wiki/Plackett–Burman_design-1.md
category: reference
title: Plackett–Burman design
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Plackett–Burman_design
date_saved: 2026-05-05T09:51:29.193177+00:00
instance: kb-cron
parent: plackett-burman-design-20260505-0
summary:
== 4 to 48 runs, sorted to show half-fractions == P.B.4 + + + + – – – + – – – + P.B.8 + + + + + + + + + – – – – + + – + + – – – + – – – + + – – + + – + – – – + – + – + – – – + – – + + – – – + + – + P.B.12 + + + + + + + + + + + + + + + – – – + – – – + + – – – + – – + – + + – + – + + + – – – – + – – + – – + – + + – + – – – + – – + – + + – + + – – – + – – + + – + – + + + – – – + – – + – – + – + + + – – – – + + + – – – + – + – – + – – + – + +


id: plackett-burman-design-20260505-2
file: data/en.wikipedia.org/wiki/Plackett–Burman_design-2.md
category: reference
title: Plackett–Burman design
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Plackett–Burman_design
date_saved: 2026-05-05T09:51:29.193177+00:00
instance: kb-cron
parent: plackett-burman-design-20260505-0
summary:
== References == This article incorporates public domain material from the National Institute of Standards and Technology


id: pocock-boundary-20260505-0
file: data/en.wikipedia.org/wiki/Pocock_boundary-0.md
category: reference
title: Pocock boundary
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Pocock_boundary
date_saved: 2026-05-05T09:51:30.364390+00:00
instance: kb-cron
parent: pocock-boundary-20260505-0
summary:
The Pocock boundary is a method for determining whether to stop a clinical trial prematurely. The typical clinical trial compares two groups of patients. One group are given a placebo or conventional treatment, while the other group of patients are given the treatment that is being tested. The investigators running the clinical trial will wish to stop the trial early for ethical reasons if the treatment group clearly shows evidence of benefit. In other words, "when early results proved so promising it was no longer fair to keep patients on the older drugs for comparison, without giving them the opportunity to change." The concept was introduced by the medical statistician Stuart Pocock in 1977. The many reasons underlying when to stop a clinical trial for benefit were discussed in his editorial from 2005. == Details == The Pocock boundary gives a p-value threshold for each interim analysis which guides the data monitoring committee on whether to stop the trial. The boundary used depends on the number of interim analyses. The Pocock boundary is simple to use in that the p-value threshold is the same at each interim analysis. The disadvantages are that the number of interim analyses must be fixed


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: academic-torrents-20260505-0
file: data/en.wikipedia.org/wiki/Academic_Torrents-0.md
category: reference
title: Academic Torrents
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Academic_Torrents
date_saved: 2026-05-05T09:52:40.258053+00:00
instance: kb-cron
parent: academic-torrents-20260505-0
summary:
Academic Torrents is a website which enables the sharing of research data using the BitTorrent protocol. The site was founded in November 2013, and is a project of the Institute for Reproducible Research (a 501(c)3 U.S. non-profit corporation). The project is said to be similar to LOCKSS but with a focus on "offering researchers the opportunity to distribute the hosting of their papers and datasets to authors and readers, providing easy access to scholarly works and simultaneously backing them up on computers around the world." == Notable datasets == === Developing Human Connectome Project === The developing Human Connectome Project related to the Human Connectome Project uses the platform. "Researchers from three leading British institutions are using BitTorrent to share over 150 GB of unique high-resolution brain scans of unborn babies with colleagues worldwide... The researchers opted to go for the Academic Torrents tracker, which specializes in sharing research data" === CrossRef metadata === The site hosts public metadata releases from Crossref which contain over 120+ million metadata records for scholarly work, each with a DOI. This was done so to allow the community to work with the entire database programmatically instead of using their API. "The sheer number of


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: american-institute-of-biological-sciences-20260505-0
file: data/en.wikipedia.org/wiki/American_Institute_of_Biological_Sciences-0.md
category: reference
title: American Institute of Biological Sciences
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/American_Institute_of_Biological_Sciences
date_saved: 2026-05-05T09:52:50.014686+00:00
instance: kb-cron
parent: american-institute-of-biological-sciences-20260505-0
summary:
The American Institute of Biological Sciences (AIBS) is a nonprofit scientific public charitable organization. The organization's mission is to promote the use of science to inform decision-making and advance biology for the benefit of science and society. == Overview == AIBS serves as a society of societies. It has 98 member organizations and is headquartered in Herndon, VA. Its staff work to achieve its mission by publishing the peer-reviewed journal BioScience, providing peer review and advisory support services for funding organizations, providing professional development for scientists and students, advocating for science policy and educating the public about biology. AIBS works with like-minded organizations, funding agencies, and nonprofit and for-profit entities to promote the use of science to inform decision-making. AIBS is governed by an esteemed Board of Directors and a Council of representatives of its member organizations. == Background and history == AIBS was established in 1947 as a part of the National Academy of Sciences. The overarching goal was to unify the individuals and organizations that collectively represent the biological sciences, so that the community could address matters of common concern. In the 1950s, AIBS became an independent, member-governed, nonprofit 501(c)3 public charity scientific organization. In 1962, the National


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


