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Statistical tests are used to test the fit between a hypothesis and the data. [1] [2] Choosing the right statistical test is not a trivial task. [1] The choice of the test depends on many properties of the research question. The vast majority of studies can be addressed by 30 of the 100 or so statistical tests in use. [3] [4] [5]
A test method is a method for a test in science or engineering, such as a physical test, chemical test, or statistical test. It is a definitive procedure that produces a test result. [ 1 ] In order to ensure accurate and relevant test results, a test method should be "explicit, unambiguous, and experimentally feasible.", [ 2 ] as well as ...
Bowker's test of symmetry; Categorical distribution, general model; Chi-squared test; Cochran–Armitage test for trend; Cochran–Mantel–Haenszel statistics; Correspondence analysis; Cronbach's alpha; Diagnostic odds ratio; G-test; Generalized estimating equations; Generalized linear models; Krichevsky–Trofimov estimator; Kuder ...
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 ...
Some common post hoc tests include: [6] [7] Holm-Bonferroni Procedure; Newman-Keuls; Rodger’s Method; Scheffé’s Method; Tukey’s Test (see also: Studentized Range Distribution) However, with the exception of Scheffès Method, these tests should be specified "a priori" despite being called "post-hoc" in conventional usage.
Process tracing is a qualitative research method used to develop and test theories. [1] [2] [3] Process-tracing can be defined as the following: it is the systematic examination of diagnostic evidence selected and analyzed in light of research questions and hypotheses posed by the investigator (Collier, 2011). Process-tracing thus focuses on ...
Permutation tests can be understood as surrogate data testing where the surrogate data under the null hypothesis are obtained through permutations of the original data. [1] In other words, the method by which treatments are allocated to subjects in an experimental design is mirrored in the analysis of that design.
A/B testing (also known as bucket testing, split-run testing, or split testing) is a user experience research method. [1] A/B tests consist of a randomized experiment that usually involves two variants (A and B), [ 2 ] [ 3 ] [ 4 ] although the concept can be also extended to multiple variants of the same variable.