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Unpaired samples are also called independent samples. Paired samples are also called dependent. Finally, there are some statistical tests that perform analysis of relationship between multiple variables like regression. [1] Number of samples: The number of samples of data. Exactness: A test can be exact or be asymptotic delivering approximate ...
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[27] [29] [30] The nonparametric counterpart to the paired samples t-test is the Wilcoxon signed-rank test for paired samples. For a discussion on choosing between the t-test and nonparametric alternatives, see Lumley, et al. (2002). [19] One-way analysis of variance (ANOVA) generalizes the two-sample t-test when the data belong to more than ...
"A/B testing" is a shorthand for a simple randomized controlled experiment, in which a number of samples (e.g. A and B) of a single vector-variable are compared. [1] A/B tests are widely considered the simplest form of controlled experiment, especially when they only involve two variants.
Template:Test case nowiki, for templates with complex invocations; Template:Collapsible test case, to collapse test cases when the main and sandbox templates produce the same result; Note that all of these templates can produce collapsible test cases, but Template:Collapsible test case has this feature turned on by default. For detailed ...
A paired difference test is designed for situations where there is dependence between pairs of measurements (in which case a test designed for comparing two independent samples would not be appropriate). That applies in a within-subjects study design, i.e., in a study where the same set of subjects undergo both of the conditions being compared.
For two matched samples, it is a paired difference test like the paired Student's t-test (also known as the "t-test for matched pairs" or "t-test for dependent samples"). The Wilcoxon test is a good alternative to the t-test when the normal distribution of the differences between paired individuals cannot be assumed. Instead, it assumes a ...
If set to "y" or "yes", the test case is made collapsible. The test case is collapsed and given a green heading if all the template outputs are the same. If any of the template outputs differ, the test case is expanded and given a yellow heading. See #Collapsible test cases for other parameters which only work when _collapsible is enabled.