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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 ...
The one-sample version serves a purpose similar to that of the one-sample Student's t-test. [2] 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 ...
The table shown on the right can be used in a two-sample t-test to estimate the sample sizes of an experimental group and a control group that are of equal size, that is, the total number of individuals in the trial is twice that of the number given, and the desired significance level is 0.05. [4] The parameters used are:
The program provides methods that are appropriate for matched and independent t-tests, [2] survival analysis, [5] matched [6] and unmatched [7] [8] studies of dichotomous events, the Mantel-Haenszel test, [9] and linear regression. [3] The program can generate graphs of the relationships between power, sample size and the detectable alternative ...
In statistics, Welch's t-test, or unequal variances t-test, is a two-sample location test which is used to test the (null) hypothesis that two populations have equal means. It is named for its creator, Bernard Lewis Welch , and is an adaptation of Student's t -test , [ 1 ] and is more reliable when the two samples have unequal variances and ...
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.
Given an r-sample statistic, one can create an n-sample statistic by something similar to bootstrapping (taking the average of the statistic over all subsamples of size r). This procedure is known to have certain good properties and the result is a U-statistic. The sample mean and sample variance are of this form, for r = 1 and r = 2.
When there are only two means to compare, the t-test and the F-test are equivalent; the relation between ANOVA and t is given by F = t 2. An extension of one-way ANOVA is two-way analysis of variance that examines the influence of two different categorical independent variables on one dependent variable.