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The Wilcoxon signed-rank test is a non-parametric rank test for statistical hypothesis testing used either to test the location of a population based on a sample of data, or to compare the locations of two populations using two matched samples. [1] The one-sample version serves a purpose similar to that of the one-sample Student's t -test. [2]
Nonparametric statistics is a type of statistical analysis that makes minimal assumptions about the underlying distribution of the data being studied. Often these models are infinite-dimensional, rather than finite dimensional, as is parametric statistics. [1] Nonparametric statistics can be used for descriptive statistics or statistical inference.
The Wald–Wolfowitz runs test (or simply runs test ), named after statisticians Abraham Wald and Jacob Wolfowitz is a non-parametric statistical test that checks a randomness hypothesis for a two-valued data sequence. More precisely, it can be used to test the hypothesis that the elements of the sequence are mutually independent .
The Kruskal–Wallis test by ranks, Kruskal–Wallis test [1] (named after William Kruskal and W. Allen Wallis ), or one-way ANOVA on ranks [1] is a non-parametric method for testing whether samples originate from the same distribution. [2] [3] [4] It is used for comparing two or more independent samples of equal or different sample sizes.
The Friedman test is a non-parametric statistical test developed by Milton Friedman. [1] [2] [3] Similar to the parametric repeated measures ANOVA, it is used to detect differences in treatments across multiple test attempts. The procedure involves ranking each row (or block) together, then considering the values of ranks by columns.
Mann–Whitney test (also called the Mann–Whitney–Wilcoxon ( MWW/MWU ), Wilcoxon rank-sum test, or Wilcoxon–Mann–Whitney test) is a nonparametric test of the null hypothesis that, for randomly selected values X and Y from two populations, the probability of X being greater than Y is equal to the probability of Y being greater than X .
Appearance. hide. In statistics, the Siegel–Tukey test, named after Sidney Siegel and John Tukey, is a non-parametric test which may be applied to data measured at least on an ordinal scale. It tests for differences in scale between two groups. The test is used to determine if one of two groups of data tends to have more widely dispersed ...
Cochran's test is a non-parametric statistical test to verify whether k treatments have identical effects in the analysis of two-way randomized block designs where the response variable is binary. [1] [2] [3] It is named after William Gemmell Cochran. Cochran's Q test should not be confused with Cochran's C test, which is a variance outlier test.