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Mann–Whitney test (also called the Mann–Whitney–Wilcoxon (MWW/MWU), Wilcoxon rank-sum test, or Wilcoxon–Mann–Whitney test) is a nonparametric statistical 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.
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]
The Kruskal–Wallis test' by ranks, Kruskal–Wallis test (named after William Kruskal and W. Allen Wallis), or one-way ANOVA on ranks is a non-parametric statistical test for testing whether samples originate from the same distribution. [ 1 ][ 2 ][ 3 ] It is used for comparing two or more independent samples of equal or different sample sizes.
Over his career Wilcoxon published over 70 papers. [3] His most well-known paper [4] contained the two new statistical tests that still bear his name, the Wilcoxon rank-sum test and the Wilcoxon signed-rank test. These are non-parametric alternatives to the unpaired and paired Student's t-tests respectively. He died on November 18, 1965.
In some cases, the observations for all subjects can be assigned a rank value (1, 2, 3, ...). If the observations can be ranked, and each observation in a pair is a random sample from a symmetric distribution, then the Wilcoxon signed-rank test is appropriate. The Wilcoxon test will generally have greater power to detect differences than the ...
A rank correlation coefficient measures the degree of similarity between two rankings, and can be used to assess the significance of the relation between them. For example, two common nonparametric methods of significance that use rank correlation are the Mann–Whitney U test and the Wilcoxon signed-rank test.
To test the difference between groups for significance a Wilcoxon rank sum test is used, which also justifies the notation W A and W B in calculating the rank sums. From the rank sums the U statistics are calculated by subtracting off the minimum possible score, n(n + 1)/2 for each group: [1] U A = 54 − 7(8)/2 = 26 U B = 37 − 6(7)/2 = 16
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