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The 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]
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.
Wilcoxon rank-sum charts based on the Wilcoxon rank-sum test [3] - used to monitor location parameter of a process; Control charts based on precedence or excedance statistic; Shewhart-Lepage chart based on the Lepage test [4] - used to monitor both location and scale parameters of a process simultaneously in a single chart
In statistics, a ranklet is an orientation-selective non-parametric feature which is based on the computation of Mann–Whitney–Wilcoxon (MWW) rank-sum test statistics. [1] Ranklets achieve similar response to Haar wavelets as they share the same pattern of orientation-selectivity, multi-scale nature and a suitable notion of completeness. [2]
In statistics, a rank test is any test involving ranks. ... Wilcoxon signed-rank test; ... L.A. (2013). Permutation and Rank Tests. In: Essential Statistical ...
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
In statistics, one purpose for the analysis of variance (ANOVA) is to analyze differences in means between groups. The test statistic, F, assumes independence of observations, homogeneous variances, and population normality. ANOVA on ranks is a statistic designed for situations when the normality assumption has been violated.