The Wilcoxon-Mann-Whitney (WMW) test is used for assessing whether two samples of observations come from the same distribution, and given certain assumptions, have the same median. In many situations, this test has important advantages -. It is valid for either ordinal or measurement variables, including derived variables. The Wilcoxon-Mann-Whitney test requires that two distributions are symmetrical. No, it doesn't require symmetry of both distributions. (What makes you think this is necessary?) It requires exchangeability of the ranks under H0 (and not under H1); the most typical way to get that would be if the two distributions had the same shape when H0 is true.They don't have to have the same shape when its The Mann-Whitney U-test is used to test whether two independent samples of observations are drawn from the same or identical distributions. An advantage with this test is that the two samples under consideration do not necessarily need to have the same number of observations or instances. The Mann-Whitney U test is a nonparametric statistical significance test for determining whether two independent samples were drawn from a population with the same distribution. The test was named for Henry Mann and Donald Whitney, although it is sometimes called the Wilcoxon-Mann-Whitney test, also named for Frank Wilcoxon, who also developed I'm not an expert, but I believe that the Mann-Whitney (aka, Wilcoxon-Mann-Whitney or just Wilcoxon) test is generally used as an alternative to a t test when the data are not normally distributed.The Mann-Whitney test is commonly regarded as a test of population medians, but this is technically only true if the two populations have the same shape and one is a "translation" (or shift) of the The Wilcoxon-Mann-Whitney makes no distributional assumptions and so could always be applied. It has the advantage of providing exceptionally good robustness; for instance, if there were to be a few extremely bad points in your data (e.g., individuals with 10000+ diseases) these wouldn't heavily influence the Wilcoxon-Mann-Whitney test, whereas even one of these would destroy the t-test. vCOD3.