title: [Do we always correctly interpret the results of statistical nonparametric tests].
abstract: Mann-Whitney, Wilcoxon, Kruskal-Wallis and Friedman tests create a group of commonly used tests to analyze the results of clinical and laboratory data. These tests are considered to be extremely flexible and their asymptotic relative efficiency exceeds 95 percent. Compared with the corresponding parametric tests they do not require checking the fulfillment of the conditions such as the normality of data distribution, homogeneity of variance, the lack of correlation means and standard deviations, etc. They can be used both in the interval and or-dinal scales. The article presents an example Mann-Whitney test, that does not in any case the choice of these four nonparametric tests treated as a kind of gold standard leads to correct inference.
authors: Jerzy A
authors: JA
authors: Moczko
mesh_terms: D003627:Data Interpretation, Statistical; D012107:Research Design; D018709:Statistics, Nonparametric