Comparer des méthodes

	Test de Kruskal-Wallis robuste	Robust Mann-Whitney U test
Domaine	Statistique	Statistique
Famille	Hypothesis test	Hypothesis test
Année d'origine	1952 (base); robust variants 1990s–2000s	1947 / 2003
Auteur d'origine	Kruskal & Wallis (1952); robust extensions by Wilcox and others	Rand Wilcox (robust extensions); original test by Mann & Whitney (1947)
Type	Nonparametric robust rank-based test	Robust nonparametric two-group comparison
Source fondatrice	Mielke, P. W., & Berry, K. J. (2007). Permutation Methods: A Distance Function Approach (2nd ed.). Springer. ISBN: 978-0387698137	Wilcox, R. R. (2005). Introduction to Robust Estimation and Hypothesis Testing (2nd ed.). Academic Press. ISBN: 978-0127515427
Alias	robust K-W test, trimmed Kruskal-Wallis, robust nonparametric one-way test, robust rank-based ANOVA	robust Wilcoxon rank-sum test, robust two-sample rank test, outlier-resistant Mann-Whitney test, robust nonparametric two-group comparison
Apparentées	3	1
Résumé	The robust Kruskal-Wallis test is a nonparametric, rank-based method for comparing three or more independent groups when data contain outliers, heavy tails, or heterogeneous spread. It augments the classical Kruskal-Wallis H statistic with robust techniques — such as trimmed means on ranks or permutation-based inference — to maintain valid Type I error rates even when distributional assumptions are violated.	The Robust Mann-Whitney U test is a nonparametric two-group comparison that combines the rank-based logic of the classic Mann-Whitney U test with modern robust techniques — such as outlier screening, trimmed means, or robust variance estimation — to produce reliable inferences when data contain extreme values, heavy-tailed distributions, or other violations that compromise the standard test.