Comparer des méthodes

	Test de Kruskal-Wallis robuste	Test de Friedman
Domaine	Statistique	Statistique
Famille	Hypothesis test	Hypothesis test
Année d'origine	1952 (base); robust variants 1990s–2000s	1937
Auteur d'origine	Kruskal & Wallis (1952); robust extensions by Wilcox and others	Milton Friedman
Type	Nonparametric robust rank-based test	Nonparametric repeated-measures comparison (by ranks)
Source fondatrice	Mielke, P. W., & Berry, K. J. (2007). Permutation Methods: A Distance Function Approach (2nd ed.). Springer. ISBN: 978-0387698137	Friedman, M. (1937). The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association, 32(200), 675–701.
Alias	robust K-W test, trimmed Kruskal-Wallis, robust nonparametric one-way test, robust rank-based ANOVA	Friedman two-way analysis of variance by ranks, Friedman rank test, Friedman Testi
Apparentées	3	2
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 Friedman test is a nonparametric hypothesis test that compares three or more related conditions measured on the same blocks or subjects, serving as the rank-based alternative to repeated-measures ANOVA. It was introduced by Milton Friedman in 1937 and works on ordinal or continuous data without assuming normality.