Methoden vergleichen

	Robuster Friedman-Test	Robuster Kruskal-Wallis-Test
Fachgebiet	Statistik	Statistik
Familie	Hypothesis test	Hypothesis test
Entstehungsjahr	1990s–2000s	1952 (base); robust variants 1990s–2000s
Urheber	Extension of Friedman (1937); robust variants developed by Wilcox and colleagues	Kruskal & Wallis (1952); robust extensions by Wilcox and others
Typ	Robust nonparametric repeated measures comparison	Nonparametric robust rank-based test
Wegweisende Quelle	Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Academic Press. ISBN: 978-0123869838	Mielke, P. W., & Berry, K. J. (2007). Permutation Methods: A Distance Function Approach (2nd ed.). Springer. ISBN: 978-0387698137
Aliasnamen	robust rank-based repeated measures test, trimmed-mean Friedman test, Friedman test with robust estimation, Fried-type robust test	robust K-W test, trimmed Kruskal-Wallis, robust nonparametric one-way test, robust rank-based ANOVA
Verwandt	6	3
Zusammenfassung	The robust Friedman test is a nonparametric procedure for comparing three or more related (within-subjects) conditions that replaces standard ranking or mean-based summaries with robust location estimates — typically trimmed means or Winsorized statistics — to reduce the influence of outliers and heavy-tailed distributions on the inference.	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.