Comparer des méthodes

	Analyse de variance à deux facteurs (ANOVA à deux facteurs)	Test H de Kruskal-Wallis	Analyse de variance à un facteur
Domaine	Statistique	Statistique	Statistique
Famille	Hypothesis test	Hypothesis test	Hypothesis test
Année d'origine	1925	1952	1925
Auteur d'origine	Ronald A. Fisher	William Kruskal & W. Allen Wallis	Ronald A. Fisher
Type	Parametric factorial mean comparison	Nonparametric group comparison	Parametric mean comparison
Source fondatrice	Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley. ISBN: 978-1119113478	Kruskal, W. H. & Wallis, W. A. (1952). Use of ranks in one-criterion variance analysis. Journal of the American Statistical Association, 47(260), 583–621.	Fisher, R. A. (1925). Statistical Methods for Research Workers. Edinburgh: Oliver and Boyd.
Alias	factorial ANOVA, two-factor ANOVA, İki Yönlü ANOVA	Kruskal-Wallis H test, one-way ANOVA on ranks, Kruskal-Wallis one-way analysis of variance, Kruskal-Wallis Testi	one-factor ANOVA, single-factor ANOVA, analysis of variance, tek yönlü ANOVA
Apparentées	6	5	4
Résumé	Two-Way ANOVA is a parametric hypothesis test that simultaneously examines the main effects of two independent categorical factors and their interaction effect on a single continuous dependent variable. The technique was developed within the broader framework of the analysis of variance established by Ronald A. Fisher in 1925 and remains the standard approach whenever an experiment or survey includes exactly two between-subjects factors.	The Kruskal-Wallis H test is a nonparametric hypothesis test that compares three or more independent groups to decide whether their distributions (typically their medians) differ. Introduced by William Kruskal and W. Allen Wallis in 1952, it works on ranks rather than raw values and is the distribution-free counterpart to one-way ANOVA.	One-way ANOVA is a parametric hypothesis test that compares the means of three or more independent groups on a single continuous outcome to decide whether at least one group mean differs. It rests on the variance-partitioning framework introduced by Ronald A. Fisher in 1925.