Comparer des méthodes

	Test de Friedman	Test par permutation (ou randomisation)	ANOVA à mesures répétées
Domaine	Statistique	Statistique	Statistique
Famille	Hypothesis test	Regression model	Hypothesis test
Année d'origine	1937	2005	1992
Auteur d'origine	Milton Friedman	Good (2005); Edgington & Onghena (2007); resampling tradition	Girden (textbook treatment); Field (2013)
Type	Nonparametric repeated-measures comparison (by ranks)	Nonparametric resampling test	Parametric within-subjects mean comparison
Source fondatrice	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.	Good, P. (2005). Permutation, Parametric and Bootstrap Tests of Hypotheses (3rd ed.). Springer. ISBN: 978-0387202792	Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics (4th ed., Ch. 14). SAGE. ISBN: 978-1446249185
Alias	Friedman two-way analysis of variance by ranks, Friedman rank test, Friedman Testi	randomization test, exact permutation test, re-randomization test, Permütasyon Testi	within-subjects ANOVA, repeated measures analysis of variance, rm-ANOVA, Tekrarlı Ölçüm ANOVA
Apparentées	2	5	4
Résumé	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.	The permutation test is a nonparametric resampling procedure that builds the sampling distribution of a test statistic directly from the data by repeatedly shuffling the group labels. Developed in the resampling tradition and treated systematically by Good (2005) and Edgington & Onghena (2007), it requires no parametric distributional assumption and yields an exact p-value.	Repeated-measures ANOVA is a parametric hypothesis test that compares three or more measurements taken from the same individuals — typically across time points or conditions — to decide whether their means differ. It extends one-way ANOVA to within-subjects designs, as treated in standard references such as Girden (1992) and Field (2013).