Comparer des méthodes

	ANCOVA Robuste	Analyse de Covariance (ANCOVA)
Domaine	Statistique	Statistique
Famille	Hypothesis test	Hypothesis test
Année d'origine	1990s–2000s	1932
Auteur d'origine	Rand R. Wilcox and colleagues	Ronald A. Fisher
Type	Robust parametric covariate-adjusted comparison	Parametric group comparison with covariate control
Source fondatrice	Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Academic Press. ISBN: 978-0123869838	Tabachnick, B.G. & Fidell, L.S. (2013). Using Multivariate Statistics (6th ed.). Pearson. ISBN: 978-0205849574
Alias	robust ANCOVA, heteroscedastic ANCOVA, trimmed-mean ANCOVA, resistant ANCOVA	analysis of covariance, covariance analysis, ANCOVA (Kovaryans Analizi)
Apparentées	4	4
Résumé	Robust ANCOVA is a covariate-adjusted group comparison that replaces classical ANCOVA's ordinary least squares estimation with resistant methods — typically trimmed means or M-estimators — so that the test retains valid Type I error control and reasonable power when data contain outliers, heavy-tailed distributions, or heteroscedastic errors.	ANCOVA is a parametric hypothesis test that compares the adjusted means of two or more independent groups while statistically controlling for one or more continuous covariates. By removing the portion of outcome variance explained by the covariate, ANCOVA increases statistical precision and produces fairer group comparisons. The method builds on the general linear model framework consolidated by Fisher in the early 1930s and is described comprehensively by Tabachnick and Fidell (2013).