Comparer des méthodes

	Analyse de puissance robuste	Test t robuste pour échantillons indépendants
Domaine	Statistique	Statistique
Famille	Hypothesis test	Hypothesis test
Année d'origine	1990s–2000s	1974–1990s
Auteur d'origine	Rand R. Wilcox and colleagues	Rand R. Wilcox; Karen K. Yuen (trimmed-mean form)
Type	Power and sample-size planning	Robust parametric mean comparison
Source fondatrice	Luh, W.-M., & Guo, J.-H. (2010). Approximate sample size formulas for the two-sample trimmed mean test with unequal variances. British Journal of Mathematical and Statistical Psychology, 63(1), 83–100.	Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Academic Press. ISBN: 978-0123869838
Alias	power analysis under non-normality, distribution-free power analysis, robust sample-size determination, contamination-robust power	Yuen's t-test, trimmed-mean t-test, Winsorized t-test, robust two-sample test
Apparentées	4	2
Résumé	Robust power analysis computes the statistical power or required sample size for hypothesis tests that use robust estimators — such as trimmed means or Winsorized variances — instead of ordinary means and standard deviations. It protects against inflated or deflated power estimates that arise when data contain outliers, heavy tails, or skewness that violate classical normality assumptions.	The robust independent samples t-test compares the central tendency of two independent groups using trimmed means and Winsorized variances, making it substantially less sensitive to outliers and non-normality than the classical Student or Welch t-test. The most widely used form is Yuen's test, which also accommodates unequal variances across groups.