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Robuste Power-Analyse×Robuster t-Test für unabhängige Stichproben×
FachgebietStatistikStatistik
FamilieHypothesis testHypothesis test
Entstehungsjahr1990s–2000s1974–1990s
UrheberRand R. Wilcox and colleaguesRand R. Wilcox; Karen K. Yuen (trimmed-mean form)
TypPower and sample-size planningRobust parametric mean comparison
Wegweisende QuelleLuh, 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. link ↗Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Academic Press. ISBN: 978-0123869838
Aliasnamenpower analysis under non-normality, distribution-free power analysis, robust sample-size determination, contamination-robust powerYuen's t-test, trimmed-mean t-test, Winsorized t-test, robust two-sample test
Verwandt42
ZusammenfassungRobust 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.
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ScholarGateMethoden vergleichen: Robust power analysis · Robust independent samples t-test. Abgerufen am 2026-06-18 von https://scholargate.app/de/compare