Salīdzināt metodes

	Robustas jaudas analīze	Robustā neatkarīgo paraugu t-kriterijs
Nozare	Statistika	Statistika
Saime	Hypothesis test	Hypothesis test
Izcelsmes gads	1990s–2000s	1974–1990s
Autors	Rand R. Wilcox and colleagues	Rand R. Wilcox; Karen K. Yuen (trimmed-mean form)
Tips	Power and sample-size planning	Robust parametric mean comparison
Pirmavots	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
Citi nosaukumi	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
Saistītās	4	2
Kopsavilkums	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.