Porównaj metody

	Test Kołmogorowa-Smirnowa dla dwóch prób	Test Levene’a i Browna-Forsythe’a na równość wariancji	Test permutacyjny (randomizacyjny)
Dziedzina	Statystyka	Statystyka	Statystyka
Rodzina	Regression model	Regression model	Regression model
Rok powstania	1948	1960	2005
Twórca	N. V. Smirnov	Howard Levene; Morton B. Brown and Alan B. Forsythe	Good (2005); Edgington & Onghena (2007); resampling tradition
Typ	Nonparametric two-sample distribution test	Homogeneity of variance test (robust)	Nonparametric resampling test
Źródło pierwotne	Smirnov, N. V. (1948). Table for Estimating the Goodness of Fit of Empirical Distributions. Annals of Mathematical Statistics, 19(2), 279-281.	Levene, H. (1960). Robust Tests for Equality of Variances. In Contributions to Probability and Statistics: Essays in Honor of Harold Hotelling. Stanford University Press.	Good, P. (2005). Permutation, Parametric and Bootstrap Tests of Hypotheses (3rd ed.). Springer. ISBN: 978-0387202792
Inne nazwy	KS two-sample test, two-sample KS test, İki Örneklem Kolmogorov-Smirnov Testi	Levene test, Brown-Forsythe test, homogeneity of variance test, Levene ve Brown-Forsythe Varyans Testi	randomization test, exact permutation test, re-randomization test, Permütasyon Testi
Pokrewne	3	5	5
Podsumowanie	The two-sample Kolmogorov-Smirnov test is a nonparametric procedure that asks whether two independent groups are drawn from the same continuous distribution. Building on Smirnov's 1948 tables, it compares the empirical cumulative distribution functions (CDFs) of the two samples and uses their maximum absolute distance as the test statistic.	The Levene and Brown-Forsythe test checks whether two or more groups share the same variance (homogeneity of variance). Levene (1960) built the test on absolute deviations from each group mean, and Brown and Forsythe (1974) made it robust to non-normal data by centring on the group median instead.	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.