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	Niezależny test t dla prób niezależnych	Wielowymiarowa analiza kowariancji (MANCOVA)	Wielowymiarowa analiza wariancji (MANOVA)
Dziedzina	Statystyka	Statystyka	Statystyka
Rodzina	Hypothesis test	Hypothesis test	Hypothesis test
Rok powstania	1908	1970	1932
Twórca	Student (W. S. Gosset)	Extension of MANOVA and ANCOVA traditions; consolidated in multivariate textbooks by the 1970s–1980s	Samuel Stanley Wilks (Wilks' Lambda, 1932); Roy, Hotelling, Pillai (mid-20th c.)
Typ	Parametric mean comparison	Parametric multivariate mean comparison with covariate control	Parametric multivariate mean comparison
Źródło pierwotne	Student (1908). The probable error of a mean. Biometrika, 6(1), 1–25.	Tabachnick, B. G. & Fidell, L. S. (2019). Using Multivariate Statistics (7th ed.). Pearson. ISBN: 978-0134790541	Tabachnick, B.G. & Fidell, L.S. (2013). Using Multivariate Statistics (6th ed.). Pearson. ISBN: 978-0205849574
Inne nazwy	student t-test, two-sample t-test, unpaired t-test, bağımsız örneklem t-testi	MANCOVA, multivariate ANCOVA, MANOVA with covariates, MANCOVA — Çok Değişkenli Kovaryans Analizi	Multivariate ANOVA, Çok Değişkenli ANOVA (MANOVA)
Pokrewne	4	5	5
Podsumowanie	The independent samples t-test is a parametric hypothesis test that compares the means of two independent groups to decide whether they differ significantly. It builds on the t-distribution introduced by Student (W. S. Gosset) in 1908 and assumes the measured values are continuous, approximately normally distributed, and have equal variances.	MANCOVA (Multivariate Analysis of Covariance) is a parametric hypothesis test that simultaneously compares two or more groups on multiple continuous dependent variables while statistically controlling for one or more covariates. It extends MANOVA by incorporating covariate adjustment, a tradition consolidated in multivariate statistical methodology by the 1970s and authoritatively documented by Tabachnick and Fidell (2019).	MANOVA is a parametric hypothesis test that simultaneously compares group means across multiple continuous dependent variables, controlling the inflation of Type I error that would result from running separate ANOVAs. Key multivariate test statistics — Wilks' Lambda, Pillai's Trace, Hotelling-Lawley Trace, and Roy's Greatest Root — were developed between the 1930s and 1950s, with Wilks' Lambda formalised by Samuel Stanley Wilks in 1932.