Porovnať metódy

	Analýza kovariancie (ANCOVA)	Diskrimačná analýza	Nezávislý t-test pre dva výberové súbory
Odbor	Štatistika	Štatistika	Štatistika
Rodina	Hypothesis test	Latent structure	Hypothesis test
Rok vzniku	1932	1936	1908
Tvorca	Ronald A. Fisher	Ronald A. Fisher	Student (W. S. Gosset)
Typ	Parametric group comparison with covariate control	Supervised classification and dimension reduction	Parametric mean comparison
Pôvodný zdroj	Tabachnick, B.G. & Fidell, L.S. (2013). Using Multivariate Statistics (6th ed.). Pearson. ISBN: 978-0205849574	Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179–188.	Student (1908). The probable error of a mean. Biometrika, 6(1), 1–25.
Ďalšie názvy	analysis of covariance, covariance analysis, ANCOVA (Kovaryans Analizi)	LDA, Fisher discriminant analysis, discriminant function analysis, canonical discriminant analysis	student t-test, two-sample t-test, unpaired t-test, bağımsız örneklem t-testi
Príbuzné	4	4	4
Zhrnutie	ANCOVA is a parametric hypothesis test that compares the adjusted means of two or more independent groups while statistically controlling for one or more continuous covariates. By removing the portion of outcome variance explained by the covariate, ANCOVA increases statistical precision and produces fairer group comparisons. The method builds on the general linear model framework consolidated by Fisher in the early 1930s and is described comprehensively by Tabachnick and Fidell (2013).	Discriminant analysis finds linear combinations of predictor variables that best separate two or more known groups. It is used both to understand which predictors distinguish the groups and to classify new observations into those groups with minimum error.	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.