Σύγκριση μεθόδων

	Ανάλυση Συνδιακύμανσης (ANCOVA)	Διακριτική Ανάλυση	Ανεξάρτητος δειγματικός t-έλεγχος
Πεδίο	Στατιστική	Στατιστική	Στατιστική
Οικογένεια	Hypothesis test	Latent structure	Hypothesis test
Έτος προέλευσης	1932	1936	1908
Δημιουργός	Ronald A. Fisher	Ronald A. Fisher	Student (W. S. Gosset)
Τύπος	Parametric group comparison with covariate control	Supervised classification and dimension reduction	Parametric mean comparison
Θεμελιώδης πηγή	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.
Εναλλακτικές ονομασίες	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
Συναφείς	4	4	4
Σύνοψη	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.