השוואת שיטות

	ניתוח שונות משותפת (ANCOVA)	ניתוח שונות חד-כיווני	מבחן t לדגימות מזווגות
תחום	סטטיסטיקה	סטטיסטיקה	סטטיסטיקה
משפחה	Hypothesis test	Hypothesis test	Hypothesis test
שנת המקור	1932	1925	1908
הוגה השיטה	Ronald A. Fisher	Ronald A. Fisher	Student (W. S. Gosset)
סוג	Parametric group comparison with covariate control	Parametric mean comparison	Parametric mean comparison (paired)
מקור מכונן	Tabachnick, B.G. & Fidell, L.S. (2013). Using Multivariate Statistics (6th ed.). Pearson. ISBN: 978-0205849574	Fisher, R. A. (1925). Statistical Methods for Research Workers. Edinburgh: Oliver and Boyd.	Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics (4th ed.). SAGE. ISBN: 978-1446249185
כינויים	analysis of covariance, covariance analysis, ANCOVA (Kovaryans Analizi)	one-factor ANOVA, single-factor ANOVA, analysis of variance, tek yönlü ANOVA	dependent samples t-test, repeated measures t-test, matched-pairs t-test, eşleştirilmiş örneklem t-testi
קשורות	4	4	4
תקציר	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).	One-way ANOVA is a parametric hypothesis test that compares the means of three or more independent groups on a single continuous outcome to decide whether at least one group mean differs. It rests on the variance-partitioning framework introduced by Ronald A. Fisher in 1925.	The paired samples t-test is a parametric hypothesis test that compares two measurements taken on the same subjects — such as a before and after reading — to decide whether the average change differs from zero. It rests on the t-distribution introduced by Student (W. S. Gosset) in 1908 and works on the within-subject difference scores rather than the raw measurements.