قارن الطرق

	تحليل التغاير (ANCOVA)	تحليل التباين أحادي الاتجاه	اختبار ت ของ ولش (تباينات غير متساوية)
المجال	الإحصاء	الإحصاء	الإحصاء
العائلة	Hypothesis test	Hypothesis test	Hypothesis test
سنة النشأة	1932	1925	1947
صاحب الطريقة	Ronald A. Fisher	Ronald A. Fisher	B. L. Welch
النوع	Parametric group comparison with covariate control	Parametric mean comparison	Parametric mean comparison (unequal variances)
المصدر التأسيسي	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.	Welch, B. L. (1947). The generalization of Student's problem when several different population variances are involved. Biometrika, 34(1/2), 28–35.
الأسماء البديلة	analysis of covariance, covariance analysis, ANCOVA (Kovaryans Analizi)	one-factor ANOVA, single-factor ANOVA, analysis of variance, tek yönlü ANOVA	unequal variances t-test, Welch-Satterthwaite t-test, Welch t-Testi (Eşit Olmayan Varyans)
ذات صلة	4	4	4
الملخص	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.	Welch's t-test is a parametric hypothesis test that compares the means of two independent groups without assuming their variances are equal. It was introduced by B. L. Welch in 1947 as a more robust generalization of Student's two-sample test for situations where the two groups have different spread.