Linganisha mbinu

	Kipimo cha T² cha Hotelling	T-test kwa Sampuli Huru	Uchanganuzi wa Kigezo-Rejeshi wa Vigezo-Rejeshi Nyingi (MANCOVA)
Nyanja	Takwimu	Takwimu	Takwimu
Familia	Hypothesis test	Hypothesis test	Hypothesis test
Mwaka wa asili	1931	1908	1970
Mwanzilishi	Harold Hotelling	Student (W. S. Gosset)	Extension of MANOVA and ANCOVA traditions; consolidated in multivariate textbooks by the 1970s–1980s
Aina	Multivariate parametric mean comparison	Parametric mean comparison	Parametric multivariate mean comparison with covariate control
Chanzo asilia	Hotelling, H. (1931). The Generalization of Student's Ratio. Annals of Mathematical Statistics, 2(3), 360–378.	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
Majina mbadala	Hotelling T² Testi — Çok Değişkenli t-Testi, multivariate t-test, Hotelling T-squared	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
Zinazohusiana	6	4	5
Muhtasari	Hotelling's T² test is a multivariate parametric hypothesis test that simultaneously compares the mean vectors of two independent groups across multiple continuous outcome variables. It was introduced by Harold Hotelling in 1931 as the direct multivariate generalization of Student's t-test, replacing the scalar mean difference with a vector difference scaled by the pooled variance-covariance matrix.	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).