Methoden vergleichen

	Zwei-Wege-Varianzanalyse (Two-Way ANOVA)	Varianzanalyse mit Kovariaten (ANCOVA)	Kruskal-Wallis H-Test	Einfaktorielle Varianzanalyse
Fachgebiet	Statistik	Statistik	Statistik	Statistik
Familie	Hypothesis test	Hypothesis test	Hypothesis test	Hypothesis test
Entstehungsjahr	1925	1932	1952	1925
Urheber	Ronald A. Fisher	Ronald A. Fisher	William Kruskal & W. Allen Wallis	Ronald A. Fisher
Typ	Parametric factorial mean comparison	Parametric group comparison with covariate control	Nonparametric group comparison	Parametric mean comparison
Wegweisende Quelle	Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley. ISBN: 978-1119113478	Tabachnick, B.G. & Fidell, L.S. (2013). Using Multivariate Statistics (6th ed.). Pearson. ISBN: 978-0205849574	Kruskal, W. H. & Wallis, W. A. (1952). Use of ranks in one-criterion variance analysis. Journal of the American Statistical Association, 47(260), 583–621.	Fisher, R. A. (1925). Statistical Methods for Research Workers. Edinburgh: Oliver and Boyd.
Aliasnamen	factorial ANOVA, two-factor ANOVA, İki Yönlü ANOVA	analysis of covariance, covariance analysis, ANCOVA (Kovaryans Analizi)	Kruskal-Wallis H test, one-way ANOVA on ranks, Kruskal-Wallis one-way analysis of variance, Kruskal-Wallis Testi	one-factor ANOVA, single-factor ANOVA, analysis of variance, tek yönlü ANOVA
Verwandt	6	4	5	4
Zusammenfassung	Two-Way ANOVA is a parametric hypothesis test that simultaneously examines the main effects of two independent categorical factors and their interaction effect on a single continuous dependent variable. The technique was developed within the broader framework of the analysis of variance established by Ronald A. Fisher in 1925 and remains the standard approach whenever an experiment or survey includes exactly two between-subjects factors.	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).	The Kruskal-Wallis H test is a nonparametric hypothesis test that compares three or more independent groups to decide whether their distributions (typically their medians) differ. Introduced by William Kruskal and W. Allen Wallis in 1952, it works on ranks rather than raw values and is the distribution-free counterpart to one-way ANOVA.	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.