방법 비교

	완전 임의 배치법 (CRD)	전요인 실험 설계	Kruskal-Wallis H 검정
분야	실험설계	실험설계	통계학
계열	Hypothesis test	Hypothesis test	Hypothesis test
기원 연도	1935	1926	1952
창시자	R. A. Fisher	R. A. Fisher	William Kruskal & W. Allen Wallis
유형	Parametric group comparison via one-way ANOVA	Parametric factorial experiment	Nonparametric group comparison
원전	Montgomery, D.C. (2017). Design and Analysis of Experiments. Wiley. ISBN: 978-1119320937	Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley. ISBN: 978-0471718130	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.
별칭	CRD, completely randomised design, one-way experimental design, Tam Tesadüf Deneme Deseni (CRD)	factorial experiment, 2^k factorial, full factorial, Faktöriyel Deneme Deseni (Full Factorial, 2^k)	Kruskal-Wallis H test, one-way ANOVA on ranks, Kruskal-Wallis one-way analysis of variance, Kruskal-Wallis Testi
관련	3	5	5
요약	The completely randomized design is the most fundamental experimental design, in which experimental units are assigned to treatments entirely at random with no restrictions. Analysed by one-way ANOVA, it was formalised by R. A. Fisher in the 1930s and remains the reference starting point for experimental research whenever the experimental material is homogeneous and nuisance variation is absent or negligible.	A full factorial design is a parametric experimental method in which every combination of factor levels is tested simultaneously, enabling the estimation of all main effects and all interaction effects in a single study. Rooted in R. A. Fisher's foundational work on designed experiments (1926) and systematically developed by Box, Hunter, and Hunter (2005) and Montgomery (2017), the 2^k form tests k two-level factors across 2^k experimental runs and is the benchmark against which all other factorial designs are measured.	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.