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완전 임의 배치법 (CRD)×전요인 실험 설계×Kruskal-Wallis H 검정×일원 분산 분석×
분야실험설계실험설계통계학통계학
계열Hypothesis testHypothesis testHypothesis testHypothesis test
기원 연도1935192619521925
창시자R. A. FisherR. A. FisherWilliam Kruskal & W. Allen WallisRonald A. Fisher
유형Parametric group comparison via one-way ANOVAParametric factorial experimentNonparametric group comparisonParametric mean comparison
원전Montgomery, D.C. (2017). Design and Analysis of Experiments. Wiley. ISBN: 978-1119320937Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley. ISBN: 978-0471718130Kruskal, W. H. & Wallis, W. A. (1952). Use of ranks in one-criterion variance analysis. Journal of the American Statistical Association, 47(260), 583–621. DOI ↗Fisher, R. A. (1925). Statistical Methods for Research Workers. Edinburgh: Oliver and Boyd. link ↗
별칭CRD, 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 Testione-factor ANOVA, single-factor ANOVA, analysis of variance, tek yönlü ANOVA
관련3554
요약The 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.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.
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ScholarGate방법 비교: Completely Randomized Design · Full Factorial Design · Kruskal-Wallis test · One-way ANOVA. 2026-06-19에 다음에서 검색함: https://scholargate.app/ko/compare