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| 베이지안 일원 분산 분석× | 베이즈 요인 분산 분석× | |
|---|---|---|
| 분야 | 통계학 | 통계학 |
| 계열 | Hypothesis test | Hypothesis test |
| 기원 연도≠ | 1961 (foundations); 2012 (ANOVA Bayes factors) | 1961 (foundations); 2012 (default Bayes factor formulation) |
| 창시자≠ | Harold Jeffreys (foundations); Jeffrey Rouder et al. (default priors for ANOVA) | Harold Jeffreys (foundational); modern default-prior form by Jeffrey N. Rouder et al. |
| 유형 | Bayesian hypothesis test | Bayesian hypothesis test |
| 원전 | Rouder, J. N., Morey, R. D., Speckman, P. L., & Province, J. M. (2012). Default Bayes factors for ANOVA designs. Journal of Mathematical Psychology, 56(5), 356–374. DOI ↗ | Rouder, J. N., Morey, R. D., Speckman, P. L., & Province, J. M. (2012). Default Bayes factors for ANOVA designs. Journal of Mathematical Psychology, 56(5), 356–374. DOI ↗ |
| 별칭 | Bayesian ANOVA, BF ANOVA, Bayes factor one-way ANOVA, Bayesian F-test | Bayesian factorial ANOVA, Bayes factor two-way ANOVA, Bayesian 2×k ANOVA, Bayesian two-factor ANOVA |
| 관련≠ | 3 | 4 |
| 요약≠ | Bayesian one-way ANOVA tests whether the means of three or more independent groups differ by computing a Bayes factor — a ratio that quantifies how much more likely the data are under a model that allows group differences than under the null model that assumes equal means. Unlike the classical F-test, it provides direct evidence for or against the null hypothesis rather than merely rejecting or retaining it. | Bayesian two-way ANOVA extends the classical two-way analysis of variance by replacing p-values with Bayes factors and posterior distributions. It quantifies evidence for or against main effects and their interaction using prior-weighted model comparison, yielding conclusions that are directly interpretable in probabilistic terms rather than relying on a fixed significance threshold. |
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