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| ベイズ的フィッシャーの正確確率検定× | ベイズ独立標本t検定× | |
|---|---|---|
| 分野 | 統計学 | 統計学 |
| 系統 | Hypothesis test | Hypothesis test |
| 提唱年≠ | 1974 (Bayesian form); 1935 (Fisher's exact test) | 2009 (modern form); 1961 (Jeffreys prior framework) |
| 提唱者≠ | Gunel & Dickey (Bayesian form); R. A. Fisher (classical exact test) | Harold Jeffreys (foundational); operationalized by Rouder et al. |
| 種類≠ | Bayesian hypothesis test for independence | Bayesian hypothesis test |
| 原典≠ | Gunel, E., & Dickey, J. (1974). Bayes factors for independence in contingency tables. Biometrika, 61(3), 545–557. DOI ↗ | Rouder, J. N., Speckman, P. L., Sun, D., Morey, R. D., & Iverson, G. (2009). Bayesian t tests for accepting and rejecting the null hypothesis. Psychonomic Bulletin & Review, 16(2), 225–237. DOI ↗ |
| 別名 | Bayesian exact test for independence, Bayesian contingency table test, Bayes factor Fisher test, BFexact | Bayesian two-sample t-test, Bayes factor t-test, JZS t-test, Bayesian unpaired t-test |
| 関連≠ | 4 | 3 |
| 概要≠ | The Bayesian Fisher's exact test evaluates independence between two categorical variables in a 2x2 table by computing a Bayes factor rather than a p-value. Using conjugate priors on cell probabilities — most commonly the Gunel-Dickey framework — it quantifies how much the observed data favor an association model over an independence model, providing a continuous scale of evidence in both directions. | The Bayesian independent samples t-test quantifies evidence for or against a mean difference between two independent groups using a Bayes factor rather than a p-value. Rooted in Jeffreys's probability framework and popularized by Rouder et al. (2009), it places a Cauchy prior on the standardized effect size and returns continuous evidence for both the null and alternative hypotheses. |
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