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| Analisi Bayesiana delle Tabelle di Contingenza× | Test t di Bayes per campioni indipendenti× | |
|---|---|---|
| Campo | Statistica | Statistica |
| Famiglia | Hypothesis test | Hypothesis test |
| Anno di origine≠ | 1974 | 2009 (modern form); 1961 (Jeffreys prior framework) |
| Ideatore≠ | Gunel & Dickey | Harold Jeffreys (foundational); operationalized by Rouder et al. |
| Tipo≠ | Bayesian association test | Bayesian hypothesis test |
| Fonte seminale≠ | 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 ↗ |
| Alias | Bayesian chi-square test, Bayesian contingency table test, Bayes factor association test, Bayesian crosstab analysis | Bayesian two-sample t-test, Bayes factor t-test, JZS t-test, Bayesian unpaired t-test |
| Correlati≠ | 4 | 3 |
| Sintesi≠ | Bayesian cross-tabulation analysis tests whether two categorical variables are associated by computing a Bayes factor that quantifies the evidence for an association model against an independence model. Unlike classical chi-square testing, it provides a continuous measure of evidence, supports the null hypothesis directly, and updates naturally with prior knowledge about the cell probabilities. | 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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