方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 贝叶斯独立样本t检验× | 贝叶斯单因素方差分析× | |
|---|---|---|
| 领域 | 统计学 | 统计学 |
| 方法族 | Hypothesis test | Hypothesis test |
| 起源年份≠ | 2009 (modern form); 1961 (Jeffreys prior framework) | 1961 (foundations); 2012 (ANOVA Bayes factors) |
| 提出者≠ | Harold Jeffreys (foundational); operationalized by Rouder et al. | Harold Jeffreys (foundations); Jeffrey Rouder et al. (default priors for ANOVA) |
| 类型 | Bayesian hypothesis test | Bayesian hypothesis test |
| 开创性文献≠ | 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 ↗ | 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 ↗ |
| 别名 | Bayesian two-sample t-test, Bayes factor t-test, JZS t-test, Bayesian unpaired t-test | Bayesian ANOVA, BF ANOVA, Bayes factor one-way ANOVA, Bayesian F-test |
| 相关 | 3 | 3 |
| 摘要≠ | 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. | 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. |
| ScholarGate数据集 ↗ |
|
|