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| 베이즈 독립표본 t-검정× | 독립 표본 t-검정× | |
|---|---|---|
| 분야 | 통계학 | 통계학 |
| 계열 | Hypothesis test | Hypothesis test |
| 기원 연도≠ | 2009 (modern form); 1961 (Jeffreys prior framework) | 1908 |
| 창시자≠ | Harold Jeffreys (foundational); operationalized by Rouder et al. | Student (W. S. Gosset) |
| 유형≠ | Bayesian hypothesis test | Parametric mean comparison |
| 원전≠ | 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 ↗ | Student (W. S. Gosset) (1908). The probable error of a mean. Biometrika, 6(1), 1–25. DOI ↗ |
| 별칭 | Bayesian two-sample t-test, Bayes factor t-test, JZS t-test, Bayesian unpaired t-test | two-sample t-test, unpaired t-test, Student t-test, independent groups t-test |
| 관련≠ | 3 | 4 |
| 요약≠ | 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. | The independent samples t-test is a parametric hypothesis test that determines whether the means of two independent, unrelated groups differ significantly on a continuous outcome variable. Derived from Gosset's 1908 t-distribution, it is one of the most widely used inferential tests in social, behavioral, biomedical, and experimental sciences. |
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