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| 贝叶斯确认性研究× | 功效分析× | |
|---|---|---|
| 领域≠ | 研究设计 | 统计学 |
| 方法族≠ | Process / pipeline | Hypothesis test |
| 起源年份≠ | 1961 (Jeffreys); 2009–2018 (contemporary confirmatory formulation) | 1969 (1st ed.); 1988 (seminal 2nd ed.) |
| 提出者≠ | Harold Jeffreys (theoretical foundation); Jeffrey Rouder, Eric-Jan Wagenmakers (applied confirmatory framework) | Jacob Cohen |
| 类型≠ | Quantitative hypothesis-testing framework | Sample size and power planning |
| 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 ↗ | Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 978-0805802832 |
| 别名 | Bayesian hypothesis testing, confirmatory Bayesian analysis, Bayes factor hypothesis testing, BCR | sample size calculation, power calculation, sensitivity analysis, a priori power analysis |
| 相关≠ | 1 | 5 |
| 摘要≠ | Bayesian confirmatory research is a quantitative framework that tests pre-specified hypotheses by computing the Bayes factor — a ratio expressing how much more likely the observed data are under one hypothesis than another. Unlike classical null-hypothesis significance testing (NHST), it provides direct evidence for both the alternative and the null hypothesis, supports optional stopping rules under certain conditions, and updates prior beliefs with observed data through Bayes' theorem. | Power analysis is a planning and evaluation technique that quantifies the probability of detecting a real effect of a given magnitude at a chosen significance level. It links four quantities — sample size, effect size, significance level (alpha), and statistical power (1 minus beta) — so that researchers can determine the sample size needed before data collection or evaluate the sensitivity of a completed study. |
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