方法对比
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| 贝叶斯假设检验研究× | 假设检验研究× | |
|---|---|---|
| 领域 | 研究设计 | 研究设计 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1935–1961 (Jeffreys); extended by Kass & Raftery 1995, Wagenmakers 2007–2010 | Early 20th century (Fisher 1925; Neyman–Pearson 1933) |
| 提出者≠ | Harold Jeffreys (formal Bayes factor framework) | Karl Pearson, Ronald A. Fisher, Jerzy Neyman, Egon Pearson |
| 类型≠ | Quantitative research design | Quantitative confirmatory research design |
| 开创性文献≠ | Jeffreys, H. (1961). Theory of Probability (3rd ed.). Oxford University Press. ISBN: 978-0198503682 | Kerlinger, F. N., & Lee, H. B. (1986). Foundations of Behavioral Research (3rd ed.). Holt, Rinehart and Winston. ISBN: 978-0030417603 |
| 别名 | Bayesian significance testing, Bayes factor hypothesis testing, BHT research, Bayesian inference testing | hypothetico-deductive research, confirmatory quantitative research, null hypothesis significance testing, NHST design |
| 相关≠ | 5 | 4 |
| 摘要≠ | Bayesian hypothesis testing research is a quantitative design in which competing hypotheses are evaluated by updating prior beliefs with observed data to produce posterior probabilities and Bayes factors. Unlike frequentist null-hypothesis significance testing, it quantifies the relative evidence for each hypothesis, supports optional stopping, and allows accumulation of evidence across studies without inflating Type I error rates. | Hypothesis testing research is a quantitative design in which the investigator derives one or more explicit, falsifiable propositions from theory, translates them into a null hypothesis (H0) and an alternative hypothesis (H1), collects empirical data, and then applies an inferential statistical test to decide whether the evidence is sufficient to reject H0. The approach is the dominant paradigm for confirmatory science across the social, behavioral, health, and natural sciences. |
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