방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 베이지안 확증 연구× | 검정력 분석× | |
|---|---|---|
| 분야≠ | 연구설계 | 통계학 |
| 계열≠ | Process / pipeline | Hypothesis test |
| 기원 연도≠ | 1961 (Jeffreys); 2009–2018 (contemporary confirmatory formulation) | 1969 (1st ed.); 1988 (seminal 2nd ed.) |
| 창시자≠ | Harold Jeffreys (theoretical foundation); Jeffrey Rouder, Eric-Jan Wagenmakers (applied confirmatory framework) | Jacob Cohen |
| 유형≠ | Quantitative hypothesis-testing framework | Sample size and power planning |
| 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 ↗ | Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 978-0805802832 |
| 별칭 | Bayesian hypothesis testing, confirmatory Bayesian analysis, Bayes factor hypothesis testing, BCR | sample size calculation, power calculation, sensitivity analysis, a priori power analysis |
| 관련≠ | 1 | 5 |
| 요약≠ | 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. |
| ScholarGate데이터셋 ↗ |
|
|