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| 검정력 분석× | 베이지안 검정력 분석 (확신도)× | |
|---|---|---|
| 분야 | 통계학 | 통계학 |
| 계열 | Hypothesis test | Hypothesis test |
| 기원 연도≠ | 1969 (1st ed.); 1988 (seminal 2nd ed.) | 1986 |
| 창시자≠ | Jacob Cohen | Spiegelhalter & Freedman (1986); O'Hagan, Stevens & Campbell (2005) |
| 유형≠ | Sample size and power planning | Bayesian sample size determination |
| 원전≠ | Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 978-0805802832 | O'Hagan, A., Stevens, J.W. & Campbell, M.J. (2005). Assurance in Clinical Trial Design. Pharmaceutical Statistics, 4(3), 187–201. DOI ↗ |
| 별칭 | sample size calculation, power calculation, sensitivity analysis, a priori power analysis | assurance, bayesian sample size determination, bayesian assurance, Bayesian Güç Analizi (Assurance / Bayesian Sample Size) |
| 관련≠ | 5 | 3 |
| 요약≠ | 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. | Bayesian power analysis — also called assurance — is a sample size determination method that replaces the frequentist notion of power with a probability-weighted average over a prior distribution on the effect size. First formalised by Spiegelhalter and Freedman (1986) and further developed by O'Hagan, Stevens and Campbell (2005), it answers the question: given our current uncertainty about the true effect, what sample size gives us a high overall probability of obtaining a statistically significant result? |
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