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| p-값과 통계적 유의성× | 통계적 검정력과 표본 크기× | |
|---|---|---|
| 분야 | 연구 통계 | 연구 통계 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 1925 | 1988 |
| 창시자≠ | Ronald Fisher | Jacob Cohen |
| 유형 | Concept | Concept |
| 원전≠ | Fisher, R. A. (1925). Statistical Methods for Research Workers. Oliver and Boyd. link ↗ | Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 0-8058-0283-5 |
| 별칭 | p-value, significance test, statistical significance, alpha level | power analysis, sample size calculation, 1 minus beta, sensitivity |
| 관련≠ | 5 | 4 |
| 요약≠ | The p-value is the probability of observing data as extreme as or more extreme than what was actually observed, assuming the null hypothesis is true. Introduced by Ronald Fisher in 1925, it is the foundation of frequentist hypothesis testing. Statistical significance is declared when the p-value falls below a pre-specified threshold (alpha level, typically 0.05). | Statistical power is the probability of detecting a true effect if it exists (1 − β). Power analysis determines the sample size required to detect a hypothesized effect size with specified Type I error (α) and Type II error (β) rates. Introduced by Jacob Cohen (1988), power analysis is foundational to research design: underpowered studies produce inflated effect size estimates and are unlikely to replicate. The standard benchmark is 80% power (β = 0.20), though critical studies may require 90% power. |
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