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| p-값과 통계적 유의성× | 귀무가설 검정× | |
|---|---|---|
| 분야 | 연구 통계 | 연구 통계 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도 | 1925 | 1925 |
| 창시자≠ | Ronald Fisher | Ronald Fisher; Neyman & Pearson |
| 유형 | Concept | Concept |
| 원전 | Fisher, R. A. (1925). Statistical Methods for Research Workers. Oliver and Boyd. link ↗ | Fisher, R. A. (1925). Statistical Methods for Research Workers. Oliver and Boyd. link ↗ |
| 별칭≠ | p-value, significance test, statistical significance, alpha level | NHST, hypothesis formulation, null hypothesis, alternative hypothesis |
| 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). | Null Hypothesis Significance Testing (NHST) is the dominant statistical framework in empirical research. The null hypothesis (H₀) represents the default assumption—typically 'no effect' or 'no difference'—while the alternative hypothesis (H₁) represents the claim being tested. The test calculates the probability of observing the data given H₀ is true (p-value); if p is very small, H₀ is rejected in favor of H₁. Formulated by Ronald Fisher and extended by Neyman and Pearson in the early 20th century, NHST is foundational to confirmatory research but has been widely critiqued for misuse and misinterpretation. |
| ScholarGate데이터셋 ↗ |
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