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| 신뢰구간× | p-값과 통계적 유의성× | |
|---|---|---|
| 분야 | 연구 통계 | 연구 통계 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 1937 | 1925 |
| 창시자≠ | Jerzy Neyman | Ronald Fisher |
| 유형 | Concept | Concept |
| 원전≠ | Neyman, J. (1937). Outline of a Theory of Statistical Estimation Based on the Classical Theory of Probability. Philosophical Transactions of the Royal Society, 236, 333–380. DOI ↗ | Fisher, R. A. (1925). Statistical Methods for Research Workers. Oliver and Boyd. link ↗ |
| 별칭 | CI, 95% CI, credible interval, interval estimate | p-value, significance test, statistical significance, alpha level |
| 관련≠ | 4 | 5 |
| 요약≠ | A confidence interval (CI) is a range of values, calculated from sample data, that likely contains the true population parameter. Introduced by Jerzy Neyman in 1937, it provides an interval estimate rather than a single point estimate, incorporating both the observed value and the uncertainty around it. The standard 95% confidence interval is a robust, intuitive alternative to p-values for communicating research results. | 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). |
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