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| 출판 편향× | p-값과 통계적 유의성× | |
|---|---|---|
| 분야 | 연구 통계 | 연구 통계 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 1979 | 1925 |
| 창시자≠ | Robert Rosenthal | Ronald Fisher |
| 유형 | Concept | Concept |
| 원전≠ | Rosenthal, R. (1979). The file drawer problem and tolerance for null results. Psychological Bulletin, 86(3), 638–641. DOI ↗ | Fisher, R. A. (1925). Statistical Methods for Research Workers. Oliver and Boyd. link ↗ |
| 별칭 | file drawer problem, selective reporting, outcome reporting bias, funnel plot asymmetry | p-value, significance test, statistical significance, alpha level |
| 관련≠ | 4 | 5 |
| 요약≠ | Publication bias occurs when the results of a study influence whether the study is published. Typically, studies with statistically significant or positive results are more likely to be published than studies with non-significant or negative results, even if both are scientifically valid. This bias distorts the published literature, making treatments appear more effective than they actually are. Rosenthal (1979) termed this the 'file drawer problem': research with null results sits in file drawers, unpublished, creating a biased sample of published evidence. Funnel plots and statistical tests (e.g., Egger test) can detect asymmetry suggesting publication bias; meta-analyses must account for this bias. | 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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