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| 강건 피셔 정확 검정 (Robust Fisher's Exact Test)× | 강건 카이제곱 검정× | |
|---|---|---|
| 분야 | 통계학 | 통계학 |
| 계열 | Hypothesis test | Hypothesis test |
| 기원 연도≠ | 1935 (base); mid-p robustification 1961+ | 1984 (power divergence); 1900 (Pearson baseline) |
| 창시자≠ | Fisher (1935); mid-p extension by Lancaster (1961) and others | Cressie & Read (power divergence framework); Pearson chi-square extended by multiple authors |
| 유형≠ | Robust exact conditional test | Robust categorical association / goodness-of-fit test |
| 원전≠ | Agresti, A. (2002). Categorical Data Analysis (2nd ed.). Wiley-Interscience. ISBN: 978-0471360933 | Cressie, N., & Read, T. R. C. (1984). Multinomial goodness-of-fit tests. Journal of the Royal Statistical Society: Series B, 46(3), 440–464. DOI ↗ |
| 별칭 | mid-p Fisher's exact test, robust exact test for contingency tables, conditional robust Fisher test, Fisher mid-p test | robust chi-squared test, Cressie-Read power divergence test, adjusted chi-square test, robust contingency test |
| 관련 | 3 | 3 |
| 요약≠ | The robust Fisher's exact test extends Fisher's classic exact test for contingency tables by applying conservative-correcting adjustments — most commonly the mid-p correction — to reduce the extreme conservatism of the standard exact test. This produces better-calibrated Type I error rates while maintaining validity in small and sparse samples. | The robust chi-square test extends the classic Pearson chi-square framework to remain reliable when standard assumptions — especially the minimum expected-cell-count rule — are violated. Using power divergence statistics (Cressie & Read, 1984) or resampling-based corrections, it produces valid inferences for sparse contingency tables, small samples, and unbalanced categorical data where the ordinary chi-square approximation breaks down. |
| ScholarGate데이터셋 ↗ |
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