Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Robustní Chí-kvadrát test× | Fisherův exaktní test× | |
|---|---|---|
| Obor | Statistika | Statistika |
| Rodina | Hypothesis test | Hypothesis test |
| Rok vzniku≠ | 1984 (power divergence); 1900 (Pearson baseline) | 1922 |
| Tvůrce≠ | Cressie & Read (power divergence framework); Pearson chi-square extended by multiple authors | R. A. Fisher |
| Typ≠ | Robust categorical association / goodness-of-fit test | Exact test of independence for categorical data |
| Původní zdroj≠ | 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 ↗ | Fisher, R. A. (1922). On the interpretation of chi-squared from contingency tables, and the calculation of P. Journal of the Royal Statistical Society, 85(1), 87–94. DOI ↗ |
| Další názvy≠ | robust chi-squared test, Cressie-Read power divergence test, adjusted chi-square test, robust contingency test | Fisher-Irwin test, exact test of independence, Fisher'ın Kesin Testi |
| Příbuzné≠ | 3 | 2 |
| Shrnutí≠ | 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. | Fisher's exact test is a nonparametric exact-probability test of independence for small-sample contingency tables, introduced by R. A. Fisher in 1922. Rather than relying on a large-sample approximation, it computes the exact probability of the observed table directly from the hypergeometric distribution. |
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