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Teste Qui-Quadrado Robusto×Teste exato robusto de Fisher×
ÁreaEstatísticaEstatística
FamíliaHypothesis testHypothesis test
Ano de origem1984 (power divergence); 1900 (Pearson baseline)1935 (base); mid-p robustification 1961+
Autor originalCressie & Read (power divergence framework); Pearson chi-square extended by multiple authorsFisher (1935); mid-p extension by Lancaster (1961) and others
TipoRobust categorical association / goodness-of-fit testRobust exact conditional test
Fonte seminalCressie, N., & Read, T. R. C. (1984). Multinomial goodness-of-fit tests. Journal of the Royal Statistical Society: Series B, 46(3), 440–464. DOI ↗Agresti, A. (2002). Categorical Data Analysis (2nd ed.). Wiley-Interscience. ISBN: 978-0471360933
Outros nomesrobust chi-squared test, Cressie-Read power divergence test, adjusted chi-square test, robust contingency testmid-p Fisher's exact test, robust exact test for contingency tables, conditional robust Fisher test, Fisher mid-p test
Relacionados33
ResumoThe 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.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.
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ScholarGateComparar métodos: Robust chi-square test · Robust Fisher's exact test. Recuperado em 2026-06-18 de https://scholargate.app/pt/compare