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Robust chi-square test

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.

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Robust Chi-Square Test of Independence / Goodness-of-Fit

Taksonomisk metoderegister · hypothesis-test / statistics

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 10.1111/j.2517-6161.1984.tb01318.x
Agresti, A. (2002). Categorical Data Analysis (2nd ed.). Wiley-Interscience. · ISBN 978-0471360933

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