Confronta i metodi

	Analisi di Frequenza Robusta	Test Chi-Quadro Robusto
Campo	Statistica	Statistica
Famiglia	Hypothesis test	Hypothesis test
Anno di origine	1970s–1980s (foundations); applied to frequency analysis throughout the 1990s–2000s	1984 (power divergence); 1900 (Pearson baseline)
Ideatore	Huber, Hampel, Wilcox and the robust statistics tradition	Cressie & Read (power divergence framework); Pearson chi-square extended by multiple authors
Tipo	Robust descriptive and inferential procedure	Robust categorical association / goodness-of-fit test
Fonte seminale	Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing (3rd ed.). Academic Press. ISBN: 978-0123869838	Cressie, N., & Read, T. R. C. (1984). Multinomial goodness-of-fit tests. Journal of the Royal Statistical Society: Series B, 46(3), 440–464.
Alias	robust count analysis, outlier-resistant frequency analysis, robust distributional analysis	robust chi-squared test, Cressie-Read power divergence test, adjusted chi-square test, robust contingency test
Correlati	3	3
Sintesi	Robust frequency analysis applies outlier-resistant estimation and resampling or exact methods to the counting and tabulation of categorical data, reducing the distortion caused by extreme observations, sparse cells, or violations of large-sample assumptions that can make conventional frequency summaries misleading.	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.