Hypothesis testClassical statistics

Bayesian Cross-Tabulation Analysis

Bayesian cross-tabulation analysis tests whether two categorical variables are associated by computing a Bayes factor that quantifies the evidence for an association model against an independence model. Unlike classical chi-square testing, it provides a continuous measure of evidence, supports the null hypothesis directly, and updates naturally with prior knowledge about the cell probabilities.

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Sources

  1. Gunel, E., & Dickey, J. (1974). Bayes factors for independence in contingency tables. Biometrika, 61(3), 545–557. DOI: 10.1093/biomet/61.3.545
  2. Jamil, T., Ly, A., Morey, R. D., Love, J., Marsman, M., & Wagenmakers, E.-J. (2017). Default Gunel and Dickey Bayes factors for contingency tables. Behavior Research Methods, 49(2), 638–652. DOI: 10.3758/s13428-016-0739-8

Related methods

ScholarGateBayesian cross-tabulation analysis (Bayesian Contingency Table Analysis). Retrieved 2026-06-04 from https://scholargate.app/en/statistics/bayesian-cross-tabulation-analysis