Volume 12, Issue 4 (December 2025)
J. Food Qual. Hazards Control 2025, 12(4): 317-324 |
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Madadizadeh F, Abdoli M. Tutorial on Bonferroni Correction as a Post Hoc Analysis of a Significant Chi-Squared Test: A Methodological Guide in Food Science. J. Food Qual. Hazards Control 2025; 12 (4) :317-324
URL: http://jfqhc.ssu.ac.ir/article-1-1182-en.html
URL: http://jfqhc.ssu.ac.ir/article-1-1182-en.html
Student Research Committee, Hamadan University of Medical Sciences, Hamadan, Iran, Department of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran , abdoli_75@yahoo.com
Abstract: (74 Views)
Background: In medical research, analyzing the relationship between two categorical variables is common. While chi-square tests (e.g., Pearson's, McNemar's, and Cochran-Mantel-Haenszel) can determine if a significant association exists, they do not identify which specific categories differ. This tutorial aimed to examine post hoc tests that enable detailed pairwise comparisons of variable categories following a significant chi-square result.
Methods: This tutorial instructs on conducting pairwise Z-tests for comparing proportions, followed by the Bonferroni correction to adjust p-values for multiple comparisons. It also reviews and contrasts four alternative post-hoc approaches for contingency tables: standardized residuals, partitioning, cell comparison, and ransacking. A practical guide for implementing the Bonferroni-adjusted Z-test in common statistical software (R, SPSS, Stata) is provided.
Results: The Bonferroni-adjusted pairwise Z-test provides a straightforward and accessible method for pinpointing significant differences within a contingency table. This approach, readily available in major statistical software, simplifies interpretation by directly adjusting p-values and highlighting specific cells with significant deviations.
Conclusion: To mitigate the increased Type I error risk from multiple comparisons, the Bonferroni adjustment is a crucial tool for post hoc analysis after a significant chi-square test. Compared to other, more complex techniques, it offers a simpler and more intuitive framework for accurately identifying where significant differences lie.
DOI: 10.18502/jfqhc.12.4.20410
Methods: This tutorial instructs on conducting pairwise Z-tests for comparing proportions, followed by the Bonferroni correction to adjust p-values for multiple comparisons. It also reviews and contrasts four alternative post-hoc approaches for contingency tables: standardized residuals, partitioning, cell comparison, and ransacking. A practical guide for implementing the Bonferroni-adjusted Z-test in common statistical software (R, SPSS, Stata) is provided.
Results: The Bonferroni-adjusted pairwise Z-test provides a straightforward and accessible method for pinpointing significant differences within a contingency table. This approach, readily available in major statistical software, simplifies interpretation by directly adjusting p-values and highlighting specific cells with significant deviations.
Conclusion: To mitigate the increased Type I error risk from multiple comparisons, the Bonferroni adjustment is a crucial tool for post hoc analysis after a significant chi-square test. Compared to other, more complex techniques, it offers a simpler and more intuitive framework for accurately identifying where significant differences lie.
DOI: 10.18502/jfqhc.12.4.20410
Keywords: Chi-Square Distribution, Data Interpretation, Statistical, Biostatistics, Chi-Square Test, Paired Comparisons
Type of Study: Short communication |
Subject:
Special
Received: 24/02/11 | Accepted: 25/04/16 | Published: 25/12/21
Received: 24/02/11 | Accepted: 25/04/16 | Published: 25/12/21
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