en تخصصي Special Short communication Short communication <div style="text-align: justify;"><span style="font-family:Times New Roman;"><span style="font-size:14px;"><span style="line-height:115%"><b><span style="line-height:115%"><span style="letter-spacing:-.2pt">Background: </span></span></b><span style="line-height:115%"><span style="letter-spacing:-.2pt">In medical research, analyzing the relationship between two categorical variables is common. While chi-square tests (e.g., Pearson&#39;s, McNemar&#39;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.</span></span></span><br> <span style="line-height:115%"><b><span style="line-height:115%">Methods: </span></b><span style="line-height:115%">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.</span></span><br> <span style="line-height:115%"><b><span style="line-height:115%">Results: </span></b><span style="line-height:115%">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.</span></span><br> <span style="line-height:107%"><b><span style="line-height:107%">Conclusion: </span></b><span style="line-height:107%">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.<span style="font-family:&quot;Times New Roman&quot;,&quot;serif&quot;"></span></span></span><br> <br> <br> <span style="line-height:107%"><b><span style="line-height:107%">DOI:</span></b><span style="line-height:107%"> 10.18502/jfqhc.12.4.20410</span></span></span></span></div> Chi-Square Distribution, Data Interpretation, Statistical, Biostatistics, Chi-Square Test, Paired Comparisons 317 324 http://jfqhc.ssu.ac.ir/browse.php?a_code=A-10-1700-1&slc_lang=en&sid=1 F. Madadizadeh 0000-0002-5757-182X No Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran, Associate professor of Biostatistics, Center for Healthcare Data Modeling, Departments of Biostatistics and Epidemiology, School of public health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran M. Abdoli abdoli_75@yahoo.com 0000-0002-5525-8549 Yes Student Research Committee, Hamadan University of Medical Sciences, Hamadan, Iran, Department of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran