The Chi-square test of independence is a non-parametric statistic used to analyze group differences when the dependent variable is measured at a nominal level. It is robust to the distribution of the data and does not require equal variances or homoscedasticity. It can evaluate both dichotomous and multiple group studies, providing detailed information about group performance. However, it has limitations, such as sample size requirements and difficulty in interpreting results with many categories. The Chi-square is a significance test and should be followed by a strength test, such as Cramer's V, to assess the strength of the association. Cramer's V provides a correlation measure, with values of 0.26 considered weak. The Chi-square is useful when parametric assumptions are not met and is flexible for both two-group and multiple-group studies. It is also valuable when data are categorical and when there are violations of parametric assumptions. The test requires that at least 80% of cells have expected values of 5 or more. In the case study, the Chi-square test was used to compare the incidence of pneumonia between vaccinated and unvaccinated employees, revealing a significant difference. Cramer's V indicated a weak association, but the clinical importance of reducing pneumonia incidence was significant. The Chi-square and Cramer's V are simple to compute and are useful for researchers in fields where statistical programs may not be accessible. The test is most reliable when data are collected from randomly selected subjects and sample sizes are sufficiently large. The Chi-square is a valuable tool for analyzing nominal data and is particularly useful when parametric tests are not appropriate.The Chi-square test of independence is a non-parametric statistic used to analyze group differences when the dependent variable is measured at a nominal level. It is robust to the distribution of the data and does not require equal variances or homoscedasticity. It can evaluate both dichotomous and multiple group studies, providing detailed information about group performance. However, it has limitations, such as sample size requirements and difficulty in interpreting results with many categories. The Chi-square is a significance test and should be followed by a strength test, such as Cramer's V, to assess the strength of the association. Cramer's V provides a correlation measure, with values of 0.26 considered weak. The Chi-square is useful when parametric assumptions are not met and is flexible for both two-group and multiple-group studies. It is also valuable when data are categorical and when there are violations of parametric assumptions. The test requires that at least 80% of cells have expected values of 5 or more. In the case study, the Chi-square test was used to compare the incidence of pneumonia between vaccinated and unvaccinated employees, revealing a significant difference. Cramer's V indicated a weak association, but the clinical importance of reducing pneumonia incidence was significant. The Chi-square and Cramer's V are simple to compute and are useful for researchers in fields where statistical programs may not be accessible. The test is most reliable when data are collected from randomly selected subjects and sample sizes are sufficiently large. The Chi-square is a valuable tool for analyzing nominal data and is particularly useful when parametric tests are not appropriate.