Abstract
Pearsons chi-square (Χ²) tests are essential nonparametric statistical tools for analyzing associations among categorical data, making them crucial for research involving non-numeric variables. These tests are widely utilized in various research fields due to their independence from normal distribution assumptions. Chi-square tests are utilized to assess whether there is a significant association between groups, populations, or criteria, and to examine how closely observed data distributions align with expected ones. The three primary types of chi-square tests are: the Goodness-of-Fit test, which checks if the distribution of categorical data in a sample conforms to a predefined distribution the Test of Independence, which investigates whether there is a relationship between categorical variables within a single sample and the Test of Homogeneity, which compares the frequency counts of a categorical variable across multiple populations to see if their distributions are similar.For valid and reliable results, it is crucial to consider factors such as random sampling, adequate cell counts, sufficient sample size, and mutually exclusive variables. In healthcare research, chi-square tests are essential for analyzing risk factors, evaluating AYUSH treatments, assessing nursing interventions, and studying health behavior trends. This review underscores their importance in statistical analysis and evidence-based decision-making.
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