<![CDATA[The assumptions of homogeneity and normality are fundamental prerequisites in parametric statistical analyses such as the t-test, ANOVA, and linear regression. Without meeting these assumptions, the results of data analysis may become invalid and lead to biased conclusions. This article aims to provide a comprehensive review of the fundamental concepts of homogeneity and normality, their importance in quantitative research, and various methods used to test them. The discussion covers definitions and the roles of homogeneity and normality in statistical tests, testing methods such as Levene’s Test and Bartlett’s Test for homogeneity, as well as Kolmogorov-Smirnov Test and Shapiro-Wilk Test for normality. The analysis also includes the interpretation of test results and an example of their application to research data. The findings indicate that a solid understanding of these basic statistical assumptions is crucial to ensure the validity and reliability of research outcomes. This article provides both theoretical and practical foundations to help researchers select and apply appropriate testing methods prior to conducting data analysis. ]]>
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