In quantitative research, selecting the appropriate statistical test is essential for accurately interpreting the relationships between variables. In this regard, Pearson’s correlation is used when both variables are continuous, normally distributed, and the goal is to examine a linear relationship. This test measures both the strength and direction of the association. In contrast, Spearman’s rho is a non-parametric test applied when the data do not meet the assumption of normality or are ordinal. It is useful for identifying monotonic relationships, even if they are not linear, and is more resistant to outliers. Finally, the Chi-square test allows for the analysis of associations between categorical variables, especially when working with frequencies in contingency tables. Understanding the assumptions behind each test such as normality and level of measurement is key to drawing valid conclusions. Choosing the correct test ensures reliable analysis and meaningful interpretation in any research study.