Statistical tests are essential tools for identifying relationships between variables in research. Pearson correlation measures the linear relationship between two continuous variables, assuming normality and interval or ratio measurement levels; it is especially useful for analyzing direct associations in normally distributed data. In contrast, Spearman's rho is a non-parametric test that evaluates the association between ranked variables, making it suitable when data do not meet normality or are ordinal. This test helps to identify monotonic relationships, where one variable consistently increases or decreases with another. The Chi-square test is used to assess the association between categorical variables without needing normality, allowing for the analysis of relationships between qualitative variables. The importance of assumptions, such as normality and measurement level, should not be underestimated, as violating them can lead to incorrect conclusions. This highlights the need for careful analysis and the selection of appropriate tests based on the specific characteristics of the data being studied.