Statistical tests are essential for analyzing relationships between variables. We choose the appropriate test depending on the type of variables involved and the purpose of the analysis. For instance, Pearson correlation is used when examining the relationship between two continuous variables that are normally distributed. Spearman’s rho is more appropriate when the data is ordinal or does not meet the assumption of normality, making it useful for non-parametric analysis. The Chi-square test, on the other hand, is used to explore relationships between categorical variables. These tests help us understand whether there is a significant association between variables and how strong that relationship is. However, it is important to consider certain assumptions before applying them. For example, Pearson’s test requires normality and measurement level (interval or ratio data), while the Chi-square test assumes independence and sufficient expected frequencies. Recognizing these assumptions and the level of measurement helps ensure valid and reliable results in research.