In the first one, understanding relationships between variables is key in research, and choosing the right test depends on the type of data and its distribution. Pearson correlation is best when we work with continuous, normally distributed data and want to see if two variables increase or decrease together in a straight line. For example, we might use it to study the relationship between study hours and exam scores. In contrast, Spearman’s rho is ideal when the data is ordinal or does not meet the assumption of normality. It ranks the data to see if higher values in one variable generally match higher values in another. This is useful when analyzing, for instance, the correlation between satisfaction level (ranked from 1 to 5) and service speed. Finally, the Chi-square test is used with categorical data to check if there is a significant association between groups, like gender and voting preference. Following assumptions like normality and measurement level ensures valid results and avoids misinterpretation.