The Pearson correlation is used to measure the strength and direction of a linear relationship between two continuous variables, such as height and weight. It assumes that the data are normally distributed, measured at the interval or ratio level, and free of extreme outliers. When these assumptions aren’t met, the Spearman’s rho is a better choice. It’s a non-parametric test that works well with ordinal data or non-normal distributions, focusing on the consistency of ranks rather than exact values. The Chi-square test is ideal for exploring relationships between categorical variables, like gender and product preference. It shows whether the distribution of one variable is related to another. Each test is useful for different types of data, and choosing the correct one is essential. Understanding assumptions such as normality, measurement level, and independence ensures more accurate results and helps researchers make meaningful interpretations about variable relationships.