The Pearson correlation, Spearman Rho, and Chi-square tests each address specific characteristics depending on the type and distribution of the data. The Pearson test is ideal for analyzing linear relationships between two continuous variables with a normal distribution and measured by interval or ratio, such as age and income. Spearman Rho is a non-meaning alternative used when data are generally normal or when normality is violated; it evaluates monotonic conditions, meaning that variables move in equal or opposite directions, but not always at a constant rate. On the other hand, the chi-square test is used to assess the relationship between two categorical variables, such as occupation and desired news source. Pearson accepts linearity and normality, while Spearman accepts ordinary or continuous data with a monotonic trend. The chi-square test requires a sufficiently large test and expected frequencies for at least five days. Understanding these assumptions and selecting appropriate tests ensures meaningful interpretation and eliminates statistical errors or misconduct.