Selecting the appropriate statistical test is crucial for valid conclusions in any investigation. It is important to know how these tests work in different situations. Pearson correlation is used when examining the linear relationship between two continuous variables measured at the interval or ratio level. It requires key assumptions: normality, linearity, and homoscedasticity. If these are violated, Spearman’s rho becomes more suitable, as it assesses monotonic relationships between ordinal or non-normally distributed continuous variables, without assuming linearity or normality.
The Chi-square test, by contrast, is applied to explore associations between categorical variables. It helps determine whether observed frequencies differ significantly from expected frequencies, revealing dependency or independence between variables.
Choosing the correct test hinges on measurement level (nominal, ordinal, interval, ratio) and distribution. Ignoring assumptions can lead to misleading results. Proper application of these tests allows investigators to uncover meaningful patterns and relationships, guiding further analysis or decision-making with statistical rigor.