The F-test is a statistical method used to compare two or more models to determine if they significantly differ in their ability to explain the variance in a dependent variable. Commonly applied in regression analysis and ANOVA, the F-test evaluates the null hypothesis that the models have equal explanatory power. By calculating the F-statistic, which follows an F-distribution, researchers can assess whether the addition of variables improves the model’s fit. A significant F-test result suggests that at least one of the independent variables contributes meaningfully to the model, guiding decisions on model complexity and variable selection.