Model Validation: They help validate whether a chosen statistical model accurately represents the observed data. Predictive Accuracy: Ensuring the model fits well can improve the accuracy of predictions regarding disease spread and intervention outcomes. Assumption Checking: Many statistical models rely on assumptions (e.g., normality, independence); goodness of fit tests verify these assumptions.
