In epidemiology, the selection of an appropriate model is vital for understanding the distribution and determinants of health-related events. BIC helps to avoid overfitting, which occurs when a model is too complex and captures the noise rather than the underlying data pattern. By penalizing models with more parameters, BIC encourages the selection of simpler, more interpretable models that generalize better to new data.
