In epidemiology, data collection often involves numerous variables that may or may not be relevant to the outcome of interest. Including irrelevant variables can lead to overfitting, where the model describes random error or noise instead of the underlying relationship. Conversely, omitting important variables can result in bias and incorrect conclusions. Proper variable selection ensures that the model remains robust, interpretable, and generalizable to other datasets or populations.
