In the context of epidemiology, overfitting occurs when a statistical model describes random error or noise instead of the underlying relationship. This typically happens when a model is excessively complex, such as having too many parameters compared to the number of observations. Overfitting can lead to models that perform well on training data but poorly on new, unseen data, thereby limiting the ability to generalize findings to a larger population.
