Evidence-appraisal glossary
Overfitting
Overfitting happens when a model learns the noise and quirks of its development data rather than the real signal, so it looks accurate at home but performs worse on new patients.
It is more likely when a model has many predictors relative to the number of outcome events, or when predictors are chosen by chasing statistical significance. The telltale signs surface in validation: a shrunken calibration slope and a c-statistic that drops in new data. Remedies include limiting the number of candidate predictors, penalized estimation that shrinks coefficients, and honest external validation.
This is a plain-language methodology definition for reading research. It is general education, not medical advice.