Goodness of fit
\ ˈgʊdnəs \ ʌv \ fɪt \
The goodness of fit of a statistical model is how well the model fits a set of observed data.
When creating a predictive statistical model based on historical data it is important to strike a balance between a good fit to the data (aimed at capturing underlying predictive factors) and overfitting the data (incorporating random noise into the model).
There are a number of statistical tests often used in longevity modelling to measure goodness of fit, such as the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), and the Hosmer & Lemeshow (H&L) test.