In practice, several different measures exist for determining the significance, or goodness of fit, of a logistic regression model. These measures include the G statistic, Pearson statistic, and Hosmer-Lemeshow statistic. In a theoretical sense, all three measures are equivalent. To be more precise, as the number of rows in the predictor matrix goes to infinity, all three measures converge to the same estimate of model significance. However, for any practical regression problem with a finite number of rows in the predictor matrix, each measure produces a different estimate.

Commonly a regression model designer refers to more than one measure. If any single measure indicates a low goodness of fit, or if the measures differ greatly in their assessments of significance, the designer goes back and makes improvements to the regression model.