Multiple logistic regression coefficients (or the associated odds ratios) alone are not always an adequate basis for making practical and/or scientific interpretations of a model. In this paper I discuss methods for enhancing interpretation through the examination of fitted probabilities. In particular, I focus on analyzing the contribution of specific effects of interest in relation to the background of variation introduced by the other factors in the model.