The debate concerning the choice of effect measure for epidemiologic data has been renewed in the literature, and it suggests some continuing disagreement between the pertinent clinical and statistical criteria. In this article, some defining characteristics of the main choices of effect measure [risk difference (RD), relative risk (RR), and odds ratio (OR)] for binary data are presented and compared, with consideration of both the clinical and statistical perspectives. Relationships of these measures to the relative risk reduction (RRR) and number needed to treat (NNT) are also discussed. A numerical comparison of models of constant RD, RR, and OR is made to assess when and by how much they might differ in practice. Typically the models show only small numerical differences, unless extreme extrapolation is involved. The RD and RR models can predict impossible event rates, either less than zero or greater than 100%. Each measure has potential theoretical justification. RD and RR may enjoy some advantages for communication of risk, but OR may be preferred for data analysis. A clear distinction should be maintained between the objectives of data analysis and subsequent risk communication, and different effect measures may be needed for each.