Fractures are binary events (they either occur or they do not), and predicting whether fractures may occur involves assigning probabilities of one or more of those events occurring over time to populations and to individuals. Fracture risk prediction has become central to the management of osteoporosis and fracture prevention in clinical practice, and the ultimate clinical usefulness of the prediction tools used to estimate these risks depends, at a minimum, on the validity and accuracy of those tools. In this paper, we will describe how fracture prediction models are developed and validated, and how their performance characteristics are assessed. We will provide a checklist by which clinicians, clinical researchers, and reviewers of journal submissions can judge whether a fracture prediction tool meets basic requirements of good performance. We will further describe how the incremental predictive value of additional diagnostic tools, such as bone mass measurement technologies, is assessed.