Results indicate that Bayes’ Theorem is a potentially useful tool for applying research findings to the difficult task of diagnosing child abuse. The research demonstrates the potential use of Bayes’ Theorem for combining base rate information and test information to assist in diagnostic decisions about child abuse. Two examples of the application of research findings concerning signs of child abuse to decisionmaking are demonstrated, using data from research studies of signs of physical abuse and sexual abuse. The calculation of the probability of the presence of abuse using Bayes’ Theorem is described, given prevalence information and specific indicators of abuse. In addition, the precision of probability estimates can be described using methods such as sampling for beta distributions. However, the accuracy of posterior probability estimates derived from Bayes’ Theorem can be affected by three additional issues, including imprecision in base rate estimates, imprecision in sensitivity and specificity, and the non-independence of information which is combined in Bayes’ Theorem. These sources of imprecision are discussed and recommendations are made with regard to these three issues. References