A model for epidemiologic typing data is introduced, and likelihood ratio methods are developed for evaluating these data as evidence about disease spread. The observed data consist of microorganism subtypes from an index case of infectious disease, cases clustered with the index case, and a reference sample. The likelihood methods are evaluated via probabilities of observing epidemiologic subtypes that represent strong and sometimes misleading evidence favoring one hypothesis over another. A general bound is identified for the probability of observing strong evidence favoring a close epidemiologic relationship between the index and cluster cases vis-à-vis no relationship when in fact there is none. The advantages of this approach versus alternate approaches to measuring the strength of typing evidence are discussed.