The theory of evidential probability originates in Kyburg (1961), who developed a logical system to deal consistently with the results of classical statistical analyses.  Evidential probability thus falls within the attempts to establish the epistemic use of classical statistics. Haenni et al (2010) and Kyburg and Teng (2001) present an insightful introduction to evidential probability.  The system is based on a version default reasoning: statistical hypotheses come attached with a confidence level, and the logical system organizes how such confidence levels are propagated in inference, and thus advises which hypothesis to use for predictions and decisions. Particular attention is devoted to the propagation of confidence levels in inferences that involve multiple instances of the same hypothesis tagged with different confidences, where those confidences result from diverse data sets that are each associated with a particular population. Evidential probability assists in selecting the optimal confidence level, and thus in choosing the appropriate population for the case under consideration. In other words, evidential probability helps to resolve the reference class problem alluded in the foregoing.