Results indicate that overall, despite some limitation, the approach outline in this paper has the potential to expand criminological understanding in meaningful ways. Bayesian analysis offers a useful complement to existing approaches and warrants further inclusion in the ongoing discussion about how best to assess program effectiveness, synthesize evidence, and report findings from crime and justice evaluations that is relevant to policymakers and practitioners. First, it inherently moves away from the long run view of probability used in Null Hypothesis Significance Testing (NHST) and expresses a specific effect based on the hard evidence. Second, it offers a framework for the analysis of a primary study in the context of similar previous ones. Third, this approach can aid in moving criminologists away from an overblown concern with statistical significance at the expense of consideration of the size of intervention effects. Fourth, the reporting of statistical findings in Bayesian analysis may be more pertinent to the layperson's view of probability and might engender greater impact for research findings. Lastly, the analytic process, which revises expressed belief regarding the intervention effects as evidence accumulates, may best fit the real-life process in which programs should be developed, implemented, and evaluated. Evidence from various fields is used as a foundation for the discussion, and an empirical example illustrates how this approach might be useful in practical criminological research. Figure, table, and references