Policy analysis, like all empirical research, combines assumptions and data to draw conclusions about a population of interest. The logic of empirical inference is summarized by the relationship: assumptions + data conclusions. Data alone do not suffice to draw conclusions. Inference requires assumptions that relate the data to the population of interest ... Holding fixed the available data, and presuming avoidance of errors in logic, stronger assumptions yield stronger conclusions. At the extreme, one may achieve certitude by posing sufficiently strong assumptions. [However, ] the credibility of inference decreases with the strength of the assumptions maintained ... Researchers regularly express certitude about the consequences of alternative [policy] decisions. Exact predictions of outcomes are common, and expressions of uncertainty are rare. Yet policy predictions are often fragile. Conclusions may rest on critical, unsupported assumptions or on leaps of logic. Then the certitude of policy analysis is not credible.