In this paper we attempt to recover an integrated conception of the Precautionary Principle (PP). The α=.05 inferential-threshold convention widely employed in science is ill-suited to the requirements of policy decision making because it is fixed and unresponsive to the cost trade-offs that are the defining concern of policy decision making. Statistical decision theory - particularly
in its Signal-Detection Theory (SDT) variant - provides a standard framework within which to incorporate the (mis)classification costs associated with deciding between intervention and non-intervention. We show that the PP implements preventive intervention in precisely those circumstances where the SDT-based model yields a (1,1) corner solution. Thus the PP can be understood as a heuristic variant of the SDT corner solution, which in turn serves to patch the incongruity between the inferential practices of science and the inferential requirements of policy decision making. Furthermore, SDT's analytical structure directs attention to a small number of variables - (mis)classification costs and prior probabilities - as determinants of the (1,1) corner solution. Subjective biases impinging upon these variables - omission bias, protected values, and the affect heuristic in particular, moderated by the decision maker's industry-aligned (insider) or industry-opposed (outsider) status - combine within SDT to successfully retrodict features of the PP previously considered puzzling, if not inconsistent or incoherent. These psychological biases do not exclude, and may in part reflect, the decision maker's deontological moral beliefs, or indeed social norms embodied in the nation's legal system (common law vs. civil law).