On the Choice of Risk Levels in Managerial Decision-Making

The purpose of this paper was to develop a set of factors which would enable one to answer questions regarding specific alpha and beta values, whether they should be large or small errors, and to what kinds of decisions are these errors related. The set of factors found useful in thinking through the difficulties encountered are: 1) number of alternatives, 2) planning horizon, 3) past success of decision-maker, and 4) cost-revenue consequences of an action. In classical hypothesis testing there are no rules that allow one to systematically set alpha and beta error levels. Generally, type I error is set at 5 per cent (alpha per cent) and type II error is almost ignored. A number of people in diverse areas of specialization have drawn attention to the unsystematic treatment in setting alpha and beta in classical hypothesis testing. Important too is the confusion between classical statistical inference and Bayesian statistical decision-making. Selecting error levels has been made difficult by a failure to distinguish between decision and inferential problems. The difference between the expected costs of proposed projects and the expected costs associated with reducing error levels is also highlighted since this difference has led to some confusion in establishing error levels. A next step is to test whether this approach materially improves the results of hypothesis testing in organizations. Hopefully, others will be stimulated to pursue a similar line of investigation, or to provide alternative approaches.