This paper shows how to incorporate judgment in a decision problem under uncertainty, within a classical framework. The method relies on the specification of a judgmental decision with associated confidence level and application of hypothesis testing. The null hypothesis tests whether marginal deviations from the judgmental decision generate negative changes in expected utility. The resulting estimator is always at the boundary of the confidence interval: beyond that point the probability of decreasing the expected utility becomes greater than the chosen confidence level. The decision maker chooses the confidence level as a mapping from the p-value of the judgmental decision into the unit interval. I show how the choice of priors in Bayesian estimators is equivalent to the choice of this confidence level mapping. I illustrate the implications of this new framework with a portfolio choice between cash and the EuroStoxx50 index.