 Ideally, you would prefer a procedure for delivering your “shots” that had both a low bias and low variance. Absent that, and given the choice between a low bias and high variance procedure versus a high bias and low variance procedure, you would presumably prefer the latter procedure if it returned a lower overall score than the former, which is true of the corresponding figures above. Although a decision maker’s learning algorithm ideally should have low bias and low variance, in practice it is common that the reduction in one type of error yields some increase in the other. In this section we explain the conditions under which the relationship between expected squared loss of an estimator and its bias and variance holds and then remark on the role that the bias-variance trade-off plays in research on bounded rationality.