This paper centers on an asymmetry, or bias, in the accuracy of multi-criteria, conjunctive and disjunctive decisions, which originates from fundamental properties of the logical conjunction and disjunction operations. A series of Monte Carlo simulations demonstrates that, as we keep adding criteria to a multi-criteria satisficing decision rule, errors in the data produce decision errors asymmetrically. As a result, in conjunctive decisions, the probability of a false negative increases steadily while the probability of a false positive decreases. In contrast, in disjunctive decisions, as we keep adding criteria, the probability of a false positive increases while that of a false negative decreases. Take, for instance, a conjunctive business decision where the individual decision criteria do not exhibit a substantially higher probability of a false positive than a false negative. In such a decision, the probability of overlooking a bargain can be far greater than the probability of misjudging an unattractive offer to be a good one.