In Study 1, 49 college students' preferences for different brands of strawberry jams were compared with experts' ratings of the jams. Students who analyzed why they felt the way they did agreed less with the experts than students who did not. In Study 2, 243 college students' preferences for college courses were compared with expert opinion. Some students were asked to analyze reasons; others were asked to evaluate all attributes of all courses. Both kinds of introspection caused people to make (...) choices that, compared with control Ss', corresponded less with expert opinion. Analyzing reasons can focus people's attention on nonoptimal criteria, causing them to base their subsequent choices on these criteria. Evaluating multiple attributes can moderate people's judgments, causing them to discriminate less between the different alternatives. (shrink)

This article described three heuristics that are employed in making judgements under uncertainty: representativeness, which is usually employed when people are asked to judge the probability that an object or event A belongs to class or process B; availability of instances or scenarios, which is often employed when people are asked to assess the frequency of a class or the plausibility of a particular development; and adjustment from an anchor, which is usually employed in numerical prediction when a relevant value (...) is available. These heuristics are highly economical and usually effective, but they lead to systematic and predictable errors. A better understanding of these heuristics and of the biases to which they lead could improve judgements and decisions in situations of uncertainty. (shrink)

Three experiments examine the effect of base rate consistency under direct experience. Base rate consistency was manipulated by blocking trials and setting base rate choice reinforcement to be either consistent or inconsistent across trial blocks. Experiment 1 shows that, contrary to the usual finding, participants use base rate information more than individuating information when it is consistent, but less when it is inconsistent. In Experiment 2, this effect was replicated, and transferred in verbal questions posed subsequently. Despite experience with consistent (...) base rates increasing sensitivity to base rates in word problems, verbal responses were far from normative. In Experiment 3, participants’ use of base rates was once again moderated by its consistency, but this effect was itself moderated by the diagnosticity of base rate information. Participants were highly accurate in estimating experienced base rates. These studies demonstrate that base rate usage is complex and a function of how base rates are presented (experienced versus summary statistics) and response format (choice proportions versus probability estimates). Knowledge of base rates was insufficient for proper usage in verbal word problems. Although choice proportions showed a sophisticated sensitivity to experienced base rate information, participants seemed unable to demonstrate a similar sophistication when given typical word problems indicating that base rate neglect is a function of information representation and not an inherent processing bias. (shrink)

We have been oversold on the base rate fallacy in probabilistic judgment from an empirical, normative, and methodological standpoint. At the empirical level, a thorough examination of the base rate literature (including the famous lawyer–engineer problem) does not support the conventional wisdom that people routinely ignore base rates. Quite the contrary, the literature shows that base rates are almost always used and that their degree of use depends on task structure and representation. Specifically, base rates play a relatively larger role (...) in tasks where base rates are implicitly learned or can be represented in frequentist terms. Base rates are also used more when they are reliable and relatively more diagnostic than available individuating information. At the normative level, the base rate fallacy should be rejected because few tasks map unambiguously into the narrow framework that is held up as the standard of good decision making. Mechanical applications of Bayes's theorem to identify performance errors are inappropriate when (1) key assumptions of the model are either unchecked or grossly violated, and (2) no attempt is made to identify the decision maker's goals, values, and task assumptions. Methodologically, the current approach is criticized for its failure to consider how the ambiguous, unreliable, and unstable base rates of the real world are and should be used. Where decision makers' assumptions and goals vary, and where performance criteria are complex, the traditional Bayesian standard is insufficient. Even where predictive accuracy is the goal in commonly defined problems, there may be situations (e.g., informationally redundant environments) in which base rates can be ignored with impunity. A more ecologically valid research program is called for. This program should emphasize the development of prescriptive theory in rich, realistic decision environments. (shrink)