| Abstract: | Most of people′s apparent strategies for covariation assessment and Bayesian inference can lead to errors. However, it is unclear how often and to what degree the strategies are inaccurate in natural contexts. Through Monte Carlo simulation, the respective normative and intuitive strategies for the two tasks were compared over many different situations. The results indicate that (a) under some general conditions, all the intuitive strategies perform much better than chance and many perform surprisingly well, and (b) some simple environmental variables have large effects on most of the intuitive strategies′ accuracy, not just in terms of the number of errors, but also in terms of the kinds of errors (e.g., incorrectly accepting versus incorrectly rejecting a hypothesis). Furthermore, common to many of the intuitive strategies is a disregard for the strength of the alternative hypothesis. Thus, a key to better performance in both tasks lies in considering alternative hypotheses, although this does not necessarily imply using a normative strategy (i.e., calculating the φ coefficient or using Bayes′ theorem). Some intuitive strategies take into account the alternative hypothesis and are accurate across environments. Because they are presumably simpler than normative strategies and are already part of people′s repertoire, using these intuitive strategies may be the most efficient means of ensuring highly accurate judgment in these tasks. |
