Abstract
AbstractPeople regularly make sense of distributions that are complicated by noise. How do individuals determine whether an outlying observation should be incorporated into one's understanding of the true distribution of the population or considered a fluke that ought to be disregarded? In a simple prediction task, we examine how individuals incorporate outliers and compare their behavior to various prescriptive models (e.g., averaging and tests of discordancy). We find that, on average, individuals do discount outlying values and that their outlier detection strategies approximate approaches that statisticians have recommended for Gaussian distributions, even when the observed distributions are not Gaussian. However, there are notable differences in treatment of outliers across individuals.
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