Abstract. Although no consensus on the issue exists yet, some evidence indicates that people are typically overprecise in their inferences. In particular, subjective confidence intervals are often too narrow when compared with Bayesian ones. This paper uses a quasi-Bayesian theory and lab experiments to explore overprecision when people learn about the empirical frequency of some random event. Motivated by the literature on limited attention, we hypothesize that, when there is a large number of potential values of individuals mentally operate with simplified representations of the objective state space. Their mental models are however sophisticated in that they co-move with the signals observed, focusing on the values of most consistent with the evidence available. As a result, they elaborate accurate point estimates but also become too confident about them, as they hardly reflect on those values that would call more into doubt their conclusions. In this line, subjects in our experiment almost exclusively report overly narrow confidence intervals, but also unbiased point estimates of (except when takes extreme values, i.e., close to 0 or 1). Indirect evidence suggests that subjects often consider about 1/5 of the objective state space.

Keywords: Beliefs, Bias, Inference, Omission of information, Overprecision, Probability, Simplification

Highlights

• If people omit elements of the state space, they might be over-precise.
• That is, the posteriors of the included states might be too high, in Bayesian terms.
• We check this explanation of over-precision in an inference experiment.
• We find evidence partly in line with our hypotheses.