The naive intuitive statistician:: a naive sampling model of intuitive confidence intervals

The perspective of the naive intuitive statistician is outlined and applied to explain overconfidence when people produce intuitive confidence intervals and why this format leads to more overconfidence than other formally equivalent formats. The naive sampling model implies that people accurately describe the sample information they have but are naive in the sense that they uncritically take sample properties as estimates of population properties. A review demonstrates that the naive sampling model accounts for the robust and important findings in previous research as well as provides novel predictions that are confirmed, including a way to minimize the overconfidence with interval production. The authors discuss the naive sampling model as a representative of models inspired by the naive intuitive statistician.