The naïve intuitive statistician: a naïve sampling model of intuitive confidence intervals.
The perspective of the naïve 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 naïve sampling model implies that people accurately describe the sample information they have but are naïve in the sense that they uncritically take sample properties as estimates of population properties. A review demonstrates that the naïve 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 naïve sampling model as a representative of models inspired by the naïve intuitive statistician.
Juslin
Peter
P
Department of Psychology, Uppsala University, Uppsala, Sweden. Peter.Juslin@psyk.uu.se
Winman
Anders
A
Hansson
Patrik
P
D006801:Humans; D019545:Intuition; D007600:Judgment; D015233:Models, Statistical; D011337:Probability Learning; D015983:Selection Bias