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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. Thenaïve 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.

Format dependence implies that assessment of the same subjective probability distribution produces different conclusions about over- or underconfidence depending on the assessment format. In 2 experiments, the authors demonstrate that the overconfidence bias that occurs when participants produce intervals for an uncertain quantity is almost abolished when they evaluate the probability that the same intervals include the quantity. The authors successfully apply a method for adaptive adjustment of probability intervals as a debiasing tool and discuss a tentative explanation in terms of a naive sampling model. According to this view, people report their experiences accurately, but they are naive in that they treat both sample proportion and sample dispersion as unbiased estimators, yielding small bias in probability evaluation but strong bias in interval production.

Research with general knowledge items demonstrates extreme overconfidence when people estimate confidence intervals for unknown quantities, but close to zero overconfidence when the same intervals are assessed by probability judgment. In 3 experiments, the authors investigated if the overconfidence specific to confidence intervals derives from limited task experience or from short-term memory limitations. As predicted by the naïve sampling model (P. Juslin, A. Winman, & P. Hansson, 2007), overconfidence with probability judgment is rapidly reduced by additional task experience, whereas overconfidence with intuitive confidence intervals is minimally affected even by extensive task experience. In contrast to the minor bias with probability judgment, the extreme overconfidence bias with intuitive confidence intervals is correlated with short-term memory capacity. The proposed interpretation is that increased task experience is not sufficient to cure the overconfidence with confidence intervals because it stems from short-term memory limitations.

Realistic confidence judgments are essential to everyday functioning, but few studies have addressed the issue of age differences in overconfidence. Therefore, the authors examined this issue with probability judgment and intuitive confidence intervals in a sample of 122 healthy adults (ages:

35-40, 55-60, 70-75 years). In line with predictions based on the naïve sampling model (P. Juslin, A. Winman, & P. Hansson, 2007), substantial format dependence was observed, with extreme overconfidence when confidence was expressed as an intuitive confidence interval but not when confidence was expressed as a probability judgment. Moreover, an age-related increase in overconfidence was selectively observed when confidence was expressed as intuitive confidence intervals. Structural equation modeling indicated that the age-related increases in overconfidence were mediated by a general cognitive ability factor that may reflect executive processes. Finally, the results indicated that part of the negative influence of increased age on general ability may be compensated for by an age-related increase in domain-relevant knowledge.(c) 2008 APA, all rights reserved