A naïve sampling model of intuitive confidence intervals

Abstract: A particular field in research on judgment and decision making (JDM) is concerned with realism of confidence in one’s knowledge. An interesting finding is the so-called format dependence effect, which implies that assessment of the same probability distribution generates different conclusions about over- or underconfidence depending on the assessment format. In particular, expressing a belief about some unknown continuous quantity (e.g., a stock value) in the form of an intuitive confidence interval is severely prone to overconfidence as compared to expressing the belief as an assessment of a probability judgment. This thesis gives a tentative account of this finding in terms of a Naïve Sampling Model, which assumes that people accurately describe their available information stored in memory, but they are naïve in the sense that they treat sample properties as proper estimators of population properties (Study 1). The effect of this naivety is directly investigated empirically in Study 2. A prediction that short-term memory is a constraining factor for sample size in judgment, suggesting that experience per se does not eliminate overconfidence is investigated and verified in Study 3. Age-related increments in overconfidence were observed with intuitive confidence interval but not for probability judgment (Study 4). This thesis suggests that no cognitive processing bias (e.g., Tversky & Kahneman, 1974) over and above naivety is needed to understand and explain the overconfidence “bias” with intuitive confidence interval and hence the format dependence effect.