 Overconfidence can be understood as an artifact of small samples. The Naïve Sampling Model (Juslin, Winman, &amp; Hansson 2007) assumes that agents base judgments on a small sample retrieved from long-term memory at the moment a judgment is called for, even when there are a variety of other methods available to the agent. This model presumes that people are naïve statisticians (Fiedler &amp; Juslin 2006) who assume, sometimes falsely, that samples are representative of the target population of interest and that sample properties can be used directly to yield accurate estimates of a population. The idea is that when sample properties are uncritically taken as estimators of population parameters a reasonably accurate probability judgment can be made with overconfidence, even if the samples are unbiased, accurately represented, and correctly processed by the cognitive mechanisms of the agent. When sample sizes are restricted, these effects are amplified.