 However, sometimes effective behavior is aided by inaccurate judgments or cognitively adaptive illusions (Howe 2011). The statistical properties of small samples are a case in point. One feature of small samples is that correlations are amplified, making them easier to detect (Kareev 1995). This fact about small samples, when combined with the known limits to human short-term memory, suggests that our working-memory limits may be an adaptive response to our environment that we exploit at different stages in our lives. Adult short-term working memory is limited to seven items, plus or minus two. For correlations of 0.5 and higher, Kareev demonstrates that sample sizes between five and nine are most likely to yield a sample correlation that is greater than the true correlation in the population (Kareev 2000), making those correlations nevertheless easier to detect. Furthermore, children’s short-term memories are even more restricted than adults, thus making correlations in the environment that much easier to detect. Of course, there is no free lunch: this small-sample effect comes at the cost of inflating estimates of the true correlation coefficients and admitting a higher rate of false positives (Juslin &amp; Olsson 2005). However, in many contexts, including child development, the cost of error arising from under-sampling may be more than compensated by the benefits from simplifying choice (Hertwig &amp; Pleskac 2008) and accelerating learning. In the spirit of Brunswik’s argument for representative experimental design  (section 3.2),  a growing body of literature cautions that the bulk of experiments on adaptive decision-making are performed in highly simplified environments that differ in important respects from the natural world in which human beings make decisions (Fawcett et al. 2014). In response, Houston, MacNamara and colleagues argue, we should incorporate more environmental complexity in our models.