Intuitive Prediction: Ecological Validity Versus Representativeness SOREL CAHAN 1 * and TCHIA SNAPIRI 1,2 1 Hebrew University of Jerusalem, School of Education, Jerusalem, Israel 2 Hebrew University of Jerusalem, School of Social Work, Jerusalem, Israel ABSTRACT Insufficiently regressive intuitive predictions have been attributed to mistaken reliance on the representativeness heuristic. In contrast, we suggest that intuitive predictions stem from a conceptualization of ‘goodness of prediction’ that differs from the accepted statistical definition in terms of error minimization, namely, ecological validity—that is, representation of the substantive characteristics of the predicted variable Y and its distribution as well as of the relationship between Y and the predictor X —rather than minimization of prediction errors. Simultaneous satisfaction of the above representation requirements is achieved by multivalued prediction: The prediction of different Y 0 values for the same X value, resulting in conditional distributions Y 0 jX for at least some X values. Empirical results supporting this hypothesis are presented and discussed. Copyright # 2008 John Wiley & Sons, Ltd. key words intuitive prediction; representativeness heuristic; least-squares norma- tive prediction; ecological validity; rationality INTRODUCTION The study of intuitive numerical prediction (e.g. prediction of children’s height from their age; prediction of future academic achievement on the basis of past achievement) typically involves comparison between people’s predictions and ‘normative’ prediction, defined by accepted statistical rules, in an attempt to determine the merits of the ‘ordinary’ person as an intuitive statistician (e.g. Ganzach & Krantz, 1991; Kahneman & Tversky, 1973; Stanovich & West, 1998; Tversky & Kahneman, 1974). The statistical definition of the quality of prediction involves minimizing prediction errors. Specifically, the statistical solution when Y is predicted given X and their relationship is linear, namely the least-squares linear prediction Y 0 A , Y 0 iA ¼ f A ðX i Þ¼ r xy  SD y SD x ðX i  XÞþ Y (1) Journal of Behavioral Decision Making J. Behav. Dec. Making, 21: 297–316, (2008) Published online 26 March 2008 in Wiley InterScience (www.interscience.wiley.com) DOI: 10.1002/bdm.589 * Correspondence to: Sorel Cahan, Hebrew University of Jerusalem, School of Education, Jerusalem, Israel. E-mail: sorelc@mscc.huji.ac.il Copyright # 2008 John Wiley & Sons, Ltd.