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How People Use Statistics

Pedro Bordalo¹; J. Michael Conlon²; Nicola Gennaioli³; Spencer Kwon⁴; Andrei Shleifer⁵

¹ Saïd Business School, University of Oxford · ² Carnegie Mellon University · ³ Bocconi University and IGIER · ⁴ Brown University · ⁵ Harvard University

Abstract For standard statistical problems, we provide new evidence documenting (1) multimodality and (2) instability in probability estimates, including from irrelevant changes in problem description. The evidence motivates a model in which, when solving a problem, people represent each hypothesis by attending to its salient features while neglecting other, potentially more relevant, ones. Only the statistics associated with salient features are used. The model unifies biases in judgments about i.i.d. draws, such as the Gambler's Fallacy and insensitivity to sample size, with biases in inference such as under- and overreaction and insensitivity to the weight of evidence. The model makes predictions for how changes in the salience of specific features jointly shapes known biases and measured attention to features, but also create entirely new biases. We test and confirm these predictions experimentally. Salience-driven attention to features emerges as a unifying framework for biases conventionally explained using a variety of stable heuristics or distortions of Bayes' rule.

DOI

10.1093/restud/rdaf022

Volume

93 (1)

Pages

250-285

Language

en

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