The Journal of Risk and Uncertainty, 30:3; 195–209, 2005 c  2005 Springer Science + Business Media, Inc. Manufactured in The Netherlands. The Gambler’s Fallacy and the Hot Hand: Empirical Data from Casinos RACHEL CROSON crosonr@wharton.upenn.edu The Wharton School, University of Pennsylvania, Suite 500, Huntsman Hall, 3730 Walnut Street, Philadelphia, PA 19104-6340, USA JAMES SUNDALI jsundali@unr.nevada.edu Managerial Sciences/028, University of Nevada, Reno, Reno, NV 89557 Abstract Research on decision making under uncertainty demonstrates that intuitive ideas of randomness depart systemati- cally from the laws of chance. Two such departures involving random sequences of events have been documented in the laboratory, the gambler’s fallacy and the hot hand. This study presents results from the field, using videotapes of patrons gambling in a casino, to examine the existence and extent of these biases in naturalistic settings. We find small but significant biases in our population, consistent with those observed in the lab. Keywords: perceptions of randomness, uncertainty, field study JEL Classification: C9 Experimental, C93 Field Experiments, D81 Decision Making under Risk and Uncertainty The emerging field of behavioral economics uses regularities from experimental data to predict and explain real-world behavior. However, few studies demonstrate the persistence of experimentally-observed biases in natural settings. This study uses data from patrons gambling in a casino to test the robustness of two biases that have previously been observed in the lab: the gambler’s fallacy and the hot hand. The gambler’s fallacy is a belief in negative autocorrelation of a non-autocorrelated random sequence. For example, imagine Jim repeatedly flipping a (fair) coin and guessing the outcome before it lands. If he believes in the gambler’s fallacy, then after observing three heads his subjective probability of seeing another head is less than 50%. Thus he believes a tail is “due,” that is, more likely to appear on the next flip than a head. In contrast, the hot hand is a belief in positive autocorrelation of a non-autocorrelated random sequence. For example, imagine Rachel repeatedly flipping a (fair) coin and guess- ing the outcome before it lands. If she believes in the hot hand, then after observing three correct guesses in a row her subjective probability of guessing correctly on the next flip is higher than 50%. Thus she believes that she is “hot” and more likely than chance to guess correctly. Notice that these two biases are not simply inverses of each other. In particular, the gambler’s fallacy is based on beliefs about outcomes like heads or tails, the hot hand on