A Gambler’s Fallacy for Probability Judgments when Event Sequences Are Truly Random: Commentary on Xiang, Dorst, and Gershman (2025)

Xiang, Dorst, and Gershman (2025) recently challenged previous research and theory regarding the gambler’s fallacy. They concluded that when sequences of events are truly random, the fallacy is observed for binary predictions but not probability judgments and that binary predictions cannot be reconstructed from probability judgments, in contrast to most theoretical accounts. This “disconnection” implies that new theories may be needed. Based on reanalysis of Xiang et al.’s data using methods very similar to theirs, we argue that there is strong evidence for a gambler’s fallacy for probability judgments and that the gap between probability judgments and binary predictions was overstated. A version of Rabin and Vayanos’s (2010) model accounts for responses to both question types reasonably well, suggesting that such theories are not seriously threatened. Finally, we propose stratified random sampling of sequences as a more efficient method for presenting participants with representative sequences of events.