Gambler's Fallacy

The gambler's fallacy—the mistaken belief that past random events influence the probability of future independent ones—causes more financial losses than any other bias on this list by direct comparison to #8's anchoring effect. The most famous real-world example is the 1913 Monte Carlo Fallacy: the roulette ball at Casino de Monte-Carlo landed on black 26 consecutive times, and gamblers lost millions betting on red, convinced a correction was "due." The ball had no memory; each spin was independent, with a 47.4% chance of landing on red every time. This fallacy drives lottery players to avoid "recent" numbers, leads judges to grant 15% fewer paroles after a run of approvals, and fuels the common investment pattern of selling winners to "balance" a portfolio. Unlike other biases, it exploits our hardwired pattern-seeking brains, creating predictable decision errors in gambling, justice, and finance.