◐ Behavioral
◆ The PatternBelieving that past random events affect future probabilities.
The gambler's fallacy — believing past random events affect future probabilities.
Classic example: Monte Carlo, 1913 — black came up 26 times in a row. Gamblers bet massively on red, believing it was "due."
In markets:
• "It's fallen 5 days — it's due for a bounce" (each day is largely independent)
• "This strategy has lost 3 times — the next one must win"
• Confusing statistical expectation over many trials with the next single event
In markets:
• "It's fallen 5 days — it's due for a bounce" (each day is largely independent)
• "This strategy has lost 3 times — the next one must win"
• Confusing statistical expectation over many trials with the next single event
Independent events have no memory. Previous outcomes don't affect future outcomes.
Pattern bridge: Expecting past outcomes to influence independent future ones. In statistics, this is misunderstanding independent probability. In ML, each cross-validation fold is an independent trial.