AI engineering glossary · Probability
What is gambler's fallacy?
Gambler's fallacy: The mistaken belief that after a run of one result in independent trials, the opposite result becomes more likely to balance things out. Independent trials have no memory, so past results do not change future probabilities.
Gambler's fallacy: an example
After five heads in a row, the chance of heads on a fair coin's next toss is still 0.5.
Related terms
More Probability terms
- Geometric distribution
- Importance sampling
- Independence
- Intersection of events
- Inverse transform sampling
- Joint distribution
- Joint probability
- KL divergence
Where you learn it
Gambler's fallacy is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.