- The gambler’s fallacy (belief that there will be a corrective bias, e.g. “luck must break”)
- Belief in small numbers (that sample sets are very alike and highly representative of the population)
- Expectation that randomness does not carry patterns
- Belief that portions of a distribution represent the distribution (like the second above)
Ignoring base data, i.e. prior probabilities, in determining likelihoods
- Ignoring the prediction of regression (it is unlikely that phenomenal performance will be followed by greater performance)
I’m interested to see if and where these biases are ingrained in formal decision making systems in both business and public policy.