Failure to Account for Base Rates

The persistent tendency to ignore the prior probability (base rate) of an event when evaluating new evidence—focusing on how well the evidence matches a scenario rather than how common the scenario is. A test for a rare disease is 99% accurate; you test positive. Most people estimate they probably have the disease. But if the disease affects 1 in 10,000 people, even with a 99% accurate test, the chance of actually having the disease is only about 1%. The base rate (1 in 10,000) dominates the calculation, but people ignore it because the test result (99% accurate!) is more vivid and feels more relevant.

When to use it

When evaluating diagnostic tests or screening results; when strong evidence seems to overwhelm prior probabilities; when any assessment needs to account for how common the assessed condition is in the population; when Bayesian updating should replace naive evidence interpretation.

How it can help

For any diagnostic or evaluative situation: ALWAYS start with the base rate before incorporating new evidence. The practice: 'before I saw this evidence, how common was this outcome in the general population?' Then update based on the evidence using Bayes' theorem. In hiring: before concluding a candidate is exceptional based on an interview (evidence), consider how many candidates in the pool would interview similarly well (base rate). In investing: before concluding a company is the next Google based on metrics (evidence), consider how many companies with similar metrics succeed (base rate).