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base_rate_neglect
The tendency to ignore general prevalence information (base rates) when evaluating the probability of a specific event, especially when vivid individuating information is available. People focus on specific case details while ignoring how common or rare a condition is in the general population. This leads to dramatic probability estimation errors.
A medical test for a rare disease (affecting 1 in 10,000 people) has a 5% false positive rate. When a patient tests positive, both the patient and doctor may assume there is a 95% chance they have the disease, when in reality the base rate makes it far more likely to be a false positive.
A security algorithm flags a traveler as a potential threat based on a profile match. The agency treats the flag as near-certain evidence of guilt, but fails to account for the fact that genuine threats are extraordinarily rare among millions of travelers, meaning most flags are false positives.
A startup investor hears a passionate pitch and thinks the company has a 70% chance of success because the founder seems brilliant and the product is innovative. She ignores the well-documented base rate that roughly 90% of startups fail within ten years, regardless of founder quality.
Binary (yes/no) questions an LLM must answer to identify this aspect:
Are background statistics being ignored in favor of vivid case details?
Type: binaryIs the overall prevalence of the condition or event being considered?
Type: binaryWould the probability estimate change significantly if base rates were included?
Type: binaryThe tendency to ignore general prevalence information (base rates) when evaluating the probability of a specific event, especially when vivid individuating information is available. People focus on specific case details while ignoring how common or rare a condition is in the general population. This leads to dramatic probability estimation errors.
Vivid, specific, and emotionally engaging case information captures attention and feels more relevant than dry statistical base rates. The representativeness heuristic makes us judge by similarity rather than probability.
Always ask 'How common is this in the general population?' before interpreting specific evidence. Use Bayesian reasoning to combine base rates with new evidence systematically.
Base rate neglect is critical in medical diagnosis, criminal profiling, terrorism risk assessment, and fraud detection. It leads to overdiagnosis, wrongful convictions, and misallocated security resources.
Use these tools to detect, analyze, or train this aspect.