Base Rate Fallacy

The concept and classic experiments

Meehl and Rosen (1955) had already warned, when assessing the efficiency of psychological tests, that if the trait being detected is rare, judgments based on test results alone can go badly wrong. Kahneman and Tversky (1973) gave participants a personality sketch of a person drawn at random from a group of 70 engineers and 30 lawyers, or the reverse, and asked how likely the person was to be an engineer. Participants gave nearly the same probabilities whichever group composition they were told, judging only by how well the sketch fit the stereotype of an engineer. This led to the interpretation that judgment by the representativeness heuristic crowds out base rates. Nisbett and Borgida (1975) likewise reported that people weigh individual cases far more heavily than statistical consensus information.

The taxicab problem and Bayesian structure

In the taxicab problem discussed by Tversky and Kahneman (1982), 85% of a city's cabs are green and 15% are blue, and a witness who is 80% accurate testifies that the cab in an accident was blue. Many people put the probability that the cab was blue at 80%, but by Bayes' theorem it is only about 41%. Bar-Hillel (1980) showed that base rates are neglected less when they seem causally relevant to the case or feel more specifically tied to it, and Ajzen (1977) likewise reported that causal base rates are better reflected in judgments. Casscells, Schoenberger and Graboys (1978) and Eddy (1982) presented cases in which experts made similar errors in interpreting positive test results for rare conditions, showing that the fallacy is not limited to laypeople.

The frequency format debate and theoretical issues

Gigerenzer and Hoffrage (1995) showed that presenting the same problem in natural frequencies, such as how many out of 1,000 people, rather than probabilities greatly raises accuracy, and Cosmides and Tooby (1996) proposed an ecological rationality view in which the human mind evolved to process frequency information. Koehler (1996) critically reexamined the literature, arguing that base rates in experiments are not always normatively appropriate for real-world judgments and that base rate neglect may have been overstated. Barbey and Sloman (2007) argued that the frequency format effect comes from making nested-set relations transparent and is better explained by dual-process theory. Kahneman (2011) presented the fallacy as a feature of fast, intuitive System 1. Along with the gambler's fallacy and the availability heuristic, it is a classic example of the limits of probabilistic intuition.

FAQ

What is the base rate fallacy in simple terms?

It's the mistake of forgetting how common something is to begin with and judging probability from vivid details in front of you. For example, when fairly accurate evidence points to something very rare, people assume it's nearly certain.

Why do even experts make this mistake?

Combining information expressed as probabilities is hard to do intuitively. Research shows that judgments get much more accurate when the same information is presented as frequencies, such as how many out of 1,000 people.

Sources reviewed: 2026-10-26

References

1. Meehl, P. E., & Rosen, A. (1955). Antecedent probability and the efficiency of psychometric signs, patterns, or cutting scores. Psychological Bulletin, 52(3), 194–216.
2. Kahneman, D., & Tversky, A. (1973). On the psychology of prediction. Psychological Review, 80(4), 237–251.
3. Nisbett, R. E., & Borgida, E. (1975). Attribution and the psychology of prediction. Journal of Personality and Social Psychology, 32(5), 932–943.
4. Ajzen, I. (1977). Intuitive theories of events and the effects of base-rate information on prediction. Journal of Personality and Social Psychology, 35(5), 303–314.
5. Casscells, W., Schoenberger, A., & Graboys, T. B. (1978). Interpretation by physicians of clinical laboratory results. New England Journal of Medicine, 299(18), 999–1001.
6. Bar-Hillel, M. (1980). The base-rate fallacy in probability judgments. Acta Psychologica, 44(3), 211–233.
7. Tversky, A., & Kahneman, D. (1982). Evidential impact of base rates. In D. Kahneman, P. Slovic & A. Tversky (Eds.), Judgment under Uncertainty: Heuristics and Biases (pp. 153–160). Cambridge: Cambridge University Press.
8. Eddy, D. M. (1982). Probabilistic reasoning in clinical medicine: Problems and opportunities. In D. Kahneman, P. Slovic & A. Tversky (Eds.), Judgment under Uncertainty: Heuristics and Biases (pp. 249–267). Cambridge: Cambridge University Press.
9. Gigerenzer, G., & Hoffrage, U. (1995). How to improve Bayesian reasoning without instruction: Frequency formats. Psychological Review, 102(4), 684–704.
10. Cosmides, L., & Tooby, J. (1996). Are humans good intuitive statisticians after all? Rethinking some conclusions from the literature on judgment under uncertainty. Cognition, 58(1), 1–73.
11. Koehler, J. J. (1996). The base rate fallacy reconsidered: Descriptive, normative, and methodological challenges. Behavioral and Brain Sciences, 19(1), 1–17.
12. Barbey, A. K., & Sloman, S. A. (2007). Base-rate respect: From ecological rationality to dual processes. Behavioral and Brain Sciences, 30(3), 241–254.
13. Kahneman, D. (2011). Thinking, Fast and Slow. New York: Farrar, Straus and Giroux.