Base rate fallacy definition

Base Rate Fallacy occurs when we are too quick to make judgements ignoring base rates, or probabilities in favour of new information. There is a famous cab driver problem illustrated by the behavioural psychologist, and Nobel laureate, Daniel Kahneman, which demonstrates this phenomenon clearly. 

Within an experiment, individuals are presented with the following statistics: 85% of cabs in a city are blue, and 15% are green. Then they are given a second piece of information which is that a witness identified the cab as blue; Afterwards, they are told that the reliability of the witness was judged to be correct only 80% of the time, and so wrong 20% of the time. The participants were then asked what is the likelihood that the cab involved in the accident was blue rather than green. Ignoring the initial statistics, people said that there is an 80% chance for the car to be blue. This is an example of base rate fallacy because people completely neglected the initial base rate presented in the problem, i.e. that 85% of the cabs are blue and 15% are green. The problem should have been solved using Bayes' rule and combining the two probabilities which gives a correct answer of 41%. 

Accordingly, Base Rate Neglect is individuals' tendency to misjudge the likelihood of a situation by not considering the statistics presented, but by focusing more heavily on the last piece of information available.  

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