Description

The category size bias, a recent insight which has been studied primarily by Isaac and Brough, describes our tendency to believe outcomes are more likely to occur if they are part of a large category than part of a small category, even if each outcome is equally likely. This occurs because we understand that the largest category is more likely to be selected than any other category. We ignore the fact that if the small category is the correct category, each item in it is more likely to be picked than if it was a larger category. The category size bias skews our understanding of probabilities, making it difficult for those unaware of its effects to quickly and accurately analyze a situation.

Example

A study by Isaac and Brough asked people to estimate the probability of a specific lottery ball being picked. One group was provided with five each of white, grey and black balls while the other had categories of 2, 11, and 2 respectively. Group 1 predicted an 12.9% chance that grey ball 8 would be picked, while Group 2 gave the same ball an 18.5% chance on average. Since there were many grey balls in their study, each grey ball seemed more likely. In both cases, the real probability was 6.7% (1 out of 15).

Further Reading

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