Seeking an example to help me understand the following:

Exhibiting representativeness, an analyst judges the probability of a forecast being correct on how well the available data represents the outcome. The analyst incorrectly combines:

  1. The probability that information fits a certain information category

  2. The probability that the category of information fits the conclusion

What is meant by how well the available data represents the outcome and what doe this if anything with points 1 &2.

Representativeness has two parts:

  1. Sample size neglect - assumes small sample sizes are representative of the population. this is what point 1 is refering to, and

  2. Base rate neglect - probability that the category was not correct when it was established. this is what point 2 is refering to.

The whole point of representativenes is to categorize information. The bias happens when the information is categorized incorrectly due to either of the two points above. This happens when the available data is not a good representation of the outcome.