From my understanding, representative bias means that people tend to overweigh new information because they think that the new information is representative of the whole population. Sample-size neglect seems consistent with this definition. However, I don’t understand why base-rate neglect (the base rate or probability of the categorization is not adequately considered) is a type of representative bias.
CFAI uses Company ABC as an example of base-rate neglect. But in that example, what is the ‘new’ information? It seems that this example is more consistent with the definition in Wikipedia:http://en.wikipedia.org/wiki/Base_rate_fallacy
Representativeness bias is taking new information and putting into “buckets” because the information is similar or “representative” to what you have experienced in the past
Base-Rate Neglect is not adequately considering whether the logic for putting new information into “buckets” is correct. In the ABC case the FMP has “bucketed” Company ABC into a growth stock without adequately considering if it truly is a growth stock. The “new” information is simply the characteristics of Company ABC.
Sample-Size Neglect is taking a small sample size of new information and assuming that this is representative of the population as a whole.
Thank you Marathon_runner! I think your explanation of representative bias is much better than that in the curriculum (Perhaps because I am not a native speaker of English?).
Can you also help me understand base-rate neglect in mathematical terms? According to http://en.wikipedia.org/wiki/Base_rate_fallacy, base-rate neglect is wrongly equating P(A|B) = P(B|A) and neglecting P(A)/P(B) in the Bayes formula. Applying to Company ABC example, FMPs neglect P(growth) and are trying to estimate P(success|growth). Does that mean FMPs wrongly think that P(success|growth) = P(growth|sucess)?
I think it would go like this. The FMP would think like this:
P(ABC actually is growth stock | ABC looks like a growth stock) = 100% (if it looks like a growth stock then it IS a growth stock)… when in fact by Bayes:
P(ABC actually is growth stock | ABC looks like a growth stock) =
P(ABC looks like a growth stock | ABC actually is growth stock) x P(ABC actually is growth stock)/(ABC looks like a growth stock)
= 100% x (something less than 100%) / (100%) = something less than 100%
P(recover) is high but P(-ve news | recover) is low. And Verte neglects the base rate P(recover) and so wrongly concludes that P(recover | -ve news) is low.