I'm reading a book Statistics: An Introduction from Roger E. Kirk. Now I'm studding the chapter about conditional probability and thought that I understood the examples well, and then this one came.
I'll quote the book:
Common Errors in Applying the Rules of Probability
$3$. Using the multiplication rule for statistically independent events $p(A \cap B)=p(A)p(B)$, when the events are not statistically independent. Suppose the probability of seeing the product, event A, is 0.40 and the probability of buying the product, event B, is 0.30. If the dependency between A and B is ignored, the incorrect probability of both seeing an advertisement and buying the product is $p(A\cap B)=0.40\times 0.30=0.12$. The correct probability takes in to account the conditional probability of buying the product given that the ad has been seen, $p(B|A)=0.50$, so that $p(A \cap B)=p(A)p(B|A)=0.40 \times 0.50=0.20$.
First I'm not sure if I understand the problem correctly. Does this mean that the probability of buying a product is 0 if he doesn't see the ad, and 0.3 if event A happened, that is if the ad was seen?
Or does this mean that the probability of buying the product is 0.3 by chance even if he didn't see it?
Although I don't understand what the problem exactly states, if I used the formulas given for finding $$p(B|A)=p(A\cap B)/p(A)$$ I would get $0.4\times 0.3/0.4=0.3$, how did he get $0.5$?
There are lot of questions already asked about conditional probability, but it didn't help to understand this problem and the result he got. Is there any ambiguity in this problem as is originally stated in the book?
Since I can't comment, I'll put it here since I have no other way of interacting:
@Didier Piau yes, you're right, now that I see it I don't know why I wrote that.
@AMPerrine that is the source of my confusion I thought that he calculated p(B|A) somehow. Thank you for your answers.