Thanks for the question Steven. The brainteaser as posed is a hypothetical example. Any real world situations would have different data to start with and thus different results. A test might only be 90% effective, or 99.9% effective. A disease might affect one in 1,000 people, or 1 in 100,000 people. This would change the results. The point is that in order to understand false positives, we need to take into account the effectiveness of the test and how rare the thing being tested is.