The question is: A test for heart disease results in a false positive 5% of the time. 25% of the population has heart disease and 20% test positive. Given a negative test, what is the probability the patient does not have heart disease?
Let N=the event that a person doesn't have heart disease, -=the event that a person tests negative for heart disease
$$P[N|-]=\frac{P[N \cap -]}{P[-]}=\frac{P[-|N]P[N]}{P[-]}=\frac{.95*.75}{.8}=.89$$
However, that is wrong, and I am confused as to why.
The correct solution is as follows:
Let event H be heart disease, N be no heart disease and event + be a positive test. $$(N)=(N|+)∗(+)+(N|−)∗(1−(+))$$ $$.75=.05∗.20+∗(1−.20)$$ $$.75=.01+.80$$ $$.74/.80=$$ $$=.925%$$
To be clear, this solution makes sense to me. My issue is that I don't understand why my original solution was wrong.
I suppose key to understanding why I was wrong is, is the probability of a true negative equal to 1 minus the probability of a false positive?
To begin with, the problem statement is a little bit ambiguous. "A test for heart disease results in a false positive 5% of the time." Is that $5\%$ of all tests or $5\%$ of the tests performed on people without heart disease? A literal reading suggests the first interpretation, but the second interpretation is what is usually measured. So let's assume the second interpretation.
Translating this interpretation into a formula. $$ P(+ \mid N) = 0.05. $$
The "correct" solution has instead assumed that $P(N \mid +) \stackrel?= 0.05$, which I cannot see any way to justify.
I think the resolution to the discrepancy here is that the "correct" solution is incorrect and that your "incorrect" solution is correct.
OK, I think I see the interpretation of the "correct" solution. They apparently thought that "A test for heart disease results in a false positive 5% of the time" means that $5\%$ of all positive results are false. I still think this is an absurd interpretation.