This is a pretty good explanation [1]. Let's say you have a test that's 80% accurate in telling you that you have cancer given you have cancer. Let's say the f

This is a pretty good explanation [1]. Let's say you have a test that's 80% accurate in telling you that you have cancer given you have cancer. Let's say the false positive rate is 10%. - If 100% of the population has cancer, then a positive test means you're 80% likely to have cancer. This makes sense. - If 10% of the population has cancer, then a positive test means you're only 50% likely to have cancer because of all the false positives in the general population. - If 1% of the population has cancer, then a positive test means you're only 7% likely to have cancer. - If 0.1% of the population has cancer, then a positive test means you're only 0.83% likely to have cancer. Basically, the false positives dominate the results once you start applying the test to a low-incidence population. Which is why we don't. [1] https://betterexplained.com/articles/an-intuitive-and-short-explanation-of-bayes-theorem/ https://betterexplained.com/articles/an-intuitive-and-short-...