[DangitBobby]

Isn't the false positive rate the percentage of positives that should have been negative? E.g. if I test 10000 people and 100 of them test positive, but really only 50 of those are sick (and almost none of the other 9900 people are), then I have a false positive rate of 50% and a very useful test. Obviously, this is very different from flipping a coin. And flipping a coin could also have a very high false negative rate.

[usrusr]

It's the rate of actual negatives that are perceived as positives.

I make that mistake myself all the time, What helps me avoid it is this: you want a number that is a property of the test method alone, independent of case distributions. A ratio between misidentified positives and identified positives (or true positives) would depend on sample distribution.

[monktastic1]

The metric you're talking about is called the "false discovery rate": https://en.wikipedia.org/wiki/False_discovery_rate

I also have a hard time keeping these all straight: https://en.wikipedia.org/wiki/Sensitivity_and_specificity#Ap...