This is not just medical tests - you use the same criteria to evaluate any binary classifier.<p>“Precision and recall” are positive predictive value and sensitivity, respectively.
saying a test has 85% accuracy tells you nothing about the results of that test. not being hyperbolic - literally nothing. simple proof:<p>i develop a test for disease X and give it to 1 billion walking around grocery stores in the whole world. it is 85% accurate. results are 200 million people are positive? how many of those people actually have disease X? do you have a guess?<p>disease X i was testing for was death. every single positive test was wrong. how close was your guess?<p>you must, at a minimum, know test accuracy _as well as_ disease prevalence to form a statistical guess. death is prevalent in 0% of alive people, so test accuracy is worthless.
Wikipedia article on the subject: <a href="https://en.wikipedia.org/wiki/Sensitivity_and_specificity" rel="nofollow">https://en.wikipedia.org/wiki/Sensitivity_and_specificity</a>