It's 2021! Time for a crash course in four terms that I often see mixed up when people talk about testing: sensitivity, specificity, positive predictive value, negative predictive value.

These terms help us talk about how accurate a test is, but from different viewpoints. 1/

Viewpoint 1 is about the status of the person taking the test. Are they infected, or not infected? How good is the test at identifying these people? That's sensitivity/specificity. 2/

A test that is very *sensitive* will be very good at accurately identifying people who are infected.

A test that is very *specific* will be very good at accurately ruling out infection in people who are not infected. 3/

Viewpoint 2 is about the result of the test itself. It says positive or negative (or detected or not detected). How much can those results be trusted? Did the positive or negative actually "predict" the situation correctly? 4/

A test that has a high *positive predictive value* means you can really trust a positive. Most of the positives that come out do really mean that person is infected. 5/