[dschuler]

A test might have a high false positive rate, ie “low specificity”, but can still have “high sensitivity”.

That would mean that while you catch most cases of the virus, you’d also get a bunch of false positives. Flipping a coin as a hypothetical test would give false positives and negatives, or low specificity and low sensitivity.

[prepend]

Isn’t false negative specificity and false positive sensitivity?

https://en.wikipedia.org/wiki/Sensitivity_and_specificity

[boyd]

No, sensitivity is TPs / all positives (detected TP and FN). It’s the green half of the diagram in your link. The language is very easy to get tripped up by though. :)

[prepend]

I think I’m getting tripped by false positive, true positive.

A test with high sensitivity will have a low false positive rate.

A test with high specificity will have a low false negative.

So having a test with high sensitivity but low specificity will result in trust in the positives, but not trust in the negatives?

[dschuler]

For me it’s easier to think of sensitivity as being “sensitive to the true positive” without saying much about false positive or negative.

A sensitive test will catch many positive cases. It may or may not have false positives though, eg a test that’s always gives the right answer vs a test that always returns positive no matter what.

A specific test will give you few positive results when the true answer is negative. You could use it to rule something out. One test might say “patient has A or B condition”, and a second test with high specificity may then rule out A or B, leaving B or A, respectively, as the probable condition.