I am looking at a test strategy for Covid-19, where I want to combine two tests with different sensitivity and specificity.

Visualizing this would probably make sense in a "1-sensitivity" vs "1-specificity" graph. Is there a name for these quantities?

Informally (in particular in my head) I talk about false negative vs false positive rate, but I have already realized this is ambigous, since people will have different intuitions what I am normalizing to.


You can refer to $1-$ Sensitivity as the "false negative rate" (aka $\beta$ error or Type II error), and $1-$ Specificity as the "false positive rate" (aka $\alpha$ error or Type I error).

You can see it on the right-side box on the Wikipedia entry: https://en.wikipedia.org/wiki/Sensitivity_and_specificity

  • $\begingroup$ I like the wording, but from experience it is unclear what the denominator is: 1) all people tested or 2) all who were tested positive (ie people confuse with 1-NPV) or 3) all who are truly positive (1- sensitivity, what we want). $\endgroup$ – Jonas Heidelberg Dec 13 '20 at 8:42
  • $\begingroup$ But of course with the Wikipedia as a reference that will clear it up for most people ;-). I might actually start using „miss rate“ and „fallout“, which are given there as synonyms. Have you seen those used IRL? $\endgroup$ – Jonas Heidelberg Dec 13 '20 at 8:45
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    $\begingroup$ I've seen "false positive rate" and "false negative rate" used. I haven't seen "fallout" used, and I wouldn't really know what it refers to. "miss rate" I think is a bit clearer, but I still think you'd need to explain what you mean when you use it because it's not common. $\endgroup$ – Santiago Romero Brufau Dec 13 '20 at 16:55

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