| I don't think I have, it sounds like you think I'm saying false negatives are the inverse of false positives? Not at all, that's obviously not true. I was surprised at 'sensitivity and specificity' (jointly) being considered different from 'false negatives and false positives' (jointly). The given reasoning was about population differences, which.. fair enough, I understand that makes a difference, I just wasn't aware that was a standard difference in definition (if it is) and suggested the up thread commenter wasn't (or wasn't meaning to use it) either. > correctly identify presence of the condition 95% of the time, while correctly identifying absence of the condition 90% of the time. We can immediately answer the first question: when the answer is "yes", the test will say "no" 5% of the time. You have proposed that when the test says "no", the answer is "yes" a share of the time that might be the inverse of 5%, or perhaps 5% itself. I have no idea what you meant -- and I suspect you didn't either 10%. 'inverse', as I called it, of 90%, not 95%. (That's why I think you think I think (..!) that false negatives/positives rates are derivable from one another. Sorry if not and I'm just still not getting it...) I don't think I am misunderstanding though - Wikipedia calls them 'true pos/neg rate', and gives formulae for false pos/neg rates as 1-true:
https://en.m.wikipedia.org/wiki/Sensitivity_and_specificity |
No, I think you're saying that the false negative rate as I defined it in my comment is the inverse of sensitivity. You've corrected me to say that you think the rate I defined is the inverse of specificity, which makes even less sense. And you did that despite the fact that I included a full calculation demonstrating that that isn't true.
Wikipedia's definition of the false negative rate differs from mine. Wikipedia indeed defines the false negative rate as (1 - sensitivity), though not, as you seem to believe, (1 - specificity). But you get no credit for this, because I explicitly defined what I meant by the false negative rate, and you echoed that definition in your response to my comment:
>>> your changed-order definitions
So: you think you haven't misunderstood what's happening. I ask you this: in my table above, I believe that prevalence is 20%, sensitivity is 95%, and specificity is 90%. Please verify that.
I have said that the conditional probability P(condition present | test negative) is 1.4%. You responded saying that that probability is actually 10%:
>> You have proposed that when the test says "no", the answer is "yes" a share of the time that might be the inverse of 5%, or perhaps 5% itself.
> 10%
Where are you getting that figure from? Show me in the table.