Talk:False positive

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Mathematics

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OK, Can someone rewrite this so it makes sense to an ordinary person?

I second that - I clicked on "false positive" link elsewhere, and I get some esoteric stuff about Type I and II errors, null hypothesis and whatnot, without anything in between for a normal person.

All but the most hardcore statisticians will likely find this "information" hard to relate to.

Suggest removing redirect, instead keeping a "normal person" disambiguation-style page that says some thing like "False Positive means something that is incorrectly considered to fit a certain criteria, while in fact it doesn't", + a link to "See also - False Positive aka Type I Error (statistics)"

Looks like statisticians had a ball editing these articles, but forgot about the existence of non-statisticians.

p.s. For some normal-person definition examples, see false positive in Wikitionary and in The Free Dictionary - should mention usage for Medicine (person being diagnosed with a condition), Computers (email filters, antiviruses blocking something as bad or undesirable when in fact it it's legitimate) and other such uses.

184.144.109.155 (talk) 02:59, 25 August 2011 (UTC)[reply]

The article on false positives incorrectly defines a false positive rate as 1 minus the sensitivity. It is not. It is 1 minus the specificity.

I agree, according to the definition, specificity is (TP)/(TP + FN), where TP is the number of true positives and FN the number of true negatives. This makes (1 - spec) = (FN)/(TP + FN) which is the proportion of positives which were incorrectly labels. I'm fixing this page to reflect this.

MDReid

Let's keep an eye on this; vandals sometimes mess up the formulae in subtle ways.Bryan 21:58, 19 November 2005 (UTC)[reply]

False positives in computer searching

I added this section and some headings without disturbing the existing text, which is excellent. Could somebody supply a few references or external links? Bryan 12:19, 8 November 2005 (UTC)[reply]

Illustration?

Can somebody think of an illustration for this article? Bryan 21:58, 19 November 2005 (UTC)[reply]

Ya, an image of a black region on a white background. The black region is outlined and labelled, but some white spots show up inside it. These are labelled false positives. Some black spots appear in the white area. These are labelled false negatives. This is the general idea, but a more creative example would be good, or even better, an example from some real life classification image. Muxxa 10:29, 18 January 2006 (UTC)[reply]

Tradeoffs

I rewrote this: That is, an algorithm can often be made more sensitive at the risk of introducing more false positives, or it can be made more restrictive, at the risk of rejecting true positives. Took this out: The risk of Type I errors must be balanced against the risk of Type II errors (false negatives which fail to reject the null hypothesis when it is false). And Rewrote this: (Threshold) can be used to vary the tradeoff of an algorithm of how close a match to a given sample must be achieved before the algorithm reports a match. The higher this threshold, the fewer false positives and the more false negatives. Muxxa 10:29, 18 January 2006 (UTC)[reply]

MAJOR ERRATA

Even the errata in this discussion are incorrect. What a mess. Fortunately it is basic stuff with many web references for a skeptical Wikipedia reader.

Specificity = TN/(TN+FP)

False positive rate = 1 - Specificity

False positive vs. false negative

There has been a simple error of juxtaposition here which I have corrected.. False positive (the error of accepting something that should have been rejected) is a type II error. False negative (the error of rejecting something that should have been accepted) is a type I error. See, for example, Moulton, R.T., "Network Security", Datamation, Vol.29, No.7, (July 1983), pp.121-127:

The appropriate access control device may be difficult for the user to define. It must be capable of rejecting imposters while having a minimal rate of rejecting authorized users (Type I error). It must also have a high rate of accepting authorized users and a low rate of accepting imposters (Type II error).

I have also adjusted the redirections so that now has type I error to False negative; and Type II error to false positive. Once my piece on Four types of error is finished, and it will be finished very soon, the entire issue will be very plainLindsay658 08:07, 23 June 2006 (UTC)[reply]