Comments on: An amazing new HIV test http://blog.richmond.edu/physicsbunn/2012/10/06/an-amazing-new-hiv-test/ Department of Physics Sun, 26 May 2013 02:40:46 +0000 hourly 1 http://wordpress.org/?v= By: Gary Larson http://blog.richmond.edu/physicsbunn/2012/10/06/an-amazing-new-hiv-test/comment-page-1/#comment-221982 Gary Larson Wed, 10 Oct 2012 17:22:32 +0000 http://blog.richmond.edu/physicsbunn/?p=565#comment-221982 I think he is poking fun at the wording used in the article. The wording points at an individual. If Joe is not infected and the test indicates that Joe is not infected, I'd say the test (i.e. Joe's test) is 100% accurate. On the other hand, if Susie does have the virus and the test indicates Susie does not have the virus, well, that test was 0% accurate. I think he is poking fun at the wording used in the article. The wording points at an individual. If Joe is not infected and the test indicates that Joe is not infected, I’d say the test (i.e. Joe’s test) is 100% accurate. On the other hand, if Susie does have the virus and the test indicates Susie does not have the virus, well, that test was 0% accurate.

]]> By: Phillip Helbig http://blog.richmond.edu/physicsbunn/2012/10/06/an-amazing-new-hiv-test/comment-page-1/#comment-221749 Phillip Helbig Mon, 08 Oct 2012 09:56:48 +0000 http://blog.richmond.edu/physicsbunn/?p=565#comment-221749 Of course, if you want to test someone, their prior probability of being infected is probably higher than 0.6%, so adjust accordingly. However, the impression that 7% of the time when there is a negative result, someone is actually infected is still wrong. Of course, if you want to test someone, their prior probability of being infected is probably higher than 0.6%, so adjust accordingly. However, the impression that 7% of the time when there is a negative result, someone is actually infected is still wrong.

]]> By: Phillip Helbig http://blog.richmond.edu/physicsbunn/2012/10/06/an-amazing-new-hiv-test/comment-page-1/#comment-221742 Phillip Helbig Mon, 08 Oct 2012 08:24:34 +0000 http://blog.richmond.edu/physicsbunn/?p=565#comment-221742 A couple of comments on the article demonstrate what I was talking about: a misperception and its Bayesian correction: <B>So in other words, if you both test negative, it's ok to forget the condoms...but oops ...oh I'm sorry, there's was a 7 percent chance that this wasn't true..and ooops now I have infected you. * My bad, dude.*</B> <I>No. The infection rate in the US is 0.6%. From above, perhaps 10% will lie. Let's say 10% don't know, so 20% of the infected are "dangerous". 0.006 * 0.2 * 0.07 = 0.000084. So if you use this test and have no skill at detecting liars, then you're taking a 0.0084% chance of, not catching AIDS, but being exposed to AIDS.</I> A couple of comments on the article demonstrate what I was talking about: a misperception and its Bayesian correction:

So in other words, if you both test negative, it’s ok to forget the condoms…but oops …oh I’m sorry, there’s was a 7 percent chance that this wasn’t true..and ooops now I have infected you. * My bad, dude.*

No. The infection rate in the US is 0.6%. From above, perhaps 10% will lie. Let’s say 10% don’t know, so 20% of the infected are “dangerous”. 0.006 * 0.2 * 0.07 = 0.000084. So if you use this test and have no skill at detecting liars, then you’re taking a 0.0084% chance of, not catching AIDS, but being exposed to AIDS.

]]> By: Phillip Helbig http://blog.richmond.edu/physicsbunn/2012/10/06/an-amazing-new-hiv-test/comment-page-1/#comment-221732 Phillip Helbig Mon, 08 Oct 2012 07:07:09 +0000 http://blog.richmond.edu/physicsbunn/?p=565#comment-221732 In other words (assuming 99.9% for "almost 100"): Of course, we have 4 cases: someone is infected and the test say he is or isn't and the same for someone not infected. Suppose someone takes the test, not knowing his actual status. In each case (test indicates infection or not), what is the probability that the result is correct? (Note: there is one piece of information missing.) In other words (assuming 99.9% for “almost 100″): Of course, we have 4 cases: someone is infected and the test say he is or isn’t and the same for someone not infected. Suppose someone takes the test, not knowing his actual status. In each case (test indicates infection or not), what is the probability that the result is correct? (Note: there is one piece of information missing.)

]]> By: Phillip Helbig http://blog.richmond.edu/physicsbunn/2012/10/06/an-amazing-new-hiv-test/comment-page-1/#comment-221731 Phillip Helbig Mon, 08 Oct 2012 07:02:54 +0000 http://blog.richmond.edu/physicsbunn/?p=565#comment-221731 Since this is the textbook example demonstrating the role of prior probability in Bayesian analysis, I was certain this post would be a demonstration of the proper way to interpret such numbers. Since this is the textbook example demonstrating the role of prior probability in Bayesian analysis, I was certain this post would be a demonstration of the proper way to interpret such numbers.

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