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« No Cryptographers Meeting This Wednesday (6/28) | Main | Resolution To The Problem »

June 27, 2006

Comments

Maybe I'm oversimplifying this, but I don't see how Bayes applies here. If 9.6% of positive mammographies are false, that implies that 90.4% of them are true; so there ought to be a 90.4% chance that she actually has cancer. The only thing that potentially complicates this is if the 80% and 9.6% numbers apply to all women, as opposed to women in the age group.

Now if I run the numbers through Bayes, and I'm almost positive that I'm running them incorrectly, I get only about a 7.76% chance that the woman in question has breast cancer. It seems that the way I plugged the numbers, Bayes overvalues the 1% chance if we have no additional information.

I still say the 90.4% value follows from The 9.6% figure. If 9.6% of positives are false, then the remaining 90.4% of positives must be true, meaning there's a 90.4% chance that the woman in question has cancer.

I don't think that this quite fits the Bayes you talked about earlier. We know that the women has a positive scan, whihc, I think, invalidates the 1% probabality, becasue the mere presence of a scan immediately takes her out of the set of all women and puts her into the set of women who have had a mammogram. We aren't talking about the two bags of marbles anymore, but one bag of marbles and what the odds are of pulling a white one.

Or, at least, thats the way it seems to me. I lvoe stats. I need to get a decent refresher book ...

As I point out in the next thread, nevermind. :)

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