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Numbers to think about
Let's assume that the labs and their tests are 99% accurate.
The UCI did around 12000 tests last year, and about 380 came back positive. These are just rough numbers off the top of my head. It worked out to around 3.8% of all tests came back positive. So, if you take that 99% accuracy number and apply it, you end up with roughly 1 out of 3 positives due to bad testing. |
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#3
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Numbers to think about
CowPunk wrote:
Let's assume that the labs and their tests are 99% accurate. The UCI did around 12000 tests last year, and about 380 came back positive. These are just rough numbers off the top of my head. It worked out to around 3.8% of all tests came back positive. So, if you take that 99% accuracy number and apply it, you end up with roughly 1 out of 3 positives due to bad testing. It's been a million years since I took a probability class so I must have just confused myself. Someone please straighten me out here. If the probability of a false positive is .01 then the probability of both A and B samples receiving a false positives is .01 * .01 = .0001. I think that means that ~1.2 times a year someone innocent should fail both the A and B sample despite being clean. That's got to be wrong. |
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Numbers to think about
1% of 12000 = 120
120:380 ~ 1:3 |
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#6
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If the probability of a false positive is .01 then the probability of both A and B samples receiving a false positives is .01 * .01 = .0001. I think No I said 99% accuracy. Errors could be based on mishandling sample, contamination, etc.... I just don't believe that a lab is 99.9% accurate in their work. We have 12000 test and 1% have a wrong result. 1% out of 12000 = 120. Yes 380 Positive * 1% = 3.8 ( so 3.8 out of 380 are clean guys called cheaters) So now you are applying 1% again. Which means you are calculating based 0.1% accuracy. 1%x1% Where we are diverging is you are applying 1% to the positives, while I am applying 1% to the total # of tests. IMHO, Accuracy of a test applies to the total # of tests performed. |
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#8
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Numbers to think about
CowPunk wrote:
Let's assume that the labs and their tests are 99% accurate. The UCI did around 12000 tests last year, and about 380 came back positive. These are just rough numbers off the top of my head. It worked out to around 3.8% of all tests came back positive. So, if you take that 99% accuracy number and apply it, you end up with roughly 1 out of 3 positives due to bad testing. I'm not a medical technician, and I don't play one on TV, but I have heard from reliable sources that the false-positive and false-negative rates in medical testing can be substantially different. For all I know, this might be the rule rather than the exception. An illustration with made-up numbers: Some test might have a false positive rate of 10% (10% of those who are really "negative" are deemed "positive" by the test) while only returning a 3% false negative rate (only 3% of thoses truly "positive" are "missed" by the test). Again, these numbers are entirely made up, only to illustrate the phenomenon. Mark |
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