"Tim Lines" a écrit dans le message de news:
...
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.

I too

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.

Let put it in other way.



We have 12000 test and 1% have a wrong result. 1% out of 12000 = 120.



In the 120 we have some Good Guys wrongly called cheaters, and some cheaters
called Good Guys

OK ?



Let see the distribution of this mistake :



380 Positive * 1% = 3.8 ( so 3.8 out of 380 are clean guys called cheaters)

12000-380= 11620 Negative * 1 % = 116.2 (so 116.2 are cheaters but found
Good guys.)



Let see if there is a mistake . 116.2 + 3.8 = 120



Ok 120 is what we expected.



In short around 4 out 380 or 1 out 95 are poor guys called cheaters but they
are not..

On other side 116 out of 11620 or around 1 out of 100 are lucky cheaters



Once again why did you said 1 out of 3 ????