Accuracy of HIV test - probability

AI Thread Summary
The discussion revolves around calculating the probability of a person not having HIV when a test result is negative, given a test accuracy of 90% and a 2% false positive rate. Participants suggest using Bayes' theorem and contingency tables to approach the problem, with one contributor calculating the probability of an accurate result as 0.98. The conversation then shifts to the probability of a woman having HIV if the test result is positive, with differing interpretations on whether to factor in gender. Ultimately, the consensus leans towards treating the question generically, as there is no evidence suggesting that false positives vary by gender. The discussion highlights the complexities of interpreting probabilities in medical testing scenarios.
DannyCov
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Hi everone,

Really stuck on this one, if anyone has any suggestions I would be more than greatful!

Problem:
A HIV test detects at 90% accuracy
It falsly detects people as positive without HIV at 2%
and it is estimated that 50% of the tested patients have HIV

I need to work out the probability that a person does not have HIV and the test is negative?

... but first I am still confused to how to separate all the conditions, my teacher suggested using contingency tables but the ones I draw up don't make sense to me

Can anyone help?
 
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You know Bayes theorum?

anyhow,
probability that person is HIV negative=1/2
probability of false positive=0.02
therefore (1-.02)=probability of accurate result
product of .5(.98) is what I think you want.
 
Ah great thanks that's the way I approached it. The next thing they ask is very vague...
they ask the probability a woman has hiv if the result is positive?
No where else in the question do they say women are any more likely than men so I assume the answer to be another condional probability
P(woman,tumor, PositiveTest)= 0.5 * 0.5 * 90

is this reasonable?
 
hmmm, that is vague. I would simply exclude the sex reference and suggest 0.9
as there is no refernce to any notion that false positives are more likely in women than men, so i would interpret the question as a person...
 
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