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Usa hits 1.3 mill cases New york city the new epicenter of the virus


Moldoveanu
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It's a Bayesian stats problem, I started a thread about it before. Math aside, teddy understands the concept.

 

Boat say virus test is 99% accurate and 1% of people have virus and we test 1ña bunch of people and you get a positive result. What is the chance you actually have the virus (ie not a false positive). Or what are the chances you do have the virus but test negative?

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It's a Bayesian stats problem, I started a thread about it before. Math aside, teddy understands the concept.

 

Boat say virus test is 99% accurate and 1% of people have virus and we test 1ña bunch of people and you get a positive result. What is the chance you actually have the virus (ie not a false positive). Or what are the chances you do have the virus but test negative?

if we're doing 7-800K tests a day, how many false positives are there(approx)? And i dont think current tests are 99%, more like 95-98.

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98% and quite abit of change

I will spare the math for this thread but it's more like 50%.

 

Consider 100 tests (1 person has it). Tests will return positive on that person 99% of the time. The other 99 are negative 1% of them will test positive (approx 1 person). So 2 people have tested positive but only one has it.

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What I'm having trouble wrapping my mind around out of all the test that initially show incorrect results is there a higher percentage of those that shows and incorrect positive then there are ones that show an incorrect negative

Because most people do not in fact have the Coronavirus.

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