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One fallacy that seems universal with healthcare folks is they think the false positive rate is the chance that a given positive result is erroneous. If an illness is rare, a positive result in a test with a 1% error rate might have an overwhelming probability of being a false positive. This is why prior probabilities need to be taken into account in making decisions.
One fallacy that seems universal with healthcare folks is they think the false positive rate is the chance that a given positive result is erroneous. If an illness is rare, a positive result in a test with a 1% error rate might have an overwhelming probability of being a false positive. This is why prior probabilities need to be taken into account in making decisions.
>If an illness is rare, a positive result in a test with a 1% error rate might have an overwhelming probability of being a false positive. Can you elaborate on this a little more...?
Here is someone else explaining it.
https://betterexplained.com/articles/an-intuitive-and-short-...
One fallacy that seems universal with healthcare folks is they think the false positive rate is the chance that a given positive result is erroneous. If an illness is rare, a positive result in a test with a 1% error rate might have an overwhelming probability of being a false positive. This is why prior probabilities need to be taken into account in making decisions.
>If an illness is rare, a positive result in a test with a 1% error rate might have an overwhelming probability of being a false positive. Can you elaborate on this a little more...?
One fallacy that seems universal with healthcare folks is they think the false positive rate is the chance that a given positive result is erroneous. If an illness is rare, a positive result in a test with a 1% error rate might have an overwhelming probability of being a false positive. This is why prior probabilities need to be taken into account in making decisions.
>If an illness is rare, a positive result in a test with a 1% error rate might have an overwhelming probability of being a false positive. Can you elaborate on this a little more...?
One fallacy that seems universal with healthcare folks is they think the false positive rate is the chance that a given positive result is erroneous. If an illness is rare, a positive result in a test with a 1% error rate might have an overwhelming probability of being a false positive. This is why prior probabilities need to be taken into account in making decisions.
>If an illness is rare, a positive result in a test with a 1% error rate might have an overwhelming probability of being a false positive. Can you elaborate on this a little more...?
Earlier quoted context omitted.
>If an illness is rare, a positive result in a test with a 1% error rate might have an overwhelming probability of being a false positive. Can you elaborate on this a little more...?
If you test 1000 people with a test which has a 1% false positive rate (lets ignore false negatives), but only one person in the 1000 is really infected you get about 11 positive tests results of which 10 are wrong.
One fallacy that seems universal with healthcare folks is they think the false positive rate is the chance that a given positive result is erroneous. If an illness is rare, a positive result in a test with a 1% error rate might have an overwhelming probability of being a false positive. This is why prior probabilities need to be taken into account in making decisions.
I don’t follow but am interested in your point. Please elaborate.
In this case, every positive test you observe is around 10x more likely to be a false positive than a true positive.
Earlier quoted context omitted.
Or think about it this way, this means as a percentage of real people infected, the mortality rate of COVID-19 is higher than thought. This false-positive rate means that the number of real cases is lower than reported and therefore mortality rate is higher. In other words, COVID-19 is more dangerous than thought if you catch it. Then again, I'm sure the actual cases dwarfs those reported since not everyone has been…
Mortality is almost always much lower than the initially reported and observed numbers. There could be many more people that never even get to the testing phase.
Earlier quoted context omitted.
>If an illness is rare, a positive result in a test with a 1% error rate might have an overwhelming probability of being a false positive. Can you elaborate on this a little more...?
If a given test has a 1% chance of returning true, even when the actual result is false, then from a sample of say 1000 tests we would expect at least 10 trues, in addition to any actual true results. If the chance of having the disease in the general population is low (say 1 in a thousand for this example) then we would expect 11 true results in our thousand samples. Of which 91% are incorrect results - false positi…
If the false positive rate is higher than the expected rate of disease in a given community, then the majority of positive tests will be false positives.
Does this relate to COVID in any way? Since the rates among affected communities seem to be growing rapidly. Would appreciate your thoughts.
Interesting, as this means that China would be quarantining more people than "necessary", which would help slow the pandemic anyway. I can't imagine an asymptomatic person would put stress on the hospital system? But maybe I'm wrong there. I am curious if this also could indicate a false-positive problem with non asymptomatic people as well. False-positives are also why the CDC tests had to be shipped back, although…
> I can't imagine an asymptomatic person would put stress on the hospital system? If s/he is in the hospital in quarantine you must give blankets and food, probably a nurse to check the temperature and symptoms two or three times per day, a medical doctor one a day just to be sure. Perhaps a blood analysis from time to time? Luckily you don't have to handle visitors because they are in quarantine. (Or there are some…