> Here, it's closer to a random sample, but more importantly it shows really high rates. Those rates overwhelm any error due to false positives. I'm not

> Here, it's closer to a random sample, but more importantly it shows really high rates. Those rates overwhelm any error due to false positives. I'm not seeing that, at least not from what I've seen. Whether false positives skew results significantly is highly dependent on how accurate the antibody test they used is (in addition to how large a subset of the population is positive). This guy has an interesting visual tool that helps you to see how much a study could be affected. [1] Also, he says "Here's an interesting relationship. When a test with 95% sensitivity and 95% specificity is applied to a population with <5% prevalence of disease, MOST of the patients with positive tests are FALSE POSITIVEs." I.e., positive rate shows up as greater than 10%, when it's actually less than 5%. Do we know the sensitivity and specificity accuracies for the antibody tests used in NY? You'd like to think they know what they're doing. But this Bayesian stuff can be tricky, especially if they're rushing something through (esp. regarding testing of accuracy of the antibody tests themselves). And the California studies, although they seem to have some competent people behind them, seem to inflate/exaggerate the lower bound of uncertainty in their projection. [2] [1] https://sites.google.com/view/tgmteststat/home https://sites.google.com/view/tgmteststat/home [2] https://statmodeling.stat.columbia.edu/2020/04/19/fatal-flaws-in-stanford-study-of-coronavirus-prevalence/ https://statmodeling.stat.columbia.edu/2020/04/19/fatal-flaw...