> We tested 1,020 serum specimens collected prior to SARS-CoV-2 circulation in the United States and found one false positive
This gives 95% confidence intervals of between 0.0% and 0.5% false positive rate.
This gives 95% confidence intervals of between 0.0% and 0.5% false positive rate.
(More likely, 0.04% or similar is being treated as 0.0%, but I haven't done the maths to confirm what the number would be)
But, yes, you are entirely correct as to the practical meaning. 0% itself is impossible, but 0.0...1% could still happen.
For the things considered here, the point 0 is measure zero so open starting at 0 and closed at 0 are the same. L
Which can't lead to a "true 0%", since there's no such animal. But it could mean that all of the tests gave correct answers, in the trial.
> Specificity samples were derived from de-identified excess serum specimens sent to our clinical virology laboratory in 2018 and 2019.
So...not five years ago, and it is even possible that a significant number were from late 2019.
https://en.wikipedia.org/wiki/Binomial_proportion_confidence...
TLDR: whether you're a frequentist or Bayesian, the Jeffrey's interval is pretty good. Use Clopper-Pearson if you're a frequentist who is scared of undercovering.
I get 0.011% for the 2.5% quantile and 0.46% for the 97.5th quantile.