Your class interpretation is practical but technically wrong. Your initial thought is correct. We cannot draw probabilistic conclusions about the true value because it is fixed and unknown. We draw conclusions about the interval and we then extrapolate from that. Statistically all we say is that over repetitions of producing a 99% confidence intervals, 99% of them will include the true value. Now we lose our ability to speak about probabilities but we can still draw some logical conclusions. I have one 99% CI. It’s rather unlikely that I happened to pull the 1/100 that doesn’t include the population parameter. Therefore, this interval is a good viable range for the population parameter More on reddit.com

Which standard error are you using? More on reddit.com

Here’s my answer; feel free to correct if wrong. I’d recommend evaluating using Excel or your favorite programming language to solve for n. More on reddit.com

The literal default will produce Wald CIs: confint.default(health.logit, level=.99) However, the command you are using — i.e., confint() — will produce the profile likelihood CI. You can run both and see if there is a difference, but generally they will be very similar. If there is a difference, it is likely driven by a small sample size and PL is superior. (PL is generally superior in most cases but the difference becomes less meaningful in larger samples.) I can't cite documentation that PL is being used here, but you can just run this model in another stats package where you can verify the method (e.g., SAS or Stata). Edit: Formatting. More on reddit.com