# Sample proportion confidence interval estimates using logit

This seems like a problem that has an accepted, statistically and mathematically sound answer, but I can't seem to find it.

When estimating confidence intervals from sample proportions, I generally use the normal approximation technique described here: https://en.wikipedia.org/wiki/Binomial_proportion_confidence_interval#Normal_approximation_interval

However, this fails spectacularly for proportions where my sample is close to 0 or 1, notably having symmetrical distribution which causes it to go above 1 or below 0. Generally, since proportion estimates "behave better" when modeled using a logit, I assume there is some way to apply a logit transform to the confidence intervals which would result in an asymmetric confidence interval that would never cross 0 or 1.

However, instead of trying to hack together my own technique with freshman calculus and MBA statistics as my highest formal mathematical training, I have been searching the web to see if such a technique has already been described by someone more qualified.

Is anyone aware of a way to do this?