Hi MM, Please let me be sure I understand the issue correctly.  It appears to me that (a) the binomial option of the tables statement in Proc Freq lets one calculate confidence intervals using various methods (exact, Wald, score, etc), but (b) in testing a hypothesis comparing an observed proportion to a hypothesized value (e.g., 0.07) that test is always the same, regardless of the method used for estimating confidence intervals.   So, for example, if you ask for a Clopper-Pearson confidence interval, SAS will calculate that; but if you also request a hypothesis test, SAS will always use an asymptotic (probably Wald) method. Is that what you've found?   I'll try to look into this further. Note, though, that if the hypothesized rate (0.07) falls outside the confidence limits for the observed data (however those are calculated), some people might take that as sufficient evidence to reject H0; not sure if that's strictly kosher, though. Come to think of it, I don't see why one couldn't just examine the binomial distribution for pi = 0.07 and the given sample size n.  If k observed successes falls in the lower or upper .025 area of that distribution, one might reject H0:pi =0.07.  SAS has built in functions for the cumulative binomial distribution, so one could perform a hypothesis test and get a p-value that way. What I'm also wondering at this point is whether Clopper-Pearson is only designed to supply confidence intervals, not to perform hypothesis tests. For the latter, exact binomial distributions might be better.  Hope this helps. -- John Uebersax ... View more