How to calculate confidence limits in bernoulli trials with only one outcome.

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To calculate confidence limits for Bernoulli trials with only one outcome observed, the standard method using the t-distribution fails due to a standard deviation of zero. The discussion highlights the need for alternative approaches, specifically mentioning the adjusted/modified Wald method and the LaPlace method as potential solutions. These methods can provide a way to estimate confidence intervals even when only one outcome is observed. The user seeks clarification on applying these methods effectively. Overall, the conversation focuses on overcoming the limitations of traditional confidence interval calculations in this specific scenario.
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I have this problem that I have been trying to figure out all week and can't seem to get.

I have a process that produces one of two outcomes "OK" (in some unknown porportion: p) or "NOT OK" (in porportion q = 1-p). Let's say I ran the process [x] times (like 20) in a row and it reported "OK" each time. How can I calculated a 1-sided (or 2-sided) 95% confidence interval for the value of p?

I think I should be assigning a value of "1" to "OK" and "0" to "NOT OK" and then applying the conventional confidence interval calculations using the t-distribution. The problem I run into is that all I observe are "OK" then my std deviation goes to 0 and the whole thing falls apart.

Is there another method? What am I missing?

Thanks, much appreciated.
 
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After more searching on this forum, I found a reference to something called the adjusted/modified wald method and the LaPlace method. It seems like those are good candidates to apply to this problem.
 
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