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

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    Bernoulli Limits
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SUMMARY

This discussion focuses on calculating confidence limits for Bernoulli trials with a single outcome, specifically when only "OK" results are observed. The user seeks to determine a 95% confidence interval for the probability of "OK" (p) after running the process multiple times, resulting in a challenge due to a standard deviation of zero. The conversation highlights the inadequacy of traditional t-distribution methods in this scenario and introduces the adjusted Wald method and the LaPlace method as viable alternatives for calculating confidence intervals under these conditions.

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  • Understanding of Bernoulli trials and binary outcomes
  • Familiarity with confidence interval calculations
  • Knowledge of the adjusted Wald method for confidence intervals
  • Basic statistics concepts, including standard deviation and t-distribution
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  • Research the adjusted Wald method for calculating confidence intervals in Bernoulli trials
  • Explore the LaPlace method and its application in statistical analysis
  • Learn about alternative methods for confidence interval estimation when facing zero variance
  • Study the implications of using different distributions for confidence interval calculations
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Statisticians, data analysts, and researchers involved in probability theory and statistical inference, particularly those working with binary outcome data and confidence interval estimation.

azira
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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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