Confidence Intervals for Proportions, n<30

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SUMMARY

This discussion focuses on calculating confidence intervals for population proportions when sample sizes are less than 30 (n<30). The suggested method involves using the binomial distribution to derive a one-sided upper-tail confidence interval. Specifically, the equation B^p_n(k-1)=1-α is used, where B^p_n represents the cumulative distribution function of the binomial distribution. This approach aligns with the logic applied in larger sample sizes, utilizing normal approximations for proportion distributions.

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  • Understanding of binomial distribution and its cumulative distribution function (CDF).
  • Familiarity with confidence interval concepts and their applications.
  • Basic knowledge of statistical inference and hypothesis testing.
  • Ability to perform calculations involving sample proportions and significance levels.
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  • Research the properties and applications of the binomial distribution in statistical analysis.
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  • Explore simulation techniques for estimating confidence intervals in small samples.
  • Study the differences between normal approximations and exact methods for confidence intervals.
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Statisticians, data analysts, and researchers dealing with small sample sizes in their studies, particularly those focused on estimating population proportions.

WWGD
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Hi All,
I am having trouble finding a good ref. for finding confidence intervals for population proportions
for small sample sizes; n<30. I have seen suggestions to use simulations, t-distributions, etc. .
Any ref. , please?
Thanks.
 
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No refs I'm afraid, but what about using the binomial distribution?

To get a ##\alpha##-quantile one-sided upper-tail confidence interval for the proportion of a population with property X when we have observed k data with the property in a sample of n, we could solve for ##p## the equation:

$$B^p_n(k-1)=1-\alpha$$

where ##B^p_n## is the cdf of a binomial distribution with parameters ##p,n##.

Then, if the population proportion with property X is ##p##, the probability of observing ##k## or more data with the property in a sample of size ##n## is ##\alpha##.

This is just trying to follow the same logic that I think I recall is used to set conf interval limits for larger samples, where a normal approximation can be used for the distribution of the proportion in the sample.
 
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Thanks,this works, I was thinking something along this lines but the person who asked me just wanted refs.
 

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