Confidence Intervals for Proportions, n<30

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In summary, the conversation discusses the challenge of finding confidence intervals for population proportions with small sample sizes. Possible solutions mentioned include simulations, t-distributions, and using the binomial distribution with a one-sided upper-tail confidence interval. The conversation ends with a suggestion to solve for the probability of observing a certain number of data with a property in a sample of a given size.
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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.
 

What is a confidence interval for proportions with n<30?

A confidence interval for proportions with n<30 is a range of values that is likely to include the true proportion of a population, based on a sample size less than 30. It is used to estimate the true proportion with a certain level of confidence.

Why is n<30 important in calculating confidence intervals for proportions?

N<30 is important because it indicates a small sample size, which can lead to a less accurate estimate of the true proportion. This is why a different calculation method is used for confidence intervals with n<30 compared to larger sample sizes.

What is the formula for calculating a confidence interval for proportions with n<30?

The formula for calculating a confidence interval for proportions with n<30 is:
Lower bound = p - z * (sqrt(p*(1-p)/n))
Upper bound = p + z * (sqrt(p*(1-p)/n))
Where p is the sample proportion and z is the z-score associated with the desired confidence level.

What is the purpose of a confidence level in a confidence interval for proportions with n<30?

The purpose of a confidence level is to determine the likelihood that the true proportion falls within the calculated confidence interval. For example, a 95% confidence level means that there is a 95% chance that the true proportion falls within the calculated interval.

How does increasing the sample size affect the width of a confidence interval for proportions with n<30?

Increasing the sample size will decrease the width of the confidence interval for proportions with n<30. This is because a larger sample size will lead to a more accurate estimate of the true proportion, resulting in a narrower range of values in the confidence interval.

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