Calculating Confidence Interval For A Proportion In One Sample

What would be a better way to calculate Confidence Interval (CI) for a proportion when the sample size is small and even the sample size is 1? I am currently calculating CI for a P

Solution 1:

Try statsmodels.stats.proportion.proportion_confint

http://www.statsmodels.org/devel/generated/statsmodels.stats.proportion.proportion_confint.html

According to their documentation, you use it like this:

ci_low, ci_upp = proportion_confint(count, nobs, alpha=0.05, method='normal')

Where the parameters are:

• count (int or array_array_like) – number of successes, can be pandas Series or DataFrame
• nobs (int) – total number of trials
• alpha (float in (0, 1)) – significance level, default 0.05

• method (string in ['normal']) – method to use for confidence interval, currently available methods:

  • normal : asymptotic normal approximation
  • agresti_coull : Agresti-Coull interval
  • beta : Clopper-Pearson interval based on Beta distribution
  • wilson : Wilson Score interval
  • jeffreys : Jeffreys Bayesian Interval
  • binom_test : experimental, inversion of binom_test