Further possibilities are "Wilson", "Agresti-Coull", and A confidence interval (CI) is a range of values, computed from the sample, which is Suppose n is the sample size, r the number of count of interested outcome, and p = r / n is so called binomial proportion (sample proportion). A. Agresti and B.A. The point estimate of the proportion, with the confidence interval as an attribute References Rao, JNK, Scott, AJ (1984) "On Chi-squared Tests For Multiway Contingency Tables with Proportions Estimated From Survey Data" Annals of Statistics 12:46-60. This function calculates confidence intervals for a population proportion. Coull, Approximate is better than "exact" for interval estimation of binomial proportions, American Statistician, 52:119–126, 1998. By default, "Clopper-Pearson" confidence intervals are calculated (via stats::binom.test). Newcombe, Logit confidence intervals and the inverse sinh transformation, 95 percent confidence intervalの項が区間推定範囲。 > binom.test ( 3 , 100 ) Exact binomial test data : 3 and 100 number of successes = 3 , number of trials = 100 , p - value < 2.2e-16 alternative hypothesis : true probability of success is not equal to 0.5 95 percent confidence interval : 0.006229972 0.085176053 sample estimates : probability of success 0.03 R.G. Interval Estimation for a Binomial Proportion Abstract We revisit the problem of interval estimation of a binomial proportion. Reference Chart for Precision of Wilson Binomial Proportion Confidence Interval Posted on October 16, 2015 by BioStatMatt in R bloggers | 0 Comments [This article was first published on BioStatMatt » R , and kindly contributed to R-bloggers ]. The erratic behavior of the coverage probability of the standard Wald confidence interval has previously been