several research papers that compare these and other confidence intervals for the binomial proportion. Both Agresti and Coull (1998) and Ross (2003) point out that exact methods such as the Clopper–Pearson interval may not work as well as certain approximations. The Normal approximation interval and its presentation in textbooks has been heavily criticised, with many statisticians advocating that it be not used. The principal problems are overshoot (bounds exceed [0, 1]), zero-width intervals at = 0 and 1 (falsely implying certainty), and overall inconsistency with significance testing. Of the approximations listed above, Wilson score interval methods (with or without continuity correction) have been shown to be the most accurate and the most robust, though some prefer the Agresti–Coull approach for larger sample sizes. Wilson and Clopper–Pearson methods obtain consistent results with source significance tests, and this property is decisive for many researchers. Many of these intervals can be calculated in R using packages like "binom". See also Binomial distribution#Confidence intervals Estimation theory Pseudocount References Statistical approximations Statistical intervals