A Comprehensive Comparison of the Wald, Wilson, and adjusted Wilson Confidence Intervals for Proportions

The standard confidence interval for a population proportion covered in the overwhelming majority of introductory and intermediate statistics textbooks surprisingly remains the Wald confidence interval despite having a poor coverage probability, especially for small sample sizes or when the unknown population proportion is close to either 0 or 1. Using the mean coverage probability, and for some sample sizes, Agresti and Coull showed not only that the 95% Wilson confidence interval performs better, but also showed that 95% adjusted Wilson of type 4 confidence interval, obtained by simply adding four pseudo-observations, outperforms both the Wald and the Wilson confidence intervals. In this paper, we introduce a rainbow color code and pixel-color plots as ways to comprehensively compare the Wald, Wilson, and adjusted-Wilson of type $\epsilon$ confidence intervals across all sample sizes $n=1,2,\dots ,1000$, population proportion values $p=0.01,0.02,\dots ,0.99$, and for the three typical confidence levels. We show not only that adding 3 (resp., 4 and 6) pseudo-observations is the best for the 90% (resp., 95% and 99%) adjusted Wilson confidence interval, but it also performs better than both the 90% (resp., 95% and 99%) Wald and Wilson confidence intervals.