Approximate Is Better than ``Exact'' for Interval Estimation of Binomial Proportions
A. Agresti, and B. Coull. The American Statistician, 52 (2):
pp. 119-126(1998)
Abstract
For interval estimation of a proportion, coverage probabilities tend to be too large for "exact" confidence intervals based on inverting the binomial test and too small for the interval based on inverting the Wald large-sample normal test (i.e., sample proportion ± z-score × estimated standard error). Wilson's suggestion of inverting the related score test with null rather than estimated standard error yields coverage probabilities close to nominal confidence levels, even for very small sample sizes. The 95% score interval has similar behavior as the adjusted Wald interval obtained after adding two "successes" and two "failures" to the sample. In elementary courses, with the score and adjusted Wald methods it is unnecessary to provide students with awkward sample size guidelines.
%0 Journal Article
%1 agresti1998approximate
%A Agresti, Alan
%A Coull, Brent A.
%D 1998
%I American Statistical Association
%J The American Statistician
%K confidence_interval statistics teaching
%N 2
%P pp. 119-126
%T Approximate Is Better than ``Exact'' for Interval Estimation of Binomial Proportions
%U http://www.jstor.org/stable/2685469
%V 52
%X For interval estimation of a proportion, coverage probabilities tend to be too large for "exact" confidence intervals based on inverting the binomial test and too small for the interval based on inverting the Wald large-sample normal test (i.e., sample proportion ± z-score × estimated standard error). Wilson's suggestion of inverting the related score test with null rather than estimated standard error yields coverage probabilities close to nominal confidence levels, even for very small sample sizes. The 95% score interval has similar behavior as the adjusted Wald interval obtained after adding two "successes" and two "failures" to the sample. In elementary courses, with the score and adjusted Wald methods it is unnecessary to provide students with awkward sample size guidelines.
@article{agresti1998approximate,
abstract = {For interval estimation of a proportion, coverage probabilities tend to be too large for "exact" confidence intervals based on inverting the binomial test and too small for the interval based on inverting the Wald large-sample normal test (i.e., sample proportion ± z-score × estimated standard error). Wilson's suggestion of inverting the related score test with null rather than estimated standard error yields coverage probabilities close to nominal confidence levels, even for very small sample sizes. The 95% score interval has similar behavior as the adjusted Wald interval obtained after adding two "successes" and two "failures" to the sample. In elementary courses, with the score and adjusted Wald methods it is unnecessary to provide students with awkward sample size guidelines.},
added-at = {2013-11-06T15:26:05.000+0100},
author = {Agresti, Alan and Coull, Brent A.},
biburl = {https://www.bibsonomy.org/bibtex/2f32194139c39d573b7e30794d57c5bf8/peter.ralph},
interhash = {7a4eb85e4242285c36a42b7f33227a03},
intrahash = {f32194139c39d573b7e30794d57c5bf8},
issn = {00031305},
journal = {The American Statistician},
keywords = {confidence_interval statistics teaching},
language = {English},
number = 2,
pages = {pp. 119-126},
publisher = {American Statistical Association},
timestamp = {2013-11-06T15:26:05.000+0100},
title = {Approximate Is Better than ``Exact'' for Interval Estimation of Binomial Proportions},
url = {http://www.jstor.org/stable/2685469},
volume = 52,
year = 1998
}