When is n large enough? Looking for the right sample size to estimate proportions. Issue 10 (3rd July 2019)

• Record Type: Journal Article
• Authors: Canals, Catalina; Canals, Andrea
• Is Part Of: Journal of statistical computation and simulation. Volume 89:Issue 10(2019)
• Page Start: 1887
• Page End: 1898
• Publication Date: 2019-07-03
• Subjects: Sample size -- proportion -- central limit theorem -- confidence interval -- Bernoulli distribution; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2019.1602125
• Languages: English
• ISSNs: 0094-9655

Abstract

ABSTRACT: The central limit theorem indicates that when the sample size goes to infinite, the sampling distribution of means tends to follow a normal distribution; it is the basis for the most usual confidence interval and sample size formulas. This study analyzes what sample size is large enough to assume that the distribution of the estimator of a proportion follows a Normal distribution. Also, we propose the use of a correction factor in sample size formulas to ensure a confidence level even when the central limit theorem does not apply for these distributions.