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Published on: January 7, 2019
Setting sample size to ensure narrow confidence intervals for precise estimation of population values
1Penn State Erie, The Behrend College, Erie, Pennsylvania 16563, USA. ewcorty@gmail.com
Background:
Sample sizes set on the basis of desired power and expected effect size are often too small to yield a confidence interval narrow enough to provide a precise estimate of a population value.
Approach:
Formulae are presented to achieve a confidence interval of desired width for four common statistical tests: finding the population value of a correlation coefficient (Pearson r), the mean difference between two populations (independent- and dependent-samples t tests), and the difference between proportions for two populations (chi-square for contingency tables).
Discussion:
Use of the formulae is discussed in the context of the two goals of research: (a) determining whether an effect exists and (b) determining how large the effect is. In addition, calculating the sample size needed to find a confidence interval that captures the smallest benefit of clinical importance is addressed.
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