title: Setting sample size to ensure narrow confidence intervals for precise estimation of population values.
abstract: 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.
  
  
  METHODS
  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).
  
  
  CONCLUSIONS
  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.
authors.affiliation: Penn State Erie, The Behrend College, Erie, Pennsylvania 16563, USA. ewcorty@gmail.com
authors.forename: Eric W
authors.initials: EW
authors.lastname: Corty
authors.forename: Robert W
authors.initials: RW
authors.lastname: Corty
mesh_terms: D015982:Bias; D016009:Chi-Square Distribution; D016001:Confidence Intervals; D003627:Data Interpretation, Statistical; D015987:Effect Modifier, Epidemiologic; D006801:Humans; D016014:Linear Models; D015999:Multivariate Analysis; D015399:Nursing Research / Q000379:methods*; D015203:Reproducibility of Results; D012107:Research Design; D018401:Sample Size