An interesting alternative to power calculations (created 2010-06-09).

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Someone on the MedStats Internet discussion group mentioned an alternative to power calculations called accuracy in parameter estimation (AIPE). It looks interesting. Here are some relevant references.

  1. Ken Kelley, Keke Lai. R: Sample size planning for Accuracy in Parameter Estimation (AIPE) of the standardized contrast in ANOVA. Excerpt: "A function to calculate the appropriate sample size per group for the standardized contrast in ANOVA such that the width of the confidence interval is sufficiently narrow. " [Accessed June 9, 2010]. Available at:
  2. Scott E Maxwell, Ken Kelley, Joseph R Rausch. Sample size planning for statistical power and accuracy in parameter estimation. Annu Rev Psychol. 2008;59:537-563. Abstract: "This review examines recent advances in sample size planning, not only from the perspective of an individual researcher, but also with regard to the goal of developing cumulative knowledge. Psychologists have traditionally thought of sample size planning in terms of power analysis. Although we review recent advances in power analysis, our main focus is the desirability of achieving accurate parameter estimates, either instead of or in addition to obtaining sufficient power. Accuracy in parameter estimation (AIPE) has taken on increasing importance in light of recent emphasis on effect size estimation and formation of confidence intervals. The review provides an overview of the logic behind sample size planning for AIPE and summarizes recent advances in implementing this approach in designs commonly used in psychological research." [Accessed June 9, 2010]. Available at:
  3. Ken Kelley. Sample size planning for the coefficient of variation from the accuracy in parameter estimation approach. Behav Res Methods. 2007;39(4):755-766. Abstract: "The accuracy in parameter estimation approach to sample size planning is developed for the coefficient of variation, where the goal of the method is to obtain an accurate parameter estimate by achieving a sufficiently narrow confidence interval. The first method allows researchers to plan sample size so that the expected width of the confidence interval for the population coefficient of variation is sufficiently narrow. A modification allows a desired degree of assurance to be incorporated into the method, so that the obtained confidence interval will be sufficiently narrow with some specified probability (e.g., 85% assurance that the 95 confidence interval width will be no wider than to units). Tables of necessary sample size are provided for a variety of scenarios that may help researchers planning a study where the coefficient of variation is of interest plan an appropriate sample size in order to have a sufficiently narrow confidence interval, optionally with somespecified assurance of the confidence interval being sufficiently narrow. Freely available computer routines have been developed that allow researchers to easily implement all of the methods discussed in the article." [Accessed June 9, 2010]. Available at:,%20AIPE%20for%20C%20of%20V,%20BRM,%202007.pdf.
  4. Ken Kelley, Joseph R Rausch. Sample size planning for the standardized mean difference: accuracy in parameter estimation via narrow confidence intervals. Psychol Methods. 2006;11(4):363-385. Abstract: "Methods for planning sample size (SS) for the standardized mean difference so that a narrow confidence interval (CI) can be obtained via the accuracy in parameter estimation (AIPE) approach are developed. One method plans SS so that the expected width of the CI is sufficiently narrow. A modification adjusts the SS so that the obtained CI is no wider than desired with some specified degree of certainty (e.g., 99% certain the 95% CI will be no wider than omega). The rationale of the AIPE approach to SS planning is given, as is a discussion of the analytic approach to CI formation for the population standardized mean difference. Tables with values of necessary SS are provided. The freely available Methods for the Behavioral, Educational, and Social Sciences (K. Kelley, 2006a) R (R Development Core Team, 2006) software package easily implements the methods discussed." [Accessed June 9, 2010]. Available at:,%20Psychological%20Methods,%2011,%20pp%20363%20to%20385,%202006,%20.pdf.