An interesting alternative to power calculations (created 2010-06-09).
This page is moving to a new website.
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.
- 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:
- 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:
- 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:
- 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: