Sample size planning for statistical power and accuracy in parameter estimation.

By S. Maxwell, Ken Kelley, Joseph R. Rausch. Annual Review of Psychology, 2008

519 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Equivalence Tests (2017, unknown): “For example, while sample size planning based on CIs focusses on the width of CIs, sample size planning for hypothesis testing uses power analysis to estimate the probability of observing a significant result (Maxwell, Kelley, & Rausch, 2008).”
At what sample size do correlations stabilize (2013, background): “2004; Maxwell et al., 2008).”
Recommendations for Increasing Replicability in Psychology † (2013, background): “Within this alternative approach, there is a third type of error: inaccuracy of parameter estimation (Kelley & Maxwell, 2003; Maxwell et al., 2008).”
Sample size considerations for the external validation of a multivariable prognostic model: a resampling study (2015, background): “, precise estimates) so that meaningful conclusions on the model’s predictive accuracy in the target population can be drawn [9,19].”
Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs (2013, background): “This level of uncertainty should be taken into account when planning the sample size for a study (for alternative approaches to power analysis, see Maxwell et al., 2008).”
Sample Size Justification (2021, unknown): “For these calculations an expected effect size estimate needs to be provided, but it has relatively little influence on the required sample size (Maxwell et al., 2008).”