The Power Analysis module implements the techniques of statistical power analysis, sample size estimation, and advanced techniques for confidence interval estimation. The main goal of the first two techniques is to allow you to decide, while in the process of designing an experiment, (a) how large a sample is needed to allow statistical judgments that are accurate and reliable, and (b) how likely your statistical test will be to detect effects of a given size in a particular situation. The third technique is useful in implementing objectives (a) and (b) above, and in evaluating the size of experimental effects in practice.

Performing power analysis and sample size estimation is an important aspect of experimental design, because without these calculations, sample size may be too high or too low. If sample size is too low, the experiment will lack the precision to provide reliable answers to the questions it is investigating. If sample size is too large, time and resources will be wasted, often for minimal gain.

The Power Analysis module provides a number of graphical and analytical tools to enable precise evaluation of the factors affecting power and sample size in many of the most commonly encountered statistical analyses. This information can be crucial to the design of a study that is cost-effective and scientifically useful.

Noncentrality interval estimation procedures and other sophisticated confidence interval procedures implemented in the Power Analysis module provide some sophisticated confidence interval methods for analyzing the importance of an observed experimental result. An increasing number of influential statisticians are suggesting that confidence interval estimation should augment or replace traditional hypothesis testing approaches in the analysis of experimental data.