Power and Sample Size
Power and sample size analysis optimizes the resource usage and design of a study, improving chances of conclusive results with maximum efficiency. The standard statistical testing paradigm implicitly assumes that Type I errors (mistakenly concluding significance when there is no true effect) are more costly than Type II errors (missing a truly significant result). This may be appropriate for your situation, or the relative costs of the two types of error may be reversed. Power and sample size analysis can help you achieve your desired balance between Type I and Type II errors. With optimal designs and sample sizes, you can improve your chances of detecting effects that might otherwise have been ignored, save money and time, and perhaps minimize risks to subjects.
The SAS/STAT power and sample size procedures include the following:
 GLMPOWER Procedure — Performs prospective power and sample size analysis for linear models
 POWER Procedure — Performs prospective power and sample size analyses for a variety of statistical analyses
GLMPOWER Procedure
Power and sample size analysis optimizes the resource usage and design of a study, improving chances of conclusive results with
maximum efficiency. The GLMPOWER procedure performs prospective power and sample size analysis for linear models, with a variety of goals:
 determining the sample size required to get a significant result with adequate probability (power)
 characterizing the power of a study to detect a meaningful effect
 conducting whatif analyses to assess sensitivity of the power or required sample size to other factors
The following are highlights of the GLMPOWER procedure's features:
 statistical analyses that are covered include Type III F tests and contrasts of
fixed effects in univariate and multivariate linear models, optionally with covariates
 for multivariate models, you can choose from the following tests:
 Wilks' lambda
 HotellingLawley trace
 Pillai's trace
 for the univariate approach to repeated measures, you can choose from the following types of F tests:
 uncorrected
 GreenhouseGeisser
 HuynhFeldt
 Box conservative

 supports BY group processing, which enables you to obtain separate analyses for grouped observations
 creates a SAS data set that corresponds to any output table
 automatically creates graphs by using ODS Graphics

For further details, see
GLMPOWER Procedure
POWER Procedure
The POWER procedure performs prospective power and sample size analyses for a variety of goals, such as the following:
 provides analysis for the following:
 t tests, equivalence tests, and confidence intervals for means
 tests, equivalence tests, and confidence intervals for binomial proportions
 multiple regression
 tests of correlation and partial correlation
 oneway analysis of variance
 rank tests for comparing two survival curves
 logistic regression with binary response
 WilcoxonMannWhitney (ranksum) test
 Cox proportional hazards regression
 FarringtonManning noniferiority tests of relative risk

 determining the sample size required to get a significant result with adequate probability (power)
 characterizing the power of a study to detect a meaningful effect
 conducting whatif analyses to assess sensitivity of the power or required sample size to other factors
 creates a SAS data set that corresponds to any output table
 automatically creates graphs by using ODS Graphics

For further details, see
POWER Procedure