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Sample-Size Analysis in Study Planning: Concepts and Issues, With Examples Using PROC POWER and PROC GLMPOWER

• Tags:
• sample,
• rates,
• type,
• concepts

Overview Ever-improving methods and software, including new tools in the SAS 9.1, are transforming the practice of sample-size analysis. Statisticians and subject-matter investigators need to understand the concepts and issues involved, but such matters are too often given short shrift in statistics education. This paper covers the most common type of sample-size analysis, classical prospective power analysis, which examines the chance that a given null statistical hypothesis will be rejected. Essential to this - but rarely taught - is that greater power reduces both positive and negative inference mistake rates (defined herein), which is argued are more relevant to researchers than the classical Type I and II error rates.

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