A short tutorial of GPower
Susanne Mayr, Edgar Erdfelder, Axel Buchner, Franz Faul
Abstract
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Susanne Mayr, Edgar Erdfelder, Axel Buchner, Franz Faul
Abstract
Open-access reader
The purpose of this paper is to promote statistical power analysis in the behavioral sciences by introducing the easy to use GPower software.GPower is a free general power analysis program available in two essentially equivalent versions, one designed for Macintosh OS/OS X and the other for MS-DOS/Windows platforms.Psychological research examples are presented to illustrate the various features of the GPower software.In particular, a priori, post-hoc, and compromise power analyses for t-tests, F-tests, and χ 2 -tests will be demonstrated.For all examples, the underlying statistical concepts as well as the implementation in GPower will be described.In the behavioral sciences, we routinely apply statistical tests, but control of statistical power cannot be taken for granted.However, neglecting statistical power-the probability of rejecting false null hypotheses-can have severe consequences.For example, without control of statistical power it is very difficult to interpret nonsignificant results.Statistical tests can produce nonsignificant results because (a) the null hypothesis (H0) holds and is retained correctly or (b) the alternative hypothesis (H1) holds but the test has not been powerful enough to detect the deviations from H0. Obviously, there is no reasonable way to decide between interpretations (a) and
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The purpose of this paper is to promote statistical power analysis in the behavioral sciences by introducing the easy to use GPower software.GPower is a free general power analysis program available in two essentially equivalent versions, one designed for Macintosh OS/OS X and the other for MS-DOS/Windows platforms.Psychological research examples are presented to illustrate the various features of the GPower software.In particular, a priori, post-hoc, and compromise power analyses for t-tests, F-tests, and χ 2 -tests will be demonstrated.For all examples, the underlying statistical concepts as well as the implementation in GPower will be described.In the behavioral sciences, we routinely apply statistical tests, but control of statistical power cannot be taken for granted.However, neglecting statistical power-the probability of rejecting false null hypotheses-can have severe consequences.For example, without control of statistical power it is very difficult to interpret nonsignificant results.Statistical tests can produce nonsignificant results because (a) the null hypothesis (H0) holds and is retained correctly or (b) the alternative hypothesis (H1) holds but the test has not been powerful enough to detect the deviations from H0. Obviously, there is no reasonable way to decide between interpretations (a) and
Key concepts: Computer science