One of the most common applications of statistics in the social and behavioral science is in testing null hypotheses. For example, a researcher wanting to compare the outcomes of two treatments will usually do so by testing the hypothesis that in the population there is no difference in the outcomes of the two treatments. The power of a statistical test is defined as the likelihood that a researcher will be able to reject a specific null hypothesis when it is in fact false.
Cohen (1988), Lipsey (1990), and Kraemer and Thiemann (1987) provided excellent overviews of the methods, assumptions, and applications of power analysis. Murphy and Myors (2003) extended traditional methods of power analysis to tests of hypotheses about the size of treatment effects, not merely tests of whether or not such treatment effects exist.
The power of a null hypothesis test is a function of sample size (n ), effect size (ES), and the standard used to define statistical significance (α) , and the...
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Cohen J (1988) Statistical power analysis for the behavioral sciences, 2nd edn. Erlbaum, Hillsdale
Faul F, Erdfelder E, Lang A-G, Buchner A (2007) G∗Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav Res Meth 39:175–191
Kraemer HC, Thiemann S (1987) How many subjects? Sage, Newbury Park
Lipsey MW (1990) Design sensitivity. Sage, Newbury Park
Murphy K, Myors B (2009) Statistical power analysis: a simple and general model for traditional and modern hypothesis tests, 3rd edn. Erlbaum, Mahwah
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© 2011 Springer-Verlag Berlin Heidelberg
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Murphy, K.R. (2011). Power Analysis. In: Lovric, M. (eds) International Encyclopedia of Statistical Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04898-2_454
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