# How to compute the power of a wilcoxon test?

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## How to compute the power of a wilcoxon test?

 Hello All, I posted a similar question before, but the direction was driven to whether my case is suitable for a wilcoxon test. After research about the appropriateness, I am pretty sure that a wilcoxon test is the right tool for my case. But how to compute the power of the test is still an unanswered question bothering me. The basic stats of my two paired samples are: mean1 = 0.0032,   sd1 = 0.012 mean2 = 0.00042, sd2 = 0.0016 sample size = 366 Could anyone help? Thank you in advance.
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## Re: How to compute the power of a wilcoxon test?

 On Sep 15, 2011, at 15:47 , tn85 wrote: > Hello All, > > I posted a similar question before, but the direction was driven to whether > my case is suitable for a wilcoxon test. After research about the > appropriateness, I am pretty sure that a wilcoxon test is the right tool for > my case. But how to compute the power of the test is still an unanswered > question bothering me. > > The basic stats of my two paired samples are: > mean1 = 0.0032,   sd1 = 0.012 > mean2 = 0.00042, sd2 = 0.0016 > sample size = 366 > > Could anyone help? Thank you in advance. Thing is, you can't really do that. Nonparametic tests are only distribution-free under the null hypothesis. For power, you need the distribution of the test statistic under the alternative. First: Do you really mean "paired"? If so, there would seem to be some information missing, namely the magnitude of the correlation. Also, do you really mean that the sd's differ by a factor of 10?? (And, if those are empirical quantities, don't do power calculations at all). Assuming that you have two independent samples of size 366, and parameters as above, you might do > pval <- replicate(1000, wilcox.test(rnorm(366,.0032,.012), rnorm(366,.00042,.0016))\$p.value) > summary(pval)      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 0.0000000 0.0000004 0.0000185 0.0064520 0.0003877 0.8997000 > sum(pval < .05) [1] 979 suggesting a power of around 98%. Adjust as necessary.   > > -- > View this message in context: http://r.789695.n4.nabble.com/How-to-compute-the-power-of-a-wilcoxon-test-tp3815616p3815616.html> Sent from the R help mailing list archive at Nabble.com. > > ______________________________________________ > [hidden email] mailing list > https://stat.ethz.ch/mailman/listinfo/r-help> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html> and provide commented, minimal, self-contained, reproducible code. -- Peter Dalgaard, Professor Center for Statistics, Copenhagen Business School Solbjerg Plads 3, 2000 Frederiksberg, Denmark Phone: (+45)38153501 Email: [hidden email]  Priv: [hidden email] ______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-helpPLEASE do read the posting guide http://www.R-project.org/posting-guide.htmland provide commented, minimal, self-contained, reproducible code.
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