References If you use G*Power for your research, then we would appreciate your including one or both of the following references (depending on what is appropriate) to the program in the papers in which you publish your results: Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39, 175-191. Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G.
• You may use screenshots of G*Power without asking for permission. • Considerable effort has been put into program development and evaluation, but there is no warranty whatsoever. XP, Vista, 7, 8, and 10 (about 20 MB). Please make sure to choose “unpack with folders” in your unzip tool. 10.7 to 10.13 (about 2 MB).
Version History 17 July 2017 - Release 188.8.131.52 Mac Fixed a bug that could cause crashes. 28 March 2014 - Release 3.1.9. Dragon Naturally Speaking Trial Version. 2 Mac Fixed a bug in the χ 2 tests, Goodness-of-fit tests: Contingency tables module which prevented the computed effect size from appearing in the effect size drawer. Windows Fixed a bug that could occur under very specific circumstances when transferring an effect size from the effect size drawer to the main window.
10 March 2014 - Release 184.108.40.206 Mac Now includes the calculator that previously has been included only in the Windows version. 4 February 2014 - Release 3.1.9 Mac and Windows Fixed a bug in the sign test’s sensitivity analysis which led to an offset of -0.5 in the reported effect size. Changed the behaviour of all tests based on the binomial distribution. The upper and lower limits are now always within the range [0,n] instead of [-1,n+1]. This change may lead to alpha values larger than the requested alpha values, but now we have the advantage that the upper and lower limits correspond to actual decision boundaries. For instance, in a two-sided test H0 is rejected if, for the observed number x of successes, it holds that x = upper limit.
Note, however, that the change affects the results only when N is very small.
NOTE: This page was developed using G*Power version 3.0.10. You can download the current version of G*Power from. You can also find help files, the manual and the user guide on this website. Examples Example 1. A company markets an eight week long weight loss program and claims that at the end of the program, on average, a participant will have lost 5 pounds. On the other hand, you have studied the program and you believe that their program is scientifically unsound and shouldn’t work at all. With some limited funding at hand, you want test the hypothesis that the weight loss program does not help people lose weight.