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Hi, Post hoc power analyses do not make sense and you can defend this point in a response to reviewers. You can cite many of the references given here (scroll down to the bottom of the page): http://www.childrensmercy.org/stats/size/posthoc.aspx My favorite is the 6th (*The Abuse of Power: The Pervasive Fallacy of Power Calculations for Data Analysis) *If you ran the analysis in an ROI (chosen a priori) you can construct a confidence interval, which may be useful in showing what the effect size is and how large the confidence interval is. I believe the end of the Hoenig reference I mentioned above talks about this. Otherwise the conclusion is that you basically didn't have enough power. Hope that is helpful, Jeanette On Sun, Jan 22, 2012 at 12:04 PM, Sinan Dirik <[log in to unmask]> wrote: > Hi Michael, > > What you said sounds definitely quite reasonable, thank you. But we were > asked to 'formally test if the negative result was due to our statistical > power' for a publication. So we dont have much choice. > > > On Sun, 22 Jan 2012 11:17:39 -0600, Michael Harms <[log in to unmask]> > wrote: > > >Hi Sinan, > >Post-hoc power-analyses of that sort are not really meaningful. > >By definition, given your null result, if there is an effect present of a > >given size, you didn't have sufficient power to detect it. > > > >cheers, > >-MH > > > > > >> Dear FSLers, > >> > >> > >> We compared the GM, volumetric abnormalities between 3 groups of > subjects > >> in an ANOVA design using TFCE based thresholding, at .05. However we did > >> not find any differences between two of the 3 groups. We are now trying > to > >> understand if this was due to our sample sizes. > >> > >> The previous posts on this list were not helpful, probably bec of my > level > >> of knowledge in statistics. > >> > >> > >> Could you please guide me to do this power analysis ? > >> > >> Thank you, > >> > >> Sinan > >> >