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
>>