[Eeglablist] Permutation stats and number of trials per condition

Makoto Miyakoshi mmiyakoshi at ucsd.edu
Fri Jul 12 20:45:42 PDT 2019


Dear David,

If you are not sure, perform a simple simulation using noise with the same
mean values, standard deviations, and the same number of trials. You can
test where and how the test fails while changing the number of trials. I
imagine you won't see apparent failure unless you set quite extreme. Then,
you can show the results to review to convince that the case of your data
is on the quite safe side. If you want, you can describe what you did in
the supplementary materials of the paper. A non-statistician's comment.

Makoto



On Sat, Jun 29, 2019 at 2:21 AM Jenson, David Evans <david.jenson at wsu.edu>
wrote:

> EEGLABers,
>
> I’m working through reviewer comments, and have a question about how
> permutation stats are run in EEGLAB.
>
> I’m looking at ICA-decomposed ERSP differences across 7 conditions (42
> participants), each of which has a different number of usable trials based
> on participant accuracy and noisiness of the data.  The reviewer is
> concerned because there is a significant difference in the number of trials
> across conditions, stating that comparisons between conditions are unfair
> since the signal to noise ratio is higher in the condition with more trials.
>
> Based on the use of permutation statistics, I don’t think this is a valid
> concern, but I am having trouble articulating why.  Can anyone help with:
>
> 1: Confirming that an unequal number of trials across conditions is not
> problematic?
>
> 2: Helping me understand how permutation statistics address the issue?
>
> Thanks,
>
> David Jenson, PhD
> Assistant Professor
> Department of Speech and Hearing Sciences
> o: 509-368-6913 | david.jenson at wsu.edu<mailto:david.jenson at wsu.edu>
>
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-- 
Makoto Miyakoshi
Assistant Project Scientist, Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego


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