[Eeglablist] permutation and FDR

[Ramtin Mehraram (Student)]

Dear Szilvia,

To my knowledge, the permutation-based statistics "corrects" for non-normal sample distribution. This allows you to use parametric tests (such as t-test) with any sample distribution. It does not correct for multiple comparisons.

The false discovery rate (FDR) is not very conservative as Bonferroni is. FDR is usually recommended whether you have huge amount of data (like genetic data) or in exploratory stage of your study. A more "strict" correction is recommended when finalizing the obtained results.

A good variant of the Bonferroni correction is the Holm-Bonferroni, which is slightly more powerful.

I hope this helps.

B.

Ramtin Mehraram
PhD Student @ramtinTVT
Biomedical Research Building 3rd floor
Institute of Neuroscience
Newcastle University
NE4 5PL, United Kingdom
www.lewybodylab.org 
https://www.newcastlebrc.nihr.ac.uk/research-themes/dementia/ 

-----Original Message-----
From: eeglablist [mailto:eeglablist-bounces at sccn.ucsd.edu] On Behalf Of Szilvia Linnert
Sent: 08 June 2018 09:37
To: EEGLABLIST <eeglablist at sccn.ucsd.edu>
Subject: [Eeglablist] permutation and FDR

Hi everyone,

I am planning to use permutation-based statistics using the study function in EEGLAB.

I am confused whether I need to correct for multiple comparisons (using
FDR) additionally, or the permutation method itself also corrects for multiple comparisons. I've got contradictory suggestions about this.

Many thanks ,

Szilvia

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