[Eeglablist] Bootstrap significance probability level

[Adrian Guggisberg]

Hello

I wonder, whether anyone has some practical considerations about which level
of significance probability should be chosen for bootstrap statistics. 

Since we have to deal with multiple comparisons, the significance threshold
should be decreased, but the question is how much. As much as I understand,
the Bonferroni correction for multiple testing would correspond to a level
of p < 0.05/(number of time points * number of frequency bins). However,
this would require a very high amount of surrogate data.

Most of the studies seem to use p-values < 0.01. What's the argumentation
for this?

In their J Neurosci Meth; 2004:9-21 Paper, Arnaud Delorme and Scott Makeig
write that they have 
"implemented a method to fit the observed data distribution using a forth
order distribution fit. This feature will be available in a near-term release
of EEGLAB."
Does this feature allow a significance level of 0.05? Is there a way to obtain
it?

Thank you very much for your help.


Adrian Guggisberg, MD
Neurologische Klinik und Poliklinik
Inselspital
CH-3010 Bern
Switzerland

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