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Dear Adrian,<br>
<br>
<blockquote cite="mid42E611B900023171@mail.swissinfo.org" type="cite">
<pre wrap="">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.
</pre>
</blockquote>
The problem is that in any time-frequency decomposition, neighboring
time-frequency points are highly correlated so that standard bonferoni
correction (that assumes independent observations) are overly
conservative. In the following paper, a method is decribed to
calculated the minimum time and frequency resolution (number of
independent time-frequency estimates - about 200 in our case per plot):<br>
<br>
High-frequency gamma-band activity in the basal temporal cortex during
picture-naming and lexical-decision tasks.<br>
<font size="-1"><span
title="The Journal of neuroscience : the official journal of the Society for Neuroscience.">J
Neurosci.</span> 2005 Mar 30;25(13):3287-93. <br>
</font><font size="-1"><a
href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=15800183&query_hl=3">Tanji
K, Suzuki K, Delorme A, Shamoto H, Nakasato N.</a><br>
<br>
Better than the bonferoni methos is also the Holm's method. First
choose a significance level p= (e.g., p=0.05). Then compute the exact
p-value for each test (which is usually possible using modern
computerized approaches). Rank the collection of p-values from smallest
to largest. The smallest p-value is tested against /N, where N is the
number of tests. If the smallest p-value is not less than /N, stop the
procedure. However, if it is less than /N, proceed to test the second
smallest p-value against /(N-1), etc... <br>
</font>
<blockquote cite="mid42E611B900023171@mail.swissinfo.org" type="cite">
<pre wrap="">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?
</pre>
</blockquote>
Based on discussions with R Oostenveld, we actually changed our mind
about this idea and now prefer to accumulate more values. The reason is
that, using these functions, we were fitting a distribution and trying
to make inference about the tails of this distribution using the fitted
curve. However the fitting might be especially innacurate at the tails
(where there is few data points) so to be conservative it seems more
appropriate to accumulate more values than to try to fit the
distribution.<br>
<br>
Best,<br>
<br>
Arno<br>
<br>
<br>
<blockquote cite="mid42E611B900023171@mail.swissinfo.org" type="cite">
<pre wrap="">
Thank you very much for your help.
Adrian Guggisberg, MD
Neurologische Klinik und Poliklinik
Inselspital
CH-3010 Bern
Switzerland
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</pre>
</blockquote>
<br>
<br>
<div class="moz-signature">-- <br>
<br>
<b><font face="Arial,Helvetica"><font size="+1">Arnaud Delorme, Ph.D.</font></font></b>
<br>
<font face="Arial,Helvetica"><font size="+1"><font color="#3333ff">Swartz
Center for Computational Neuroscience,</font> <font color="#3333ff">INC,
University of San Diego California</font></font></font>
<br>
La Jolla, CA92093-0961, USA
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<br>
<font face="Arial,Helvetica"><b>To think upon</b>:</font></p>
<blockquote><dt><font face="Arial,Helvetica"> In spite of all our
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<font size="-5"><br>
<br>
</font></font></dt>
<dd><font face="Arial,Helvetica"><i>Bulwer</i></font></dd>
</blockquote>
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