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Dear Oliver,
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<div>Let me give it a shot: </div>
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<div>I think the underlying idea of the statement in the referred paper is that the permutation distribution of the test-statistic (in this case a T-statistic) is symmetric around 0. Thus, under the assumption that a complete permutation distribution can be
created (i.e. one can take all possible random splits of the individual observations to one of the conditions, and subsequently compute the T-statistics, followed by thresholding and clustering), the permutation distribution obtained by retaining (for each
permutation) the largest positive cluster-based test statistic, will be exactly mirrored by a permutation distribution of the largest negative cluster-based test statistics. I would assume that this justifies the idea to use the absolute cluster statistics
and test these against its permutation distribution.</div>
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<div>However, in reality these permutation distributions will not necessarily be based on test statistics that ‘behave nicely (i.e. symmetrically) around 0’. For that reason, and this is how it is done in the code, a two-sided (cluster-based) permutation test
actually compares the observed test statistics with 2 permutation distributions, obtained by taking the most extreme (positive and negative) permuted test statistics.</div>
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<div>Best wishes,</div>
<div>Jan-Mathijs</div>
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<div>On 12 Jun 2025, at 16:08, Oliver Hermann via fieldtrip <fieldtrip@science.ru.nl> wrote:</div>
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Hi all,</div>
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I wonder if anyone can clear up some confusion regarding evaluation of summed-cluster statistics following permutation. </div>
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Specifically, as of Maris and Oostenveld (2007), evaluation is performed for all clusters, based on the permutation distribution of the max (absolute) cluster statistic, see "It is important to note that the<span class="Apple-converted-space"> </span><i>p</i>-values
for all eight clusters are calculated under the permutation distribution of the maximum (absolute value) cluster-level statistic and not under the permutation distribution of the second largest, third largest, etc."</div>
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However, when I look at the guide at<span class="Apple-converted-space"> </span><a href="https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.fieldtriptoolbox.org%2Ftutorial%2Fstats%2Fcluster_permutation_timelock%2F&data=05%7C02%7Cfieldtrip%40science.ru.nl%7C51d06a2c67fc4cd238de08ddaa5b4626%7C084578d9400d4a5aa7c7e76ca47af400%7C1%7C0%7C638854031170531498%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=xBIhIvZdtoCMMTnoD7dXjVrK8ME9NsIPE4GbFvDA%2F0I%3D&reserved=0" originalsrc="https://www.fieldtriptoolbox.org/tutorial/stats/cluster_permutation_timelock/" originalsrc="https://www.fieldtriptoolbox.org/tutorial/stats/cluster_permutation_timelock/" id="OWAbb86689d-8b00-6bcd-cd04-5d15de63247c" class="OWAAutoLink">https://www.fieldtriptoolbox.org/tutorial/stats/cluster_permutation_timelock/</a></div>
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the following recommendation is given: And third, the critical value for the cluster-level test statistic (determined by cfg.alpha; see further) is now two-sided: negative cluster-level statistics must be compared with the negative critical value, and positive
cluster-level statistics must be compared with the positive critical value.</div>
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To me, these two statements seem contradictory. Please could someone clarify the correct approach (I assume the latter) and why the discrepancy?</div>
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Thanks,</div>
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Oliver</div>
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<div style="margin-top: 0px; margin-bottom: 0px;">-------------------</div>
<div style="margin-top: 0px; margin-bottom: 0px;">Oliver Hermann</div>
<div style="margin-top: 0px; margin-bottom: 0px;">PhD Researcher</div>
<div style="margin-top: 0px; margin-bottom: 0px;">Department of Psychology</div>
<div style="margin-top: 0px; margin-bottom: 0px;">University of Bath</div>
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