Dear SPM-ers,
I would like to ask your input about the validity of a certain cascade
of hypothesis testing.
The issue pertains to testing for significant activations. The
suggestion was raised to first test for significant clusters (using
cluster level statistics), setting a more or less lenient intensity
treshold, and identifying clusters of significant size.
Then, in order to improve the localization information within those
significant clusters, it was suggested to make a mask of the significant
clusters, and test for significant voxels within this mask.
Now, my idea would be that this is invalid, since you first identify
where to look (using the greater power of cluster-level statistics) and
then use this information to do a statistical test on the same data to
get better localization precision.
It was however suggested that testing for extent of activation and
testing for intensity are only mildly associated, and can therefore be
regarded as more or less independent tests.
What is your idea about this way of treating the data, first identifying
significant clusters and using this information to identify significant
voxels? Is it valid or not?
Many thanks for your input,
Yours,
Floris
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