Still, many people ignore these methods in favor of uncontrolled statistics. Instead, false discovery rate (FDR) and familywise error correction (FWE) are both excellent options for the correction of multiple comparisons. While it is a step in the right direction, this method remains a weak control for multiple comparisons. This constrains the results to those that are extra potent and have a certain minimum size. Most people try to account for this issue by having an arbitrarily high significance level and some form of minimum cluster volume. Does this mean that the temperature in Kansas is related to brain activity? No, most certainly not. paper, I could correlate the daily temperature reading in Lawrence, Kansas with a set of fMRI data gathered in Santa Barbara, California and I would find a few significant voxels, guaranteed.
Expanding on the example in the Vul et al. The problem is this: when looking for a relationship between 160,000 voxels and ANYTHING you are likely to get a few significant results simply by random chance. do not dwell on this error, but it is an issue that is pervasive in neuroimaging.
Sin #1 is a multiple comparisons problem. This ‘non-independence error’ causes the second correlation estimate to be dramatically inflated, leading to results that may simply represent noisy data. These voxels are often selected on the basis of a strong correlation in the first analysis. They then report a second correlation, this time just between a selected set of voxels and their measure of behavior. They found that some researchers do one correlation analysis against all the voxels in the brain (~160,000+) to find those that are related to their measure of behavior. The paper highlights an issue in the relation of neuroimaging data to measures of behavior often found in social and cognitive neuroscience.
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Update: download the manuscript for free at: The recent paper by Vul, Harris, Winkielman, and Pashler (in press) represents just that: a call to action to change one particular old, bad habit. Sometimes we get so set in our methods that it requires a major wake up call to get us off our asses. For a young field cognitive neuroscience has made dramatic advances in a relative short amount of time, but there have been some mistakes that we keep making again and again.
Instead it moves forward in fits and starts, occasionally under protest. The progress of science is not a continuous, linear process. Voodoo Correlations in Social Neuroscience
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