The paper is Everything you never wanted to know about circular analysis, but were afraid to ask. Our all-star team of voodoo-hunters - including Ed "Hannibal" Vul (now styled Professor Vul), Nikolaus "Howling Mad" Kriegeskorte, and Russell "B. A." Poldrack - provide a good overview of the various issues and offer their opinions on how the field should move forward.
The fuss concerns a statistical trap that it's easy for neuroimaging researchers, and certain other scientists, to fall into. Suppose you have a large set of data - like a scan of the brain, which is a set of perhaps 40,000 little cubes called voxels - and you search it for data points where there is a statistically significant effect of some kind.
Because you're searching in so many places, in order to avoid getting lots of false positives you set the threshold for significance very high. That's fine in itself, but a problem arises if you find some significant effects and then take those significant data points and use them as a measure of the size of the effects - because you have specifically selected your data points on the basis that they show the very biggest effects out of all your data. This is called the non-independence error and it can make small effects seem much bigger.
The latest paper offers little that's new in terms of theory, but it's a good read and it's interesting to get the authors' expert opinion on some hot topics. Here's what they have to say about the question of whether it's acceptable to present results that suffer from the non-independence error just to "illustrate" your statistically valid findings:
Q: Are visualizations of non-independent data helpful to illustrate the claims of a paper?
A: Although helpful for exploration and story telling, circular data plots are misleading when presented as though they constitute empirical evidence unaffected by selection. Disclaimers and graphical indications of circularity should accompany such visualizations.Now an awful lot of people - and I confess that I've been among them - do this without the appropriate disclaimers. Indeed, it is routine. Why? Because it can be useful illustration - although the size of the effects appears to be inflated in such graphs, on a qualitative level they provide a useful impression of the direction and nature of the effects.
But the A Team are right. Such figures are misleading - they mislead about the size of the effect, even if only inadvertently. We should use disclaimers, or ideally, avoid using misleading graphs. Of course, this is a self-appointed committee: no-one has to listen to them. We really should though, because what they're saying is common sense once you understand the issues.
It's really not that scary - as I said on this blog at the outset, this is not going to bring the whole of fMRI crashing down and end everyone's careers; it's a technical issue, but it is a serious one, and we have no excuse for not dealing with it.
Kriegeskorte, N., Lindquist, M., Nichols, T., Poldrack, R., & Vul, E. (2010). Everything you never wanted to know about circular analysis, but were afraid to ask Journal of Cerebral Blood Flow & Metabolism DOI: 10.1038/jcbfm.2010.86