The featured article is a response by Lazar to Vul et al’s paper on statistics used in fMRI (and reviewed here) and Lazar’s article is freely available here. Vul’s article presents a useful focus for examining the statistical techniques used in fMRI and a systematic analysis of the responses to this paper should offer insights into the diversity of approaches considered in such analysis. Lazar is based in the Department of Statistics at the University of Georgia. He starts off the article with this neat observation
‘Statistical tools were originally devised for a rather specific set of circumstances – mostly small or moderately sized data sets, collected under controlled experimental conditions’
I was reassured to find that Lazar refers to Vul et al’s use of the term ‘non-independence error’ as a ‘selection bias’ which I had also considered in my original review above. He discusses the issue of selection bias in other fields further and I was intrigued to read of Benjamini’s experience in discussing a possible selection bias in the field of genetics.
Lazar has many interesting sessions and reiterates an important point made elsewhere
‘Increased transparency in reporting the details of an analysis will also help’
Lazar tells us that he is working with colleagues to develop new statistical approaches to analysing imaging data and suggests that researchers will move away from the correlation analysis as more appropriate forms of analysis take their place. He also warns us against the basic mistake of analysing the same data set twice and notes the increasing complexity of data available for analysis.
While relatively brief, I thought this was a thought-provoking article which suggests changes not just in neuroimaging but also in the field of statistics.
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