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I had started writing this message last night prior to receiving Andrew's timely response, but here is just a little perspective on this discussion. Indeed, the technique of squaring and summing the individual z-values and comparing the result against the Chi-squared distribution should not be considered as a substitute for performing a proper group analysis. Between subject variation is not modeled with this approach and generalization to the population level is severely limited. However, measures such as this are not without precedent. Techniques for the meta-analysis of a scientific literature routinely employ such approaches since the z-score and related measures can be taken as a measure of statistical effect size. Increasingly, more and more meta-analytic assessments of results in the functional imaging literature are being reported as part of newer research articles and as parts of quantitative literature reviews. Noteworthy is the Brainmap database which attempts to record activation results from the literature for contrast and comparison. For the consolidation of the results of individual subject results these values may have an insightful, if somewhat constrained interpretation. Depending upon the statement about the data that the person is trying to make it is possible to make some crude statements about their individual z-score maps. As was mentioned, if the z-scores are assumed to be i.i.d., then a new random variable z may be created by summing the zscores and dividing by the square root of the number of values summed. This would be a crude assessment of whether there is a systematic bias in the direction of the mean z-value, presumably related to the paradigm in question. However, the assessment of whether or not there is evidence for any sort of variation at all amongst the values (e.g. regardless of the sign of the test) would be crudely assessed by the squared-summed z-value approach. Again, these are poorman's statistics but can, with caution, appropriately assess basic characteristics of the data across individuals. So, as Andrew points out "both are right". The resulting statistical image pattern from such techniques should be similar, though not exactly equal, to those given from an SPM conjunction analysis, less data masking and the SPM probability adjustment. Arguments over statistics such as this, however, are frustrating for the non-statistically minded who simply want to know "which test is better?". Regretably, there are no easy answers if the experimental design is ill-defined from the outset and one is hunting for significant findings. The following approach will usually help to make certain that one will have available the most appropriate analytical approach to understanding their data. Determine a priori which form of experimental design is most suitable for testing your hypothesis, then perform the analysis that is most appropriate for your experimental design. The habit in this field of collecting data first and only then deciding how to analyze it is undoubtedly going to result in more confusion that fundamental results about brain function by filling the literature with post hoc statistics making meta-analytic assessment of findings useless, if not impossible. I am certain that discussion on this issue will continue. Sincerely yours, Jack ******************************** John Darrell Van Horn, Ph.D. Laboratory of Brain and Cognition, NIMH National Institutes of Health 9000 Rockville Pike Bethesda, Maryland 20892 Phone: (301) 435-4938 Fax: (301) 402-0921 email: [log in to unmask] %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%