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Sue summarised responses: > So the answer is: don't do it. I am confused, and alarmed that distorted advice might build into a myth. The matter seems to arise from using "hypothesis testing" as a shibboleth. I recently commented on a student's thesis chapter where, indeed, baseline characteristics were examined one by one, tabulated and tested. My comment was that these were not "results" as they were a consequence of the design of the study, but nevertheless the tests were appropriate. They should have been reported succinctly for the record, something like: "the demographic characteristics of the two groups were checked for comparability, and on standard tests did not differ: age (t-test, p>0.1), sex (chi-square, p>0.9) ..." The point is that in this situation the data are expected to be consistent with the null hypothesis. Too many users assume that the only use of a statistical test is to find "significant" results. However, rather than rely upon a few statistical tests, I would advise full examination of the baseline measures to check assumptions, and look for patterns and exceptions. Some of the advice quoted in the summary message smacked of mechanistic processing, suitable for routine quality control but not for research investigations. So the question is, am I mad or what? Yours, in the crisis, R. Allan Reese Email: [log in to unmask]