Statistics in medicine
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There has been a heightened awareness of the dangers of selection bias over the past two decades. Certainly coverage in statistical and 'statistics for medicine', and epidemiology textbooks have allocated pages to warn investigators and readers of investigations to be aware of its presence. ⋯ It is the intent of this paper to present examples of selection bias in a variety of areas which have resulted in misleading or entirely incorrect results. We hope to help make such research scientifically 'politically incorrect' to the degree that the scientific community 'just says no' to such studies, either proposed or reported.
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Conventional wisdom suggests that for small data sets having substantial skew, one should attempt to determine the correct distributional form, if possible, and apply statistical methods appropriate for that distribution. Transformations such as the log or square root are often used. If an appropriate distributional form cannot be determined, a distribution-free procedure such as a rank transformation or a randomization test procedure can be used. ⋯ The UMP test, that is, LOG(X), produced the highest power. There was very little power lost for the SQRT t-test. The loss in power varied between 2 per cent and 5 per cent for the RANK test.(ABSTRACT TRUNCATED AT 400 WORDS)