Research into the physiology of exercise and kinanthropometry is intended to improve our understanding of how the body responds and adapts to exercise. If such studies are to be meaningful, they have to be well designed and analysed. Advances in personal computing have made available statistical analyses that were previously the preserve of elaborate mainframe systems and have increased opportunities for investigation. However, the ease with which analyses can be performed can mask underlying philosophical and epistemological shortcomings. The aim of this review is to examine the use of four techniques that are especially relevant to physiological studies: (1) bivariate correlation and linear and non-linear regression, (2) multiple regression, (3) repeated-measures analysis of variance and (4) multi-level modelling. The importance of adhering to underlying statistical assumptions is emphasized and ways to accommodate violations of these assumptions are identified.
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