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The aim of this review is to discuss some issues in the design and statistical analysis of sport performance research, rather than to supply an authoritative 'cookbook' of methods. In general, we try to communicate some possible solutions to the conundrum of how to maintain both internal and external validity, as well as optimize statistical power, in applied sport performance research. We start by arguing that some sport performance research has been overly concerned with physiological predictors of performance, at the expense of not providing a valid and reliable description of the exact nature of the task in question. We show how the influence of certain factors on competitive performances can be described using linear or logistic regression. We discuss the choice of analysis for factorial repeated-measures designs, which is complicated by the assumption of 'sphericity' in a univariate general linear model, and the relatively low statistical power of the multivariate approach when used with small samples. We consider a little-used and simpler technique known as 'analysis of summary statistics'. In multi-group pre- and post-test designs, a useful technique can be to pair-match individuals on their performance scores in a counterbalanced fashion before the intervention or control has been introduced. Finally, we outline how confidence intervals can help in making statements about the probability of the population difference in performance exceeding the value designated as being worthwhile or not.
Atkinson et al. (Mon,) studied this question.