Significant and meaningful effects in sports biomechanics research

TLDR

Errors in statistical analysis of multiple dependent variables and in documenting effect sizes are common in sports biomechanics literature, requiring control of Type I error inflation or use of multivariate analysis to account for variable structure. This paper reviews these errors and proposes solutions to enhance the validity of sports biomechanics research reports. Solutions include applying Holm's procedure or multivariate analysis of variance for multiple comparisons and reporting confidence limits or effect sizes when significant differences are found. Authors are encouraged to analyze data with experiment‑wise Type I error control and to report effect sizes or practical significance for statistically significant findings.

Abstract

Errors in statistical analysis of multiple dependent variables and in documenting the size of effects are common in the scientific and biomechanical literature. In this paper, I review these errors and several solutions that can improve the validity of sports biomechanics research reports. Studies examining multiple dependent variables should either control for the inflation of Type I errors (e.g. Holm's procedure) during multiple comparisons or use multivariate analysis of variance to focus on the structure and interaction of the dependent variables. When statistically significant differences are observed, research reports should provide confidence limits or effect sizes to document the size of the effects. Authors of sports biomechanics research reports are encouraged to analyse and present their data accounting for the experiment-wise Type I error rate, as well as reporting data documenting the size or practical significance of effects reaching their standard of statistical significance.