The need to report effect size estimates revisited. An overview of some recommended measures of effect size

TLDR: In this article, the main objectives of this contribution are to promote various effect size measures in sport sciences through, once again, bringing to the readers' attention the benefits of reporting them, and to present examples of such estimates with a greater focus on those that can be calculated for non-parametric tests.

Abstract: Recent years have witnessed a growing number of published reports that point out the need for reporting various effect size estimates in the context of null hypothesis testing (H0) as a response to a tendency for reporting tests of statistical significance only, with less attention on other important aspects of statistical analysis. In the face of considerable changes over the past several years, neglect to report effect size estimates may be noted in such fields as medical science, psychology, applied linguistics, or pedagogy. Nor have sport sciences managed to totally escape the grips of this suboptimal practice: here statistical analyses in even some of the current research reports do not go much further than computing p-values. The p-value, however, is not meant to provide information on the actual strength of the relationship between variables, and does not allow the researcher to determine the effect of one variable on another. Effect size measures serve this purpose well. While the number of reports containing statistical estimates of effect sizes calculated after applying parametric tests is steadily increasing, reporting effect sizes with non-parametric tests is still very rare. Hence, the main objectives of this contribution are to promote various effect size measures in sport sciences through, once again, bringing to the readers’ attention the benefits of reporting them, and to present examples of such estimates with a greater focus on those that can be calculated for non-parametric tests