Abstract
Current research ideologies in sport science allow for the possibility of investigators producing statistically significant results to help fit the outcome into a predetermined theory. Additionally, under the current Neyman-Pearson statistical structure, some argue that null hypothesis significant testing (NHST) under the frequentist approach is flawed, regardless. For example, a p-value is unable to measure the probability that the studied hypothesis is true, unable to measure the size of an effect or the importance of a result, and unable to provide a good measure of evidence regarding a model or hypothesis. Many of these downfalls are key questions researchers strive to answer following an investigation. Therefore, a shift towards a magnitude-based inference model, and eventually a fully Bayesian framework, is thought to be a better fit from a statistical standpoint and may be an improved way to address biases within the literature. The goal of this article is to shed light on the current research and statistical shortcomings the field of sport science faces today, and offer potential solutions to help guide future research practices.
Highlights
While such initiatives act as a first step, a shift in the current statistical framework may be a better solution to ensure the field of sport science continually progresses
The claim that magnitude-based inference increases the bias of a decision from the researcher does not hold substance as bias has already seeped into science under the current null hypothesis significant testing (NHST) framework
While the shift towards a magnitude-based inference model may act as a better fit for inference in sport science, a commitment towards a fully Bayesian model may act as a better solution for small effects and small sample sizes [38]
Summary
The common goal of many researchers remains the same, the validity of the sport science body of literature may be in question because of common research practices and the current statistical framework applied [1,2,3]. Journals are beginning to address existing research and statistical practices through journal wide initiatives to increase the reproducibility of results found in the literature While such initiatives act as a first step, a shift in the current statistical framework may be a better solution to ensure the field of sport science continually progresses. It is plausible that a future reader will be interested in expanding on the topic, even though the effect of the treatment may have been unsuccessful, but only appeared to be effective following manipulation of the data Repeat this process over and over, and the body of literature can venture down research avenues that are based off an original study that had no true effect to begin with. An alternative research and statistical model may better suit our field
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