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Comment on Replacing Statistical Significance…

Ross D Neville

Sportscience 26, sportsci.org/2022/rdn.htm, 2022
School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland. Email.

I very much welcome and appreciate this article on sampling uncertainty, both for clarifying and updating the current status of the MBI/MBD vs NHST debate, and also for situating both MBI/MBD and NHST within the broader suite of current state-of-the-art approaches to interpreting effects. The first line says it all really: "a sample provides only an approximate estimate of the magnitude of an effect". Sport scientists therefore need to understand, be upfront about, and know how to effectively report and interpret sampling uncertainty in their manuscripts.

As I read through the article, my first thoughts were related to marketing: who is this article aimed at? I think it will benefit anyone interested in alternatives to statistical significance, be they students or experienced researchers. In this respect, the article is more valuable than an adversarial piece written to put "establishment" detractors of MBI/MBD in their place. The article is about sampling uncertainty, not primarily about NHST or MBI/MBD. Rightly so.

Other thoughts were related to the philosophy of science. Will states that "authors interpret significance as real, meaningful, worthwhile, important, useful, beneficial, harmful, or otherwise substantial". This list encompasses different philosophies of science (e.g., realism, pragmatism, instrumentalism, consequentialism) that should not all be lumped together. Furthermore, I don't think that Will’s comment about people regarding a significant effect as "real" accurately captures the attitude of most sport scientists towards their data. It’s probably more like "I need significance so I can publish", evidence of which is the upward bias of magnitudes of published statistically significant effects in small samples. So, their motivation may be more about outputs than understanding. Confirmation bias is another issue here, and p <0.05 is an easy way for authors to "confirm" their preconceived beliefs: "I expected an effect, I found it, I published it, and the finding is attributed to me."

Finally, I’m not qualified enough to emphatically state, as Will and many others have, that NHST should be retired. But having read this article, I think that sport scientists should be convinced (as I am) that NHST provides neither necessary nor sufficient evidence when assessing effect magnitudes. Sport scientists should therefore analyze and present their data (as I do) using alternative approaches.

 

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Published August 2022.

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