The Numbers Will Love You Back in Return-I Promise

被引:87
作者
Buchheit, Martin [1 ]
机构
[1] Paris St Germain Football Club, Performance Dept, St Germain En Laye, France
关键词
magnitude-based inferences; null-hypothesis significance testing; sample size; trivial effect; smallest important effect; MAGNITUDE-BASED INFERENCE; RUNNING PERFORMANCE; FOOTBALL; STRENGTH; JUMP;
D O I
10.1123/IJSPP.2016-0214
中图分类号
Q4 [生理学];
学科分类号
071003 ;
摘要
The first sport-science-oriented and comprehensive paper on magnitude-based inferences (MBI) was published 10 y ago in the first issue of this journal. While debate continues, MBI is today well established in sport science and in other fields, particularly clinical medicine, where practical/clinical significance often takes priority over statistical significance. In this commentary, some reasons why both academics and sport scientists should abandon null-hypothesis significance testing and embrace MBI are reviewed. Apparent limitations and future areas of research are also discussed. The following arguments are presented: P values and, in turn, study conclusions are sample-size dependent, irrespective of the size of the effect; significance does not inform on magnitude of effects, yet magnitude is what matters the most; MBI allows authors to be honest with their sample size and better acknowledge trivial effects; the examination of magnitudes per se helps provide better research questions; MBI can be applied to assess changes in individuals; MBI improves data visualization; and MBI is supported by spreadsheets freely available on the Internet. Finally, recommendations to define the smallest important effect and improve the presentation of standardized effects are presented.
引用
收藏
页码:551 / 554
页数:4
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