Aaron is an exercise physiologist by training with a Ph.D. in Health, Sport, and Exercise Science from the University of Arkansas. His current job is focused on human performance in extreme environments (heat, cold, and altitude). In addition, he has expanded his skillset to include applied statistics by completing a graudate certificate in statistics in research methods at the University of Arkansas and is currently continuing his education with a M.Sc. in Analytics at the Georgia Institute of Technology.
Aaron’s statistics and coding work can be found on GitHub
Learn about his academic work on ResearchGate.
Ph.D. in Health, Sport, and Exercise Science, 2019
University of Arkansas
M.Sc. in Kinesiology, 2015
Texas Christian University
B.Sc. in Exercise Science, 2009
Experienced R user
Beginner Python user
Graduate Certificate in Statistics; working on M.Sc.
Extensive background in applied physiology research
The average environmental and occupational physiologist may find statistics are difficult to interpret and use since their formal training in statistics is limited. Unfortunately, poor statistical practices can generate erroneous or at least misleading results and distorts the evidence in the scientific literature. These problems are exacerbated when statistics are used as thoughtless ritual that is performed after the data are collected. The situation is worsened when statistics are then treated as strict judgements about the data (i.e., significant versus non-significant) without a thought given to how these statistics were calculated or their practical meaning. We propose that researchers should consider statistics at every step of the research process whether that be the designing of experiments, collecting data, analysing the data or disseminating the results. When statistics are considered as an integral part of the research process, from start to finish, several problematic practices can be mitigated. Further, proper practices in disseminating the results of a study can greatly improve the quality of the literature. Within this review, we have included a number of reminders and statistical questions researchers should answer throughout the scientific process. Rather than treat statistics as a strict rule following procedure we hope that readers will use this review to stimulate a discussion around their current practices and attempt to improve them.