Statistical Analysis in Sport & Exercise Science
Altman, D. G., & Bland, J. M. (1995). Statistics notes: Absence of evidence is not evidence of absence. Bmj, 311(7003), 485. https://doi.org/10.1136/bmj.311.7003.485
Amrhein, V., Greenland, S., & McShane, B. (2019). Scientists rise up against statistical significance. Nature, 567(7748), 305-307. https://doi.org/10.1038/d41586-019-00857-9
Atkinson, G., & Nevill, A. M. (1998). Statistical methods for assessing measurement error (reliability) in variables relevant to sports medicine. Sports Medicine, 26(4), 217-238. doi.org/10.2165/00007256-199826040-00002
Bailey, C. (2019). Longitudinal Monitoring of Athletes: Statistical Issues and Best Practices. Journal of Science in Sport and Exercise, 1-11. doi.org/10.1007/s42978-019-00042-4
Conway, B. (2017) Smallest Worthwhile Change - https://www.scienceforsport.com/smallest-worthwhile-change/
Drake, D., Kennedy, R., & Wallace, E. (2018). Familiarization, validity and smallest detectable difference of the isometric squat test in evaluating maximal strength. Journal of sports sciences, 36(18), 2087-2095. doi.org/10.1080/02640414.2018.1436857
Flanagan, E. P. (2013). The effect size statistic—Applications for the strength and conditioning coach. Strength & Conditioning Journal, 35(5), 37-40.
Greenland, S., Senn, S. J., Rothman, K. J., Carlin, J. B., Poole, C., Goodman, S. N., & Altman, D. G. (2016). Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. European journal of epidemiology, 31(4), 337-350. https://doi.org/10.1007/s10654-016-0149-3
Halsey, L. (2019) The reign of the p-value is over: what alternative analyses could we employ to fill the power vacuum? Biol. Lett. 15: 20190174 https://doi.org/10.1098/rsbl.2019.0174
Hanel, P. H., Maio, G. R., & Manstead, A. S. (2018). A new way to look at the data: Similarities between groups of people are large and important. Journal of personality and social psychology. http://dx.doi.org/10.1037/pspi0000154
Kelley, K., & Preacher, K. J. (2012). On effect size. Psychological Methods, 17(2), 137. http://dx.doi.org/10.1037/a0028086
Lakens, D. (2021). The practical alternative to the p-value is the correctly used p-value. Perspectives on Psychological Science https://doi.org/10.1177/1745691620958012
Nead, K. T., Wehner, M. R., & Mitra, N. (2018). The use of “trend” statements to describe statistically nonsignificant results in the oncology literature. JAMA Oncology, 4(12), 1778-1779. doi:10.1001/jamaoncol.2018.4524
Perugini, M., Gallucci, M., & Costantini, G. (2018). A practical primer to power analysis for simple experimental designs. International Review of Social Psychology, 31(1). http://doi.org/10.5334/irsp.181
Rafi, Z., & Greenland, S. (2020). Semantic and cognitive tools to aid statistical science: replace confidence and significance by compatibility and surprise. BMC medical research methodology, 20(1), 1-13. https://doi.org/10.1186/s12874-020-01105-9
Sauret, J. (2018) Reliability - https://www.scienceforsport.com/reliability/
Spiegelhalter, D. (2019) The Art of Statistics: Learning from Data. Pelican Books
Stark, P. B., & Saltelli, A. (2018). Cargo‐cult statistics and scientific crisis. Significance, 15(4), 40-43.
Sullivan, G. M., & Feinn, R. (2012). Using effect size—or why the P value is not enough. Journal of graduate medical education, 4(3), 279-282. https://doi.org/10.4300/JGME-D-12-00156.1
Till, K., Morris, R., Emmonds, S., Jones, B., & Cobley, S. (2018). Enhancing the evaluation and interpretation of fitness testing data within youth athletes. Strength & Conditioning Journal, 40(5), 24-33. doi: 10.1519/SSC.0000000000000414
Turner, A. N., Jones, B., Stewart, P., Bishop, C., Parmar, N., Chavda, S., & Read, P. (2019). Total score of athleticism: Holistic athlete profiling to enhance decision-making. Strength & Conditioning Journal, 41(6), 91-101. doi: 10.1519/SSC.0000000000000506
Turner, A., Brazier, J., Bishop, C., Chavda, S., Cree, J., & Read, P. (2015). Data analysis for strength and conditioning coaches: Using excel to analyze reliability, differences, and relationships. Strength & Conditioning Journal, 37(1), 76-83. http://dx.doi.org/10.1519/SSC.0000000000000113
Funder, D. C., & Ozer, D. J. (2019). Evaluating effect size in psychological research: Sense and nonsense. Advances in Methods and Practices in Psychological Science, 2515245919847202. https://doi.org/10.1177/2515245919847202
Wasserstein, R. L., & Lazar, N. A. (2016). The ASA statement on p-values: context, process, and purpose. The American Statistician, 70(2), 129-133. https://doi.org/10.1080/00031305.2016.1154108
Weissgerber, T. L., Winham, S. J., Heinzen, E. P., Milin-Lazovic, J. S., Garcia-Valencia, O., Bukumiric, Z., ... & Milic, N. M. (2019). Reveal, don’t conceal: transforming data visualization to improve transparency. Circulation, 140(18), 1506-1518. https://doi.org/10.1161/CIRCULATIONAHA.118.037777
Weir, J. P. (2005). Quantifying test-retest reliability using the intraclass correlation coefficient and the SEM. The Journal of Strength & Conditioning Research, 19(1), 231-240. DOI: 10.1519/15184.1
Field, A. (2013). Discovering statistics using IBM SPSS statistics. Sage [google books]
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: data mining, inference, and prediction, New York: Springer. [full text]
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning: with applications in R. New York: Springer. [full text]
Maxwell, S. E., Delaney, H. D., & Kelley, K. (2017). Designing experiments and analyzing data: A model comparison perspective. Routledge. [preview]
Dienes, Z. (2008). Understanding psychology as a science: An introduction to scientific and statistical inference. Macmillan International Higher Education.
Statistical Problems to Document and to Avoid by Vanderbilt University https://biostat.app.vumc.org/wiki/Main/ManuscriptChecklist
Glossary of Statistical Terms https://hbiostat.org/doc/glossary.pdf
CrashCourse in Statistics -https://www.youtube.com/playlist?list=PL8dPuuaLjXtNM_Y-bUAhblSAdWRnmBUcr
RealRisk - https://realrisk.wintoncentre.uk/ -RealRisk is suitable for anyone working to communicate research findings from the medical & social sciences to a broad audience.
Stathand - which stats analysis to use - https://www.stathand.net/
Cairo, A. (2016). The truthful art: Data, charts, and maps for communication. New Riders.
McCrum-Gardner, E. (2008). Which is the correct statistical test to use?. British Journal of Oral and Maxillofacial Surgery, 46, 38-41.
Travers et al. (2019) Should this systematic review and meta-analysis change my practice? Part 1: exploring treatment effect and trustworthiness BJSM http://dx.doi.org/10.1136/bjsports-2018-099958
Travers et al. (2019) Should this systematic review and meta-analysis change my practice? Part 2: exploring the role of the comparator, diversity, risk of bias and confidence BJSM http://dx.doi.org/10.1136/bjsports-2018-099959
Morrow, J.R., Jackson, A.W., Disch, J.G. and Mood, D.P. (2011) Measurement and Evaluation in Human Performance (4th Ed.) Champaign, IL; Human Kinetics [google books]
Newell, J., Aitchison, T and Grant, S. (2010) Statistics for Sports and Exercise Science. Harlow: Pearson Education Ltd.[companion website]
Dancey, C. P., Reidy, J. G. and Rowe, R. (2012) Statistics for the Health Sciences: A Non-Mathematical Introduction. London: Sage [companion website]
Salkind, N.J. (2013) Statistics for people who think they hate statistics. London: Sage [companion website]
Reinhart, A. (n.d.) Statistics Done Wrong [click here]
Hopkins, W., Marshall, S., Batterham, A., & Hanin, J. (2009). Progressive statistics for studies in sports medicine and exercise science. Medicine & Science in Sports & Exercise, 41(1), 3.[full text]
How to choose a statistical test. An interactive RLO [click here]
StatsDirect - glossary and descriptions of most statistical analyses
A Statistics Dictionary from answers.com
Whichtest.info An app for ipad that helps users to decide on the most suitable statistical analysis
Sportsci.org - Dr Will Hopkins resource is thorough and uses many examples from the world of Sport Science
Probability associated with inferential statistics - An RLO by David Justham & Heather Wharrad
Descriptive Statistics for Interval and ratio - An RLO by David Justham & Heather Wharrad
A range of stats resources are also available at http://www.stats-class.com/default2.htm
http://www.stats-class.com/default2.htm - A series of lecture notes, links and tasks.
SPSS - online training workshop from developed by Dr Carl Lee, Dr Felix Famoye of Central Michigan Univ.
http://www.badscience.net/category/statistics/ - Ben Goldacre's site to accompany the book of the same name.
Getting Started with SPSS - Free Online OU Course
FutureLearn - Data to Insight: An Introduction to Data Analysis and Visualisation - Free Online Course
Bayesian Statistics (an introduction) - Free Online OU Course
Excel Tricks for Sports - A YouTube Channel - https://www.youtube.com/channel/UCagflprv_C-UPPdzSJ0bMCA
Improving Your Statistical Questions - https://www.coursera.org/learn/improving-statistical-questions
Improving your Statistical Inferences - https://www.coursera.org/learn/statistical-inferences
Watch a video
Adam Virgile has a growing collection of videos on the use of MS Excel for Sport & Exercise Science
Anthony Turner has produced some excellent videos to complement some of his publications on the use of statistics in Strength & Conditioning and the full set can be found here. Below are a few choice videos.