Abstract
The misuse and abuse of statistics in biomedical research are common and attributed to poor planning and limited knowledge of statistics by practitioners. This chapter examines the sources and prevention of the standard statistical faux pas in articles published in medical and allied health journals. It also discusses sample size estimation using web-based calculators, determinants, and strategies for selecting appropriate statistical tests, evaluation of the underlying assumptions for using parametric tests, parametric and non-parametric equivalent tests, and presentation of study outcomes. Undoubtedly, the errors highlighted in this chapter underscores the need for reforms in medical and health sciences education by increasing the emphasis and time allocated in the curriculum to research methods, biostatistics, and ethical issues in research. Application of the knowledge gained from this chapter will improve the quality and rigour of the statistics used in evidence-based research in medicine and allied health.
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References
Gore SM, Jones IG, Rytter EC. Misuse of statistical methods: critical assessment of articles in BMJ from January to March. BMJ. 1977;1:85–7. Available at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1604073/. Accessed 27 Dec 2018.
Thiese MS, Arnold ZC, Walker SD. The misuse and abuse of statistics in biomedical research. Biochem Med. 2015, 25;(1):5–11. Available at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4401313/. Accessed 27 Dec 2018.
Fanelli D. How many scientists fabricate and falsify research?. A systematic review and meta-analysis of survey data. PLoS ONE. 2009;4:e5738. https://doi.org/10.1371/journal.pone.0005738.
Sauerbrei W, Abrahamowicz M, Altman DG, Cessie S, Carpenter J. Strengthening analytical thinking for observational studies: the STRATOS initiative. Stat Med. 2014; https://doi.org/10.1002/sim.6265.
Lang TA, Altman DG. Basic statistical reporting for articles published in biomedical journals: the “statistical analyses and methods in the published literature” or the SAMPL guidelines.” Science editors’ handbook, European Association of Science Editors. 2013.
Strasak AM, Zaman Q, Pfeiffer KP, Göbel G. Statistical errors in medical research--a review of common pitfalls. Swiss Med Wkly. 2007;137(3–4):44–9. Available at https://pdfs.semanticscholar.org/f209/5ccffd99ff9457f782d283dc2b990cfdb7ba.pdf?_ga=2.21952571.1036601531.1545322665-1476164196.1545322665. Accessed 27 Dec 2018.
Ogee A, Ellis M, Scibilia B, Pammer C, Steele C, Keller D. Choosing between a nonparametric test and a parametric test. Available at http://blog.minitab.com/blog/adventures-in-statistics-2/choosing-between-a-nonparametric-test-and-a-parametric-test 2015. Accessed 27 Dec 2018.
Wayne W, LaMorte. When to use a nonparametric test. Available at http://sphweb.bumc.bu.edu/otlt/MPH-Modules/BS/BS704_Nonparametric/BS704_Nonparametric2.html. 2017. Accessed 27 Dec 2018.
Kellar SP, Kelvin EA. Munro’s statistical methods for healthcare research. 6th ed. Lippincott Williams & Wilkins; 2013.
Berger VW, Lunneborg CE, Ernst MD, Levin JG. Parametric analyses in randomised clinical trials. J Modern Appl Stat Methods. 2002;1(1):11, 74–82.
Burke DA, Whittemore SR, Magnuson DSK. Consequences of common data analysis inaccuracies in CNS trauma injury basic research. J Neurotrauma. 2013;30(10):797–805. Available at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3660075/. Accessed 22 Dec 2018.
Steiger JH. Outliers, leverage, and influence. n.d.. Available at http://www.statpower.net/Content/313/Lecture%20Notes/Outliers.pdf Accessed 22 Dec 2018.
Raj J. Mood’s median test. n.d. Available at https://www.youtube.com/watch?v=9Vu5aJblYpY. Accessed 22 Dec 2018.
SPSS Linear mixed effects model. Available at https://www.r-bloggers.com/beware-the-friedman-test/amp/. Accessed 22 Dec 2018.
Noguchi, et al. An R software package for the nonparametric analysis of longitudinal data in factorial experiments. J Stat Soft. 2012;50(12). Available at https://www.researchgate.net/publication/267559451_nparLD_An_R_Software_Package_for_the_Nonparametric_Analysis_of_Longitudinal_Data_in_Factorial_Experiments. Accessed 22 Dec 2018.
Zuur et al. Mixed effects models and extensions in ecology. Available at https://www.jstatsoft.org/article/view/v032b01/v32b01.pdf. Accessed 27 Dec 2018.
Saste SV, Sananse SL, Sonar CD. On parametric and nonparametric analysis of two-factor factorial experiment. Int J Appl Res. 2016;(2, 7):653–6. Available at http://www.allresearchjournal.com/archives/2016/vol2issue7/PartJ/2-6-122-317.pdf. Accessed 27 Dec 2018.
Cole TJ. Too many digits: the presentation of numerical data. Arch Dis Child. 2015;100(7):608–9. Available at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4483789/. Accessed 22 Dec 2018.
Simpson SH. Creating a data analysis plan: what to consider when choosing statistics for a study. CJHP. 2015;68(4):311–7. Available at https://www.cjhp-online.ca/index.php/cjhp/article/view/1471/2168. Accessed 7 June 2021.
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Balogun, J.A. (2021). The Common Statistical Faux Pas in Journal Publications. In: Okonofua, F., Balogun, J.A., Odunsi, K., Chilaka, V.N. (eds) Contemporary Obstetrics and Gynecology for Developing Countries . Springer, Cham. https://doi.org/10.1007/978-3-030-75385-6_68
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