Best Practices for Statistical Analysis

On this page we list a few resources for developing best practices in statistics and avoiding common pitfalls. Be sure to check the references in these articles for even more resources.


General articles

These articles apply to just about anyone performing statistical analyses.

Ten Simple Rules for Effective Statistical Practice
Kass RE, Caffo BS, Davidian M, Meng X-L, Yu B, Reid N (2016) PLoS Comput Biol 12(6): e1004961. doi:10.1371/journal.pcbi.1004961.
http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004961

The ASA’s statement on p-values: context, process, and purpose
Wasserstein RL, Lazar NA (2016) The American Statistician doi: 10.1080/00031305.2016.1154108.
http://www.tandfonline.com/doi/full/10.1080/00031305.2016.1154108

Moving to a World Beyond “p < 0.05"
Wasserstein RL, Schirm AL, Lazar NA (2019) The American Statistician doi: 10.1080/00031305.2019.1583913
https://www.tandfonline.com/doi/full/10.1080/00031305.2019.1583913

Statistical tests, confidence intervals, and power: A guide to misinterpretations
Greenland S, Senn SJ, Rothman KJ et al. (2016) Eur J Epidemiol 31: 337. doi:10.1007/s10654-016-0149-3.
http://link.springer.com/article/10.1007/s10654-016-0149-3

Statistical Problems to Document and to Avoid
Harrell F (16 Jul 2014) Vanderbilt Biostatistics Wiki
http://biostat.mc.vanderbilt.edu/wiki/Main/ManuscriptChecklist

Reference Collection to push back against “Common Statistical Myths”
Althouse A, Harrell F, datamethods.org Wiki
https://discourse.datamethods.org/t/reference-collection-to-push-back-against-common-statistical-myths/1787

Six Persistent Research Misconceptions
Rothman K (2014) J Gen Intern Med. 2014 Jul; 29(7): 1060–1064. doi: 10.1007/s11606-013-2755-z
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4061362/

Good enough practices in scientific computing
Wilson G, Bryan J, Cranston K, Kitzes J, Nederbragt L, Teal TK (2017) Good enough practices in scientific computing. PLoS Comput Biol 13(6): e1005510.
https://doi.org/10.1371/journal.pcbi.1005510

Language for communicating frequentist results about treatment effects
Harrell F (Nov 2018) datamethods.org
https://discourse.datamethods.org/t/language-for-communicating-frequentist-results-about-treatment-effects/934

Data Organization in Spreadsheets
Broman K, Woo K (2018). The American Statistician. Volume 72, 2018 – Issue 1: Special Issue on Data Science.
https://doi.org/10.1080/00031305.2017.1375989


Domain-specific articles

These articles cite examples from a specific domain but are nevertheless accessible and useful to researchers in other domains.

Statistics in a Horticultural Journal: Problems and Solutions
Kramer MH, et al. (2016) J. Amer. Soc. Hort. Sci. 141(5):400–406.
http://journal.ashspublications.org/content/141/5/400.full

Common scientific and statistical errors in obesity research
George, B. J., et al. (2016) Obesity, 24: 781–790. doi:10.1002/oby.21449.
http://onlinelibrary.wiley.com/doi/10.1002/oby.21449/full

Translating statistical findings into plain English
Pocock, S.J., and Ware, J.H. (2009) The Lancet, 373, 1926–1928. doi: 10.1016/S0140-6736(09)60499-2.
http://www.sciencedirect.com/science/article/pii/S0140673609604992

Childhood obesity intervention studies: A narrative review and guide for investigators, authors, editors, reviewers, journalists, and readers to guard against exaggerated effectiveness claims
Brown, A.W., et al. (2019) Obesity Reviews, 20(11):1523-1541. doi: 10.1111/obr.12923
https://doi.org/10.1111/obr.12923

Degrees of Freedom in Planning, Running, Analyzing, and Reporting Psychological Studies: A Checklist to Avoid p-Hacking
Wicherts, J.M., et al. (2016) Front Psychol. 2016; 7: 1832. doi: 10.3389/fpsyg.2016.01832
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5122713/