Best Practices for Statistical Data 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 and books 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

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.

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

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.

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

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


The UVa Library provides electronic access to these books for UVa students, faculty and staff.

Common Errors in Statistics (and How to Avoid Them) (4th ed.)
Good, P. and Hardin, J. W. (2012). Wiley.
Online Access for UVa community

Statistics Done Wrong: The Woefully Complete Guide
Reinhart, A. (2015). No Starch Press.
Online Access for UVa community