Ten Simple Rules for Effective Statistical Practice
Open accessAbstract
Several months ago, Phil Bourne, the initiator and frequent author of the wildly successful and incredibly useful “Ten Simple Rules” series, suggested that some statisticians put together a Ten Simple Rules article related to statistics. (One of the rules for writing a PLOS Ten Simple Rules article is to be Phil Bourne [1]. In lieu of that, we hope effusive praise for Phil will suffice.) Implicit in the guidelines for writing Ten Simple Rules [1] is “know your audience.” We developed our list of rules with researchers in mind: researchers having some knowledge of statistics, possibly with one or more statisticians available in their building, or possibly with a healthy do-it-yourself attitude and a handful of statistical packages on their laptops. We drew on our experience in both collaborative research and teaching, and, it must be said, from our frustration at being asked, more than once, to “take a quick look at my student’s thesis/my grant application/my referee’s report: it needs some input on the stats, but it should be pretty straightforward.” There are some outstanding resources available that explain many of these concepts clearly and in much more detail than we have been able to do here: among our favorites are Cox and Donnelly [2], Leek [3], Peng [4], Kass et al. [5], Tukey [6], and Yu [7]. Every article on statistics requires at least one caveat. Here is ours: we refer in this article to “science” as a convenient shorthand for investigations using data to study questions of interest. This includes social science, engineering, digital humanities, finance, and so on. Statisticians are not shy about reminding administrators that statistical science has an impact on nearly every part of almost all organizations.
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The probably riskiest assumption to violate (in terms of producing type I errors) is the assumption of independence of data points.
“The probably riskiest assumption to violate (in terms of producing type I errors) is the assumption of independence of data points (Forstmeier et al., 2017; Kass et al., 2016; Saravanan et al., 2020)”
From Violating the normality assumption may be the lesser of two evils (2021) · cites this paper as Kass et al., 2016
Rule 8 calls independence of observations a particularly worrisome assumption and says even small violations can have drastic effects. Rule 7 says overlooking dependencies usually substantially underestimates uncertainty. The paper does not specifically rank independence violations by their risk of producing type I errors. (LLM, conf=0.84)
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- Authors
- Kass, R. E., Caffo, B. S., Davidian, M., Meng, X.-L., Yu, B., & Reid, N.
- Affiliations
- Center for the Neural Basis of Cognition (US)
- Johns Hopkins University (US)
- North Carolina State University (US)
- Harvard University (US)
- University of California, Berkeley (US)
- University of Toronto (CA)
- Corresponding author
- Reid, Nancy
- Venue
- PLOS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2016-06-09
- Volume
- 12
- Issue
- 6
- Pages
- e1004961
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC4900655
- OpenAlex
- W2412645763
- Cited by
- 113
- References
- 20
- Citations by year
- 2026: 6 · 2025: 4 · 2024: 3 · 2023: 13 · 2022: 17 · 2021: 11 · 2020: 9 · 2019: 11 · 2018: 19 · 2017: 17 · 2016: 3
- FWCI
- 16.89
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- KiltHub Repository (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- eScholarship (California Digital Library) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- KiltHub Repository
- Funders
- National Science Foundation (CCF0-939370, 0939370, 1513492)
- National Institutes of Health (CCF-0939370, CA 142538)
- National Institute of Mental Health (R01MH064537, MH064537)
- National Cancer Institute (P01CA142538)
- National Institute of Biomedical Imaging and Bioengineering (R01EB012547)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistics Education and Methodologies
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- statistical practice, statistical literacy, research data analysis, statistical methods in research, statistics education
- MeSH terms
- Data Accuracy, Algorithms, Computer Simulation, Data Interpretation, Statistical, Models, Statistical, Computational Biology, Information Dissemination
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