Key Attributes of a Modern Statistical Computing Tool


Computer and Information Sciences



Document Type



bootstrap, data visualization, exploratory data analysis, randomization, reproducibility, software design, software evaluation




In the 1990s, statisticians began thinking in a principled way about how computation could better support the learning and doing of statistics. Since then, the pace of software development has accelerated, advancements in computing and data science have moved the goalposts, and it is time to reassess. Software continues to be developed to help do and learn statistics, but there is little critical evaluation of the resulting tools, and no accepted framework with which to critique them. This article presents a set of attributes necessary for a modern statistical computing tool. The framework was designed to be broadly applicable to both novice and expert users, with a particular focus on making more supportive statistical computing environments. A modern statistical computing tool should be accessible, provide easy entry, privilege data as a first-order object, support exploratory and confirmatory analysis, allow for flexible plot creation, support randomization, be interactive, include inherent documentation, support narrative, publishing, and reproducibility, and be flexible to extensions. Ideally, all these attributes could be incorporated into one tool, supporting users at all levels, but a more reasonable goal is for tools designed for novices and professionals to "reach across the gap," taking inspiration from each others' strengths.

Published in

The American Statistician

Citation/Other Information

McNamara, A. (2019). Key Attributes of a Modern Statistical Computing Tool. The American Statistician, 73(4). 375-384. https://doi.org/10.1080/00031305.2018.1482784

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.