Opinionated analysis development
- Published
- Accepted
- Subject Areas
- Statistics, Human-Computer Interaction, Computational Science, Data Science
- Keywords
- statistics, data science, python, rstats, reproducibility, opinionated software, software engineering, blameless postmortems
- Copyright
- © 2017 Parker
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2017. Opinionated analysis development. PeerJ Preprints 5:e3210v1 https://doi.org/10.7287/peerj.preprints.3210v1
Abstract
Traditionally, statistical training has focused primarily on mathematical derivations and proofs of statistical tests. The process of developing the technical artifact—that is, the paper, dashboard, or other deliverable—is much less frequently taught, presumably because of an aversion to cookbookery or prescribing specific software choices. In this paper I argue that it’s critical to teach analysts how to go about developing an analysis in order to maximize the probability that their analysis is reproducible, accurate, and collaborative. A critical component of this is adopting a blameless postmortem culture. By encouraging the use of and fluency in tooling that implements these opinions, as well as a blameless way of correcting course as analysts encounter errors, we as a community can foster the growth of processes that fail the practitioners as infrequently as possible.
Author Comment
This is part of the 'Practical Data Science for Stats' Collection. This paper is also being considered for publication in The American Statistician.