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.
Hilary Parker argues that it’s critical to teach students 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.
Hilary Parker is a data scientist at Stitch Fix and cofounder of the Not So Standard Deviations podcast. Hilary focuses on R, experimentation, and rigorous analysis development methods such as reproducibility. Previously, she was a senior data analyst at Etsy. Hilary holds a PhD in biostatistics from the Johns Hopkins Bloomberg School of Public Health. Hilary can be found on Twitter at @hspter.
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