limited shelf validity

A great article from Steve Stigler in the new, multi-scaled, and so exciting Harvard Data Science Review magisterially operated by Xiao-Li Meng, on the limitations of old datasets. Illustrated by three famous datasets used by three equally famous statisticians, Quetelet, Bortkiewicz, and Gosset. None of whom were fundamentally interested in the data for their own sake. First, Quetelet’s data was (wrongly) reconstructed and missed the opportunity to beat Galton at discovering correlation. Second, Bortkiewicz went looking (or even cherry-picking!) for these rare events in yearly tables of mortality minutely divided between causes such as military horse kicks. The third dataset is not Guinness‘, but a test between two sleeping pills, operated rather crudely over inmates from a psychiatric institution in Kalamazoo, with further mishandling by Gosset himself. Manipulations that turn the data into dead data, as Steve put it. (And illustrates with the above skull collection picture. As well as warning against attempts at resuscitating dead data into what could be called “zombie data”.)

“Successful resurrection is only slightly more common than in Christian theology.”

His global perspective on dead data is that they should stop being used before extending their (shelf) life, rather than turning into benchmarks recycled over and over as a proof of concept. If only (my two cents) because it leads to calibrate (and choose) methods doing well over these benchmarks. Another example that could have been added to the skulls above is the Galaxy Velocity Dataset that makes frequent appearances in works estimating Gaussian mixtures. Which Radford Neal signaled at the 2001 ICMS workshop on mixture estimation as an inappropriate use of the dataset since astrophysical arguments weighted against a mixture modelling.

“…the role of context in shaping data selection and form—context in temporal, political, and social as well as scientific terms—has been shown to be a powerful and interesting phenomenon.”

The potential for “dead-er” data (my neologism!) increases with the epoch in that the careful sleuth work Steve (and others) conducted about these historical datasets is absolutely impossible with the current massive data sets. Massive and proprietary. And presumably discarded once the associated neural net is designed and sold. Letting the burden of unmasking the potential (or highly probable?) biases to others. Most interestingly, this recoups a “comment” in Nature of 17 October by Sabina Leonelli on the transformation of data from a national treasure to a commodity which “ownership can confer and signal power”. But her call for openness and governance of research data seems as illusory as other attempts to sever the GAFAs from their extra-territorial privileges…