A new course at the University of Washington, in Seattle, was filled within minutes of registrations being opened. A hundred and fifty students signed up for Calling Bullshit in the Age of Big Data, taught by information scientist Jevin West and biologist Carl Bergstrom.
As explained in the New Yorker:
Humans are pretty good at detecting verbal bullshit. Members of the species have, after all, been talking rot for millennia, and its warning signs are well known. Bullshit expressed as data, on the other hand, is relatively new outside scientific circles. Multivariate graphs didn’t begin to appear in the popular press until the nineteen-eighties, and only in the past decade, as smartphones and other information-gathering devices have accelerated the accumulation of Big Data, have complex visualisations been routinely presented to the general public. While data can be used to tell remarkably deep and memorable stories…its apparent sophistication and precision can effectively disguise a great deal of bullshit.
The article summarises nine tips from the course instructors for detecting BS in data, such as:
Beware of Big Data hubris. TheGoogle Flu Trends project, which claimed, with much fanfare, to anticipate seasonal flu outbreaks by tracking user searches for flu-related terms, proved to be a less reliable predictor of outbreaks than a simple model of local temperatures. (One problem was that Google’s algorithm was hoodwinked by meaningless correlations—between flu outbreaks and high-school basketball seasons, for example, both of which occur in winter.) Like all data-based claims, if an algorithm’s abilities sound too good to be true, they probably are.