Thirty years ago, the Princeton philosopher Harry Frankfurt published an essay in an obscure academic journal, Raritan. The essay’s title was “On Bullshit”. (Much later, it was republished as a slim volume that became a bestseller.) Frankfurt was on a quest to understand the meaning of bullshit — what was it, how did it differ from lies, and why was there so much of it about?
Frankfurt concluded that the difference between the liar and the bullshitter was that the liar cared about the truth — cared so much that he wanted to obscure it — while the bullshitter did not. The bullshitter, said Frankfurt, was indifferent to whether the statements he uttered were true or not. “He just picks them out, or makes them up, to suit his purpose.”
Statistical bullshit is a special case of bullshit in general, and it appears to be on the rise. This is partly because social media — a natural vector for statements made purely for effect — are also on the rise. On Instagram and Twitter we like to share attention-grabbing graphics, surprising headlines and figures that resonate with how we already see the world. Unfortunately, very few claims are eye-catching, surprising or emotionally resonant because they are true and fair. Statistical bullshit spreads easily these days; all it takes is a click.
Tim Harford, “How politicans poisoned statistics”, TimHarford.com, 2016-04-20.
January 8, 2018
QotD: Differentiating between lies and (political) bullshit
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