A lot of philosophers and scientists seem to be talking about scientism these days–either championing it or worrying about it. What is it? It’s usually a pejorative term describing an unwarranted deference to the so-called scientific method over and above other methods of inquiry. Some push it as a way to combat postmodernism (is that even still around?) Stephen Pinker gives scientism a positive spin (and even offers it as a cure for the malaise of the humanities!). Anyway, I’m to talk at a conference on Scientism (*not statisticism, that’s my word) taking place in NYC May 16-17. It is organized by Massimo Pigliucci (chair of philosophy at CUNY-Lehman), who has written quite a lot on the topic in the past few years. Information can be found here. In thinking about scientism for this conference, however, I was immediately struck by this puzzle:
How can we worry about science being held in too high a regard when every day we’re confronted with articles shouting that “most scientific findings are false?”
Too much kowtowing toward science? Gee, in the fields I’m most closely involved, scarcely a day goes by where I’m not reading headlines: “Bad Science”, “Trouble in the Lab”, and “Science Fails to Self-correct.” Not to mention the “Crisis of Replication”.
The more I thought about it, I realized it was not really puzzling; yet my way of unraveling the puzzle points to a somewhat different direction than where writers on scientism appear heading. Even those of us utterly allergic to postmodernism can grant legitimate worries about scientism, and the most noteworthy of them, I say, grow out of methodological abuses of (broadly) statistical methodology—“lies, damned lies, and statistics.” Big data and high-powered computers allow statistical techniques to be performed with a click of a mouse in any “data driven” inquiry both in science and beyond (culturomics, philosophometrics)—but with all sorts of methodological-philosophical loopholes. It’s the false veneer of science, it’s statistics as window-dressing, that rightly bothers (most of) us; it’s the misuse and overreach of statistical methods, (QRPs) that is objectionable, as are presuppositions about “what we really, really want” in using probability to express and control errors.
Here’s the blurb I wrote before fleshing out any of the details….Send me your thoughts, ideally, by Saturday. (I may blog on the conference later on; if I update this, I’ll use (ii) in the title. See the update in a new post here.)
“The Science Wars and the Statistics Wars: scientism, popular statistics, and the philosophers”
I will explore the extent to which concerns about ‘scientism’– an unwarranted obeisance to scientific over other methods of inquiry– are intertwined with issues in the foundations of the statistical data analyses on which (social, behavioral, medical and physical) science increasingly depends. The rise of big data, machine learning, and high-powered computer programs have extended statistical methods and modeling across the landscape of science, law and evidence-based policy, but this has been accompanied by enormous hand wringing as to the reliability, replicability, and valid use of statistics. Legitimate criticisms of scientism often stem from insufficiently self-critical uses of statistical methodology, broadly construed—i.e., from what might be called “statisticism”– particularly when those methods are applied to matters of controversy.
- While provocative articles written for popular consumption give useful exposés of classic fallacies and foibles (p-values are not posterior probabilities, statistical significance is not substantive significance, association is not causation) they often lack a depth of understanding of underling philosophical, statistical, and historical issues.
- While “Big data” journalism offers novel ways to present information, that its correlational and causal headlines rely on a host of observable statistical associations and regressions may inadvertently allow biased or shaky claims to appear under the guise of hard-nosed, “just the facts” journalism.
Are philosophiesabout science relevant here? I say yes. To me, “getting philosophical” about uncertain inference is not articulating rarified concepts divorced from statistical practice, but providing tools to avoid obfuscating philosophically tinged notions about evidence, induction, testing, and objectivity/subjectivity, while offering a critical illumination of flaws and foibles surrounding technical statistical concepts. To warrant empirical methods of inquiry–both in day-to-day learning or science–demands assessing and controlling misleading, biased, and erroneous interpretations of data. But such a meta-level scrutiny is itself theory-laden–only here the theories are philosophical. Understanding and resolving these issues, I argue, calls for interdisciplinary work linking philosophers of science, statistical practitioners, and science journalists. Not only would this help to make progress in the debates–the science wars and the statistics wars–it would promote philosophies of science genuinely relevant for practice.
 See his 2013 New Republic article, “Science is not your enemy” here. But I wonder why he’s issuing: “an impassioned plea to neglected novelists, embattled professors, and tenure-less historians”. What does he want from the humanities anyway? Why is he trying to woo the tenure-less humanities professors? Surely they pose no threat to evolutionary psychology.
 Questionable research practices.
Interesting. There does seem to have been a shift to focus on these issues in recent years. My understanding is that earlier scientism complaints focused on a different class of problems from the ones targeted in big data/replicability/statistical testing criticisms; the frustration over “The Moral Landscape” comes to mind, with critics more concerned about science providing the wrong kind of explanation for what we’re interested in than they were about matters like reliability. I’m curious to see where this goes.