Equivocations about “junk science” came up in today’s “critical thinking” class; if anything, the current situation is worse than 2 years ago when I posted this.
Have you ever noticed in wranglings over evidence-based policy that it’s always one side that’s politicizing the evidence—the side whose policy one doesn’t like? The evidence on the near side, or your side, however, is solid science. Let’s call those who first coined the term “junk science” Group 1. For Group 1, junk science is bad science that is used to defend pro-regulatory stances, whereas sound science would identify errors in reports of potential risk. For the challengers—let’s call them Group 2—junk science is bad science that is used to defend the anti-regulatory stance, whereas sound science would identify potential risks, advocate precautionary stances, and recognize errors where risk is denied. Both groups agree that politicizing science is very, very bad—but it’s only the other group that does it!
A given print exposé exploring the distortions of fact on one side or the other routinely showers wild praise on their side’s—their science’s and their policy’s—objectivity, their adherence to the facts, just the facts. How impressed might we be with the text or the group that admitted to its own biases?
Take, say, global warming, genetically modified crops, electric-power lines, medical diagnostic testing. Group 1 alleges that those who point up the risks (actual or potential) have a vested interest in construing the evidence that exists (and the gaps in the evidence) accordingly, which may bias the relevant science and pressure scientists to be politically correct. Group 2 alleges the reverse, pointing to industry biases in the analysis or reanalysis of data and pressures on scientists doing industry-funded work to go along to get along.
When the battle between the two groups is joined, issues of evidence—what counts as bad/good evidence for a given claim—and issues of regulation and policy—what are “acceptable” standards of risk/benefit—may become so entangled that no one recognizes how much of the disagreement stems from divergent assumptions about how models are produced and used, as well as from contrary stands on the foundations of uncertain knowledge and statistical inference. The core disagreement is mistakenly attributed to divergent policy values, at least for the most part.
Over the years I have tried my hand in sorting out these debates (e.g., Mayo and Hollander 1991). My account of testing actually came into being to systematize reasoning from statistically insignificant results in evidence based risk policy: no evidence of risk is not evidence of no risk! (see October 5). Unlike the disputants who get the most attention, I have argued that the current polarization cries out for critical or meta-scientific scrutiny of the uncertainties, assumptions, and risks of error that are part and parcel of the gathering and interpreting of evidence on both sides. Unhappily, the disputants tend not to welcome this position—and are even hostile to it. This used to shock me when I was starting out—why would those who were trying to promote greater risk accountability not want to avail themselves of ways to hold the agencies and companies responsible when they bury risks in fallacious interpretations of statistically insignificant results? By now, I am used to it.
This isn’t to say that there’s no honest self-scrutiny going on, but only that all sides are so used to anticipating conspiracies of bias that my position is likely viewed as yet another politically motivated ruse. So what we are left with is scientific evidence having less and less a role in constraining or adjudicating disputes. Even to suggest an evidential adjudication risks being attacked as a paid insider.
I agree with David Michaels (2008, 61) that “the battle for the integrity of science is rooted in issues of methodology,” but winning the battle would demand something that both sides are increasingly unwilling to grant. It comes as no surprise that some of the best scientists stay as far away as possible from such controversial science.
Mayo,D. and Hollander. R. (eds.). 1991. Acceptable Evidence: Science and Values in Risk Management, Oxford.
Mayo. 1991. Sociological versus Metascientific Views of Risk Assessment, in D. Mayo and R. Hollander (eds.), Acceptable Evidence: 249-79.
Michaels, D. 2008. Doubt Is Their Product, Oxford.
Interesting post. On my blog, I have tried to call out both Group I and Group II. I agree that it is revealing that some political conservatives express profound distrust of observational studies, but then embrace rather doubtful ones when those studies suggest associations between abortion and depression or breast cancer. Clearly, the examples can be multiplied on both sides.
I would have hoped that improved understanding of statistical inference, and of meta-analytic techniques, prevent either Group from declaring victory solely because there are multiple study results without statistically significant results. Most of the disputes you reference I believe involve observational studies, and for such studies, internal and external validity considerations are often much more important sources of error than incorrect interpretation of statistical results. Bias and confounding in PM2.5 epidemiology of cardiovascular diseases certainly come to mind.