Unlike that famous bank teller, I’m not “active in the feminist movement,” but I’ve always considered myself a feminist, ever since I heard the term (I don’t know when that was, maybe when I was 10 or so?). It’s no big deal, it probably just comes from having 2 big sisters and growing up during the 1970s.
And most of the time this attitude is pretty much irrelevant to my professional life. It comes up every now and then when interpreting research claims (see here, for example) in which the male perspective is taken as the baseline. And when I teach I try to avoid overuse of stereotypically male-interest topics such as sports.
And my feminism has made me somewhat immune to simplistic gender-essentialist ideas such as expressed in various papers that make use of schoolyard evolutionary biology [see definition below] that we’ve discuss over the years on this blog.
But it doesn’t affect my approach for partial pooling in hierarchical models, or my approach to inference from non-random samples, or the ways in which I monitor convergence for Hamiltonian Monte Carlo, or my models for voting, etc etc etc. Most of my research, even in political science, is basically “orthogonal” to feminism. Even studies that could have some sort of feminist interpretation—for example, my analysis with Yair of differences in attitudes toward abortion, or our estimate of geographic variation in the gender gap—doesn’t have any feminist content at all, at least not that I notice.
Recently, though, I had a research project where a feminist perspective made (a bit of) a difference. It was from my paper with Christian Hennig on going beyond objectivity and subjectivity in statistical thinking.
It came up near the beginning of the paper. We start off by discussing the usual dichotomy in statistics between objective and subjective approaches:
Statistical discourse on objectivity and subjectivity is at an impasse. Ideally these concepts would be part of a consideration of the role of different sorts of information and assumptions in statistical analysis, but instead they often seemed to be used in restrictive and misleading ways.
One problem is that the terms “objective” and “subjective” are loaded with so many associations and are often used in a mixed descriptive/normative way. Scientists whose methods are branded as subjective have the awkward choice of either saying, No, we are really objective, or else embracing the subjective label and turning it into a principle. From the other direction, scientists who use methods labeled as objective often seem so intent on eliminating subjectivity from their analyses, that they end up censoring themselves. This happens, for example, when researchers rely on p-values but refuse to recognize that their analyses are contingent on data (as discussed by Simmons, Nelson, and Simonsohn, 2011, and Gelman and Loken, 2014). More generally, misguided concerns about subjectivity can lead researchers to avoid incorporating relevant and available information into their analyses.
And then we say this:
A perhaps helpful analogy is to gender roles in social interactions. To get respect, women often need to choose between claiming stereotypically-male behaviors or affirming, or “taking back,” feminine roles. At the same time, men can find it difficult to step outside the restrictions implied by traditional masculinity. Rather than point and label, it can be better in such situations to identify the positive aspects of each sex role and then go from there. Similarly, good science contains both subjective and objective elements, and we think it would be best to understand how these perspectives can complement each other.
I suspect that, to many readers, that paragraph won’t fit in at all. But to me it makes a lot of sense. Conventional labels, whether of objectivity and subjectivity, or of masculine and feminine, can be a trap. The labels are not empty, they reflect real differences (being a feminist does not is all about understanding, not denying, the real differences that exist on average between the sexes—along with recognizing that averages are just that, and don’t represent all cases), but people can also get stuck in these boxes, or get stuck trying to rearrange these boxes. So, to me, a feminist attitude gave me a useful perspective on how to think about the important topic of objectivity and subjectivity in science and statistics. (And it’s a topic with real applications; see for example this paper which discusses how we use model checking to incorporate both subjective and objective elements into a Bayesian analysis in tosicology.)
Just to be clear: I’m not claiming that feminism is purely a good thing for a researcher, or even that it’s purely good for my research. There may well be important work that I’m missing, or misunderstanding, because of my political biases. I think everyone must have such blind spots, but that doesn’t excuse me from the blind spots that I have.
At some level, in this post I’m making the unremarkable point that each of us has a political perspective which informs our research in positive and negative ways. The reason that this particular example of the feminist statistician is interesting is that it’s my impression that feminism, like religion, is generally viewed as a generally anti-scientific stance. I think some of this attitude comes from some feminists themselves who are skeptical of science in that is a generally male-dominated institution that is in part used to continue male dominance of society, and it also comes from people such as Larry Summers who might say that reality has an anti-feminist bias.
Feminism, like religion, can be competitive with science or it can be collaborative. See, for example, the blog of Echidne for a collaborative approach. To the extent that feminism represents a set of tenets are opposed to reality, it could get in the way of scientific thinking, in the same way that religion would get in the way of scientific thinking if, for example, you tried to apply faith healing principles to do medical research. If you’re serious about science, though, I think of feminism (or, I imagine, Christianity, for example) as a framework rather than a theory—that is, as a way of interpreting the world, not as a set of positive statements. This is in the same way that I earlier wrote that racism is a framework, not a theory. Not all frameworks are equal; my point here is just that, if we’re used to thinking of feminism, or religion, as anti-scientific, it can be useful to consider ways in which these perspectives can help one’s scientific work.
P.S. It would also be fair to say that I talk the talk but don’t walk the walk: a glance at my list of published papers or the stan-dev list reveals that most of my collaborators are male. I don’t know what to say about this—it could be interpreted as evidence that I’m not a real feminist because I’m not committed enough to equality between the sexes in my own professional life, or as evidence of the emptiness of feminism: like a Christian Scientist who talks tough but then goes to the doctor when he gets sick, I’m a feminist who, when given the choice of how to spend my hard-earned research dollars, generally hires men. I don’t think I’m under any obligation to explain myself at all on this one, but to the extent I do, I guess I’d say that there are more men than women working in computational statistics right now, that I hire the people who seem best for the job, and these people often happen to be male—a set of observations, or opinions, that can be interpreted in any number of ways.
P.P.S. As promised, here’s my definition of “schoolyard evolutionary biology”: It’s the idea that, because of evolution, all people are equivalent to all other people, except that all boys are different from all girls. It’s the attitude I remember from the grade school playground, in which any attribute of a person, whether it be how you walked or how you laughed or even how you held your arms when you were asked to look at your fingernails (really) were gender-typed. It’s gender and race essentialism. And when you combine it with what Kahneman and Tversky called “the law of small numbers” (the attitude that any underlying pattern should reproduce in any small sample) has led to endless chasing of noise in data analyses. In short, if you believe this sort of essentialism, you can find it just about anywhere you look.
P.P.P.S. And, just to clarify further, of course there are lots of systematic differences between boys and girls, and between men and women, that are not directly sex-linked. To be a feminist is not to deny these differences; rather, placing these differences within a larger context is part of what feminism is about.