There has been a lot of blogosphere activity in the past week around John Tierney's piece in the New York Times, where he questions the idea that "female scientists [face] discrimination and various forms of unconscious bias." He refers to a recent paper by Ceci and Williams, which argues that women with the same resources as men are just as likely to get their papers, grants, and job applications accepted. (The key thing is of course the 'same resources' bit).
Then Alison Gopnik brilliantly rebutted Tierney's argumentation in Slate (see also the Feminist Philosophers on Gopnik's piece here and here). Her piece is not only brilliant because it argues for the position that I am sympathetic with (^_^) (i.e. that implicit biases do severely affect the position of women in science and academia), but also because it manages to explain very clearly some of the basics of scientific methodology as currently endorsed and practiced (of course, we can still discuss the merits of this methodology). What she says connects nicely to some of the posts I've been writing where the matter of scientific methodology comes up, in particular here and in the discussion ensuing here. So let me quote a few key passages.
In order to understand why, we need to revisit some basic facts about the scientific method. The best scientific way to discover if one factor influences another is to do a controlled experiment. For example, you can give people two identical résumés to evaluate, one with a woman's name and one with a man's name. If people rank the one with man's name higher than the identical one with a woman's name, you know that they are discriminating on the basis of sex, and nothing else, since you've experimentally controlled all the other factors. These experiments, and others like them, have been done. They are described in the PNAS article and the results are clear. Even in fields that are traditionally considered friendly to women, such as psychology and sociology, a woman's name leads to a lower ranking. [...]
But there is another, trickier question to ask. How does this kind of discrimination actually influence the success of women scientists? That's much harder to determine, because you can't experimentally control all the other factors that shape a person's career. Instead of doing an experiment, the best you can do is to analyze the correlations between different factors, and that's much more problematic. [...] [T]he difficulty, as every first year statistics course will tell you, is that correlation does not imply causation.
Ceci and Williams did not show, or claim to show, that there was no discrimination or unconscious bias against women scientists. Instead, they tried to untangle the complicated causal factors that influence success. They found that when you factor in women's circumstances—for example, what kinds of teaching loads they have, whether they are at research universities, whether they have young children, and so on—then the correlation between sex and success goes away. Overall, female scientists have fewer resources than male scientists, just as poor people have less access to health care. But if you compare male and female scientists with identical resources you find that the women are just as likely to be successful.
(Do read the whole article! I am tempted to quote it all here, but let me keep it short.)
Methodologically, the main point is that controlled experiments are really at the core of current scientific practices, but are not always possible to implement, i.e. when the causal connections and different factors are much too entangled. In such cases, one has to resort to different methods, e.g. the search for correlations, but these require a lot more care and prudence (because correlation is by no means a sure sign of causation).
As for women in science, the main point is clearly that, when given the same resources, men and women are equally likely to succeed in science -- which in itself is already evidence for the absence of so-called innate 'sex differences'! But women are typically not given the same resources as men, for all kinds of reasons, and in particular (by not exclusively) due to the effects of implicit biases. So what we need to be looking into is why the development of women's careers is hindered by all kinds of resource-diminishing factors along the way, and this seems consistent with the observations I offered last week on the matter of attending conferences and professional relocation.
Gopnik's conclusion is:
This tension between experimental studies and correlational ones is not uncommon in science, but the rule is that experiments win. In this case, the experiments [the resumes studies] prove that there is bias against women—and the correlational data suggest that this bias interacts with other factors in complicated ways to influence their success.
QED. In fact, I would go further and add that the women who have somehow managed to put themselves in a situation of equality of resources with men must be so highly motivated and thick-skinned (having fought quite a bit of adversity along the way), that they could even be more successful than their male peers, and this would still not prove that there is no bias against women in science.