Hidden Biases: Looking for Bias in Unexpected Places

           Assuming that we, and all of our colleagues, are striving to conduct unbiased science, I often forget to look for bias in obvious places. Sure, there’s the scientist trying to make his or her research appear more important, the prototypical bias we have come to expect, but what about the biases we can’t control?

            This article fromNPR points out that bias can be virtually impossible to avoid. To make a long story short, a researcher found that the results of his experiment had been skewed by natural hormone differences between men and women. Depending on the gender of the person administering the experiment, the results were different.

            Of course this doesn’t mean that one gender gets inherently better results than the other, but it does illustrate an important idea. Not only do we have to be proactive in controlling our own personal biases, but also we have to look for external factors that may affect or otherwise disrupt results.

            I wouldn’t have expected gender of the experimenter to affect results of an experiment. This could be the tip of the iceberg. Does the color of the experimenter’s eyes matter? What about the perfume the experimenter was wearing? Of course some external factors are inevitable, and the point of the article wasn’t to condemn scientists of either gender. The article was a simple plea that scientists report these kinds of external factors.

            To me, this requires two fold effort from scientists: First, we need guidelines on the myriad external factors that can affect our science. Second, we need to be vigilant, reporting every factor that could possibly affect results, no matter how seemingly inconsequential. On the surface, it sounds like a pain. I understand that. It isn’t easy to keep copious and detailed notes. Reporting like this will, almost certainly, be tedious. Still, if there is any chance that a factor could affect reproducibility, it should be recorded.

            More than anything, I think the real call to action is to think about hidden ways that bias could exist in our work. I think our experiments and our credibility will be all that much better for it.