Simply Statistics A statistics blog by Rafa Irizarry, Roger Peng, and Jeff Leek

Data as an antidote to aggressive overconfidence

A recent NY Times op-ed reminded us of the many biases faced by women at work. A [A recent NY Times op-ed reminded us of the many biases faced by women at work. A ](  gave specific recommendations for how to conduct ourselves in meetings_. In general, I found these very insightful, but don’t necessarily agree with the recommendations that women should “Practice Assertive Body Language”.  Instead, we should make an effort to judge ideas by their content and not be impressed by body language. More generally, it is a problem that many of the characteristics that help advance careers contribute nothing to intellectual output. One of these is what I call _aggressive overconfidence.

Here is an example (based on a true story). A data scientist finds a major flaw with the data analysis performed by a prominent data-producing scientist’s lab. Both are part of a large collaborative project. A meeting is held among the project leaders to discuss the disagreement. The data producer is very self-confident in defending his approach. The data scientist, who in not nearly as aggressive, is interrupted so much that she barely gets her point across. The project leaders decide that this seems to be simply a difference of opinion and, for all practical purposes, ignore the data scientist. I imagine this story sounds familiar to many. While in many situations this story ends here, when the results are data driven we can actually fact check opinions that are pronounced as fact. In this example, the data is public and anybody with the right expertise can download the data and corroborate the flaw in the analysis. This is typically quite tedious, but it can be done. Because the key flaws are rather complex, the project leaders, lacking expertise in data analysis, can’t make this determination. But eventually, a chorus of fellow data analysts will be too loud to ignore.

That aggressive overconfidence is generally rewarded in academia is a problem. And if this trait is highly correlated with being male, then a manifestation of this is a worsened gender gap. My experience (including reading internet discussions among scientists on controversial topics) has convinced me that this trait is in fact correlated with gender. But the solution is not to help women become more aggressively overconfident. Instead we should continue to strive to judge work based on content rather than style. I am optimistic that more and more, data, rather than who sounds more sure of themselves, will help us decide who wins a debate.