Outliers are another important consideration since OLS regression can be very sensitive to extreme values in the dataset. There are several methods to determine the impact of outliers on results which will not be discussed in detail here. However, more generally there is some debate on how outliers should be treated in these studies. Some may argue that at many universities, “super-star” earners tend to be primarily men. Since this trend is a manifestation of the systematic pay difference of men and women, these observations should remain in the data.²⁶ However, one may argue that since OLS results reflect average impacts, extreme values should not be included because they are not representative of average salaries. Additionally, there are other regression methods that are less sensitive to outliers, such as robust regression or median regression, that may mitigate the impact of outliers on regression results.  

²⁶ Interview with McMaster faculty member, 2018.