Some argue that it is not necessary to consider the statistical significance of the gender coefficient in the context of a university pay equity study because the data used to estimate the model is a census, not a sample. The University of Lethbridge’s pay equity study of 2008 provides a more detailed explanation of this point.27 However, some also argue that statistical significance is relevant because there may be imperfections with the data and/or with the regression specification. It is unlikely that regression is a literal replica of the system used to determine pay at the university. Testing for statistical significance acknowledges this discrepancy.28
27 Mellow, Muriel, et al.. Salary Equity Committee Report to the University of Lethbridge. 2008.
28 It is also important to recognize the critical relationship between statistical significance and sample size. For a given pay gap, as sample size increases, statistical significance will also tend to increase. Thus, the issue of statistical significance is particularly relevant to universities with a relatively small number of faculty.