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M.W. Gray 131the kit and its refinements. As we know, discrimination cannot be proved bystatistics alone, but extreme gender-disproportionate hiring, promotion andpay are extremely unlikely to occur by chance.Professor Scott was not happy with the “remedies” that institutions wereusing to “fix” the problem. Once women’s salaries were fitted to a male model,administrators appeared to love looking at a regression line and circling theobservations below the line. “Oh look, if we add $2000 to the salaries of a fewwomen and $1000 to the salaries of a few more, we will fix the problem,” theyexclaimed. Such an implementation relied on a common misunderstanding ofvariation. Sure, some women will be below the line and, of course, some women(although probably not many) may be above the male line. But what theregression models generally show is that the overall effect is that, on average,women are paid less than similarly qualified men. What is even worse, alltoo frequently administrators then engage in a process of showing that those“circled” women really “deserve” to be underpaid on some subjective basis,so that the discrimination continues.Not only does the remedy described confuse the individual observationwith the average, but it may be rewarding exactly the wrong women. Nomatter how many variables we throw in, we are unlikely entirely to account forlegitimate, objective variation. Some women are “better” than other women,or other men, with ostensibly similar qualifications, no matter what metric theinstitution uses: research, teaching, or service (Gray, 1988). But systematicallytheir salaries and those of all other women trail those of similarly qualifiedmen.What is appropriate for a statistically identified problem is a statisticallybased remedy. Thus Betty and I wrote an article (Gray and Scott, 1980),explaining that if the average difference between men’s and women’s salariesas shown by a regression model is $2000, then the salary of each woman shouldbe increased by that amount. Sorry to say, this is not an idea that has beenwidely accepted. Women faculty are still paid less on the whole, there arestill occasional regression-based studies, there are spot remedies, and oftenthe very best women faculty continue to be underpaid.The great interest in statistical evaluation of salary inequities, particularlyin the complex setting of academia, led to Gray (1993), which expanded onthe methods we used. Of course, statistical analysis may fail to show evidenceof inequity, but even when it does, a remedy may not be forthcoming becausethose responsible may be disinclined to institute the necessary changes. If theemployers refuse to remedy inequities, those aggrieved may resort to litigation,which is a long, expensive process, often not successful and almost alwayspainful.Experience as an expert witness teaches that however convincing the statisticalevidence might be, absent anecdotal evidence of discrimination anda sympathetic plaintiff, success at trial is unlikely. Moreover, one should notforget the frequent inability of courts to grasp the significance of statisticalevidence. Juries are often more willing to make the effort to understand and