Background: Few studies have investigated how engineering education admission policies contribute to the underrepresentation of specific groups. Transforming these policies may significantly affect who becomes an engineer. This article reports the outcome of using research results to inform change in admission policy at a Midwestern public university. Purpose: There were three research questions: Is there statistically significant evidence of admission decision gender bias for engineering applicants? Do affective and cognitive factors predictive of engineering student success differ between men and women? Can a difference in the resulting admitted class be confirmed when such factors inform admission policy? Design/Method: Admissions records were examined for differences in cognitive metrics between men and women. Student records were analyzed before and after the policy change. Neural network modeling of student records predicted the cognitive and affective measures most important for success in retention and graduation. Results: Statistical analysis indicated a gender bias in the admission process results, which was traced back to the policy. Success factor modeling suggested a different set of criteria could mitigate this bias. After admission criteria were changed, statistical analysis confirmed the gender bias against women was mitigated. Conclusions: The application of research and the change process described shows the important role of research in motivating and informing policy change. This work highlights the contribution of institutional bias in admission policy to the underrepresentation of groups in engineering education, especially if admission is limited to a minimum standardized math test score.