The Reliability of the gender Implicit Association Test (gIAT) for Explaining Female−Male Differences in High-Ability Careers

Females are outnumbered by males in many high-ability careers in the fields of academic medicine and science, technology, engineering, and mathematics (STEM). These differences are often attributed to implicit bias as measured by the gender Implicit Association Test (gIAT). Statistical p-value plots were used to independently test the ability to reproduce research claims made in relation to female−male differences in these careers. The p-value plots were developed using data sets from two published meta-analyses. One examined predictive power of the gIAT, and the other examined predictive power of vocational interests (personal interests and behaviors) for explaining female−male differences in these careers. The gIAT (implicit bias) p-value plot showed that it is unreliable for predicting female−male differences. Whereas the p-value plot for vocational interests supported these differences. Researchers of implicit bias should expand their modeling to include vocational interests and additional relevant explanatory variables. In short, these meta-analyses and the p-value plots provided no support for the gender Implicit Association Test influencing choice and female−male differences of high-ability careers.