The reliability of the gender Implicit Association Test (gIAT) for high-ability careers

Males outnumber females in many high ability careers, for example, in the fields of science, technology, engineering, and mathematics and professors in academic medicine. These differences are often attributed to implicit, subconscious, bias. One objective of this study was to use statistical p value plots to independently test the ability to support the claim of implicit bias made in a meta analysis of gender bias studies. The meta analysis examined correlations between implicit bias measures based on the gender Implicit Association Test, g IAT, and measures of intergroup, female and male, behavior. A second objective was to investigate general intelligence g and vocational, things people, interests as explanatory factors for gender differences in high ability careers. The p value plots constructed using data sets from the meta analysis did not support real associations between the tested variables. These findings reinforce the lack of correlation between g IAT, implicit bias, measures and real world gender behaviors in high ability careers. More demanding careers, attorneys, engineers, scientists, corporate executives, are recruited from people with higher g. One is dealing with gender groups and the group of high g females is smaller than high g males. Regarding vocational interests, females prefer working with people and males prefer working with things. STEM fields are typically things oriented. One is dealing with gender groups and the group of females who prefer working with things is smaller than the group of males. These facts make it predictable that there are more males in high complexity, things careers, STEM, academic medicine positions, than females. Implicit bias gIAT measures have little or no explanatory power for gender differences in high ability careers relative to g and interests in working with things.