  Males outnumber females in many high-ability careers in the fields of
science, technology, engineering, and mathematics, STEM, and academic medicine,
to name a few. These differences are often attributed to subconscious bias as
measured by the gender Implicit Association Test, gIAT. We compute p-value
plots for results from two meta-analyses, one examines the predictive power of
gIAT, and the other examines the predictive power of vocational interests, i.e.
personal interests, and behaviors, for explaining gender differences in
high-ability careers. The results are clear, the gender Implicit Association
Test provides little or no information on male versus female differences,
whereas vocational interests are strongly predictive. Researchers of implicit
bias should expand their modeling to include additional relevant covariates. In
short, these meta-analyses provide no support for the gender Implicit
Association Test influencing choice and gender differences of high-ability
careers.
