Scientists are trained to evaluate and interpret evidence without bias or subjectivity. Thus, growing evidence revealing a gender bias against women-or favoring men-within science, technology, engineering, and mathematics (STEM) settings is provocative and raises questions about the extent to which gender bias may contribute to women's underrepresentation within STEM fields. To the extent that research illustrating gender bias in STEM is viewed as convincing, the culture of science can begin to address the bias. However, are men and women equally receptive to this type of experimental evidence? This question was tested with three randomized, double-blind experiments-two involving samples from the general public (n = 205 and 303, respectively) and one involving a sample of university STEM and non-STEM faculty (n = 205). In all experiments, participants read an actual journal abstract reporting gender bias in a STEM context (or an altered abstract reporting no gender bias in experiment 3) and evaluated the overall quality of the research. Results across experiments showed that men evaluate the gender-bias research less favorably than women, and, of concern, this gender difference was especially prominent among STEM faculty (experiment 2). These results suggest a relative reluctance among men, especially faculty men within STEM, to accept evidence of gender biases in STEM. This finding is problematic because broadening the participation of underrepresented people in STEM, including women, necessarily requires a widespread willingness (particularly by those in the majority) to acknowledge that bias exists before transformation is possible.gender bias | science workforce | diversity | science education | sexism O bjectivity is a fundamental value in the practice of science and is required to optimally assess one's own research findings, others' findings, and the merits of others' abilities and ideas (1). For example, when scientists evaluate data collected on a potentially controversial topic (such as climate change), they strive to set aside their own belief systems and instead focus solely on the strength of the data and conclusions warranted. Similarly, when scientists evaluate a resume for a laboratory-manager position or assess the importance of a conference submission, the gender of the applicant or author should be immaterial. If they are truly objective, scientists should focus only on the relevant criteria of applicant qualifications or research merit.However, despite rigorous training in the objective evaluation of information and resultant values (2), people working and learning within the science, technology, engineering, and mathematics (STEM) community are still prone to the same subtle biases that subvert objectivity and distort accurate perceptions of scientific evidence by the general public (3, 4). We focus here on the robust gender biases documented repeatedly within the psychological literature (5-7). Some within the STEM community have turned to these methods and ideas as an explanation for the c...