As it became clear that Donald Trump had a real base of political support, even as analysts consistently underestimated his electoral prospects, they grew increasingly fascinated with the question of who was supporting him (and why). However, researchers also tend to hold strong negative opinions about Trump. Consequently, they have approached this research with uncharitable priors about the kind of person who would support him and what they would be motivated by. Research design and data analysis often seem to be (...) oriented towards reinforcing those assumptions. This essay highlights the epistemological consequences of these tendencies through a series of case studies featuring prominent and influential works that purport to explain the role of race and racism in the 2016 U.S. presidential election. It demonstrates that quality control systems, which should catch major errors, seem to be failing in systematic ways as a result of shared priors and commitments between authors, reviewers and editors – which are also held in common with the journalists and scholars citing and amplifying this work – leading to misinformation cascades. Of course, motivated reasoning, confirmation bias, prejudicial study design, and failure to address confounds are not limited to questions about Trump – however they seem to be particularly pronounced in this case due to the relative homogeneity and intensity of scholars’ views about this topic as compared to other social phenomena. “Trump studies,” therefore, provides fertile ground for exploring how social research can go awry – and the consequences of these failures -- particularly with respect to work on contentious and politically-charged topics. (shrink)