This seminar brings together data visualization and psychology graduate students to investigate new ways of representing and hypothesizing about data while rigorously questioning what conclusions can legitimately be drawn. How should we think about where the data came from and the methods by which they were generated? What sources of potential measurement error should psychologists and data scientists be concerned about? When can we trust that data collected from nonprobability samples generalize to a full population? When are patterns that emerge in exploratory data visualization trustworthy? How can skepticism and questions about data be communicated with the potential audiences for a visual representation of data? How can we better visualize measurement error and multivariate confidence intervals? Class sessions will combine discussion of academic articles with hands-on examination of existing data sets and practical examples. Psychology and data visualization students will be paired to carry out two hands-on projects during the semester, ideally using their own data from class or thesis projects (although having one's own data is not required). From these projects, students will gain experience in communicating with collaborators with quite different backgrounds and expertise. Students are only expected to have background knowledge from their own discipline; data visualization students are not expected to have any psychology expertise, and psychology students are not expected to have any coding or design expertise. **Psychology students should already have taken a graduate-level Research Methods course, or Intro to Applied Psychology and Design, or Intro to Statistics and Research Design. Design and Data Visualization students should already have proficiency in interaction design.** The course counts as an elective and satisfies the seminar requirement for the Psychology PhD programs; it counts toward any of the umbrella courses for Data Visualization students.