Monday, Aug 7: 10:55 AM - 11:15 AM
Invited Paper Session
Metro Toronto Convention Centre
Students working on interdisciplinary projects usually have enough knowledge to correctly apply statistical methodology. However, often they have not had much practice thinking carefully about the data and potential implications to inference due to population source, data collection methodology, and factors resulting from structural, social, and environmental determinants. When data is available, students commonly make the mistake of immediately embarking on conducting statistical tests or fitting statistical models. To translate statistical results into interdisciplinary conclusions, statisticians must take a step back to understand, respond to, and describe the broader context of the research study and data available. I will share our "3 Cs framework for teaching how to approach, implement, and present on research questions utilizing datasets from interdisciplinary collaborators" (Moore, Mehta) and highlight, as case studies, projects that I've utilized in my statistics consulting classes to engage students in thinking critically, including considerations of structural racism and health inequity, and how to contribute to the team based on their backgrounds and perspectives.