This month, in the CAUSE (Consortium for the Advancement of Undergraduate Statistics Education) / JSDSE (Journal of Statistics and Data Science Education) webinar, we highlight the research article, Think-Aloud Interviews: A Tool for Exploring Student Statistical Reasoning, in our Journal of Statistics and Data Science Education webinar series. In the webinar, the presenters will discuss think-aloud interviews, in which students narrate their reasoning in real time while solving problems. Think-aloud interviews are a valuable but underused tool for statistics education research. In this webinar, the presenters suggest possible use cases for think-alouds and summarize best practices for designing think-aloud interview studies. They hope that their overview of think-alouds encourages more statistics educators and researchers to begin using this method. Article: https://www.tandfonline.com/doi/full/10.1080/26939169.2022.2063209 Presented By: Alex Reinhart (Carnegie Mellon University), Ciaran Evans (Wake Forest University), and Amanda Luby (Swarthmore college. Alex Reinhart is an Assistant Teaching Professor of Statistics and Data Science at Carnegie Mellon University. His work has ranged from spatiotemporal data analysis to large-scale COVID surveys, and he is interested in statistical pedagogy and course design. Ciaran Evans is an Assistant Professor of Statistics at Wake Forest University. He is interested in statistical education and pedagogy, and enjoys collaborating with other educators on teaching and research. Amanda Luby is an Assistant Professor of Statistics at Swarthmore College. She works on statistical methods for understanding complex decision-making, and is also interested in statistics education research and practice. The webinar will take place on June 28th, from 4:00-4:30pm EDT. Registration is required but is free: https://www.causeweb.org/cause/webinar/jsdse/2022-06 We hope that you can join us for an informative discussion. Sincerely, Leigh Johnson (Capital University) Moderator, CAUSE/JSDSE Webinar Series