Pausing to Take a Deeper Look at Assumptions - Dr. Megan Higgs

Event: DSI Spring Symposium 2025 About the Talk: Assumptions are a necessary part of making conclusions and inferences from data. Formal training in Statistics and data science tends to encourage a mathematical and automatic treatment of assumptions, with relatively low expectations for justifying assumptions in the context of the problem or conveying the extent to which conclusions are conditional on assumptions. With so much focus on methods and computing, it is important to also take time to step back and more deeply consider the layers of assumptions that make up the foundation of any data analysis. I believe statisticians and data scientists have a responsibility to better convey the conditional nature of results, and to work with subject matter experts to translate and interrogate assumptions within a particular scientific context. While this is challenging in most problems, it is a necessary step toward better justifying the use of, and trust in, statistical results. The goal of this high-level talk is to increase, or re-highlight, awareness and spur discussion about related challenges and strategies. About the Speaker: Megan Higgs is Ph.D. statistician with over 20 years of experience working with researchers and practitioners across many disciplines. She has worked as a tenured Associate Professor of Statistics, where she developed and taught the year-long sequence in statistical inference for graduate students in other departments, Bayesian Statistics, Statistical Consulting, along with other electives. Since 2016, she has worked in environmental consulting, as director of Statistical Consulting and Research Services at Montana State University, and as an independent statistical consultant through her company Critical Inference LLC. In her current consultant role, she is mainly involved in high level reviews, study design, creative data visualization, and providing focused professional development opportunities. She is also committed to writing about the use of statistical inference in science and collaborating with others who value high level reflections about how we use statistical reasoning, methods, and results in science and decision making. About the Organizer: DSI is a student-led club at the University of Florida. Our primary goal is to help fellow students learn more about data science, artificial intelligence and machine learning. While most of our events are in person (at the University of Florida), we make sure to post our recordings to our Youtube channel!