Dr. Lance Waller from Emory University presents a lecture titled "Data Issues: Multiple Testing, Bias, Confounding, & Missing Data."

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Lecture Abstract

Once data are scraped, wrangled, linked, merged, and analyzed, what information do they reveal and can we trust the resulting conclusions? In this presentation, we define and review data issues relating to the analysis and interpretation of observational data from the field of epidemiology and consider implications for data science, especially regarding the goal of moving from big data to knowledge. Specifically, we explore concepts of bias, confounding, effect modification, and missing/mismeasured data as applied to data science. We provide an analytic context based on sampling concepts and explore relevant literature and tools from epidemiology, biostatistics, computer science, and data science. As with many issues in data science, the full applicability of the concepts is very much a work in progress and present multiple opportunities for future development.

About the Speaker

Lance A. Waller, Ph.D. is Rollins Professor and Chair of the Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University. He is a member of the National Academy of Science Committee on Applied and Theoretical Statistics. His research involves the development of statistical methods for geographic data including applications in environmental justice, epidemiology, disease surveillance, spatial cluster detection, conservation biology, and disease ecology. His research appears in biostatistical, statistical, environmental health, and ecology journals and in the textbook Applied Spatial Statistics for Public Health Data (2004, Wiley).

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