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Rethinking Standard Statistical Practice
Thu, September 5, 4:00 to 5:30pm, Pennsylvania Convention Center (PCC), 112ASession Submission Type: Created Panel
Session Description
The panel offers exciting and thought-proving perspectives on political science methodology. The papers point the field in new and exciting directions, emphasizing the importance of simplicity in models, the role of projective and descriptive inference, the scrutiny of statistical significance in research findings, and the evolving role of data visualizations. They all share a common theme of critically evaluating standard practices in empirical political science, shedding light on parts of the field we often take for granted.
Sub Unit
Individual Presentations
Applied Machine Learning: Why Simple Models Are Optimal, and Why It Matters - Arthur Spirling, Princeton University; Marco Morucci, Michigan State University
Thinking about the Future: The Methodology of Projective Inference - Henry E. Brady, University of California, Berkeley
The Steroid Era in Social Science? Statistical Significance in 1970s-2010s - Nicolas Idrobo, University of Pennsylvania; Arthur Lupia, University of Michigan, Ann Arbor; Hwayong Shin, Dartmouth College; Rocio Titiunik, Princeton University
The State of Data Visualization in Political Science - Frederick J. Boehmke, University of Iowa; Hyein Ko, University of Illinois Urbana-Champaign; Tianhui Wu, The University of Iowa; Weidong Zhang, University of Texas at El Paso