The butterfly and the piranha: Understanding the generalizability and reproducibility crisis from statistical and political perspectives
Event Date & Time
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MPMC will virtually host Dr. Andrew Gelman Monday (Mar 13) 11:30am-1:00pm. The Zoom link is z.umn.edu/MPMC2023.
Andrew Gelman is a professor of statistics and political science at Columbia University. He is well known for his contributions to Bayesian methods, multilevel modeling, causal inference, and social science research. He is also the director of the Applied Statistics Center at Columbia University, which promotes the use of statistics in various fields and disciplines. He is also a prolific blogger, writing regularly on his popular blog, Statistical Modeling, Causal Inference, and Social Science.
The butterfly and the piranha: Understanding the generalizability and reproducibility crisis from statistical and political perspectives
Andrew Gelman, Department of Statistics and Department of Political Science, Columbia University
Researchers often act as if causal identification + statistical significance = discovery. This belief is appealing but incorrect, and it can lead to an unfortunate feedback loop by which important aspects of measurement get neglected in social science. From a statistical perspective, we can understand these problems using the framework of multilevel regression and poststratification (MRP), a method originally developed for survey research but which also applies to causal inference and generalization in other settings.
Now consider various flawed quantitative social research claiming large effects on voting and political attitudes based on factors such as hormones, football games, shark attacks, and subliminal smiley faces. We argue there is a political dimension to the continued appeal of what might be called the foolish-voter model. We explore the connections between the statistical problems of ungeneralizable or unreplicable claims, and the political positions supported by those claims.