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Survey Statistics: 5 flavors of calibration

Andrew Gelman · published 3 February 2026

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Survey Statistics: 2 flavors of calibration Read at the source ↗

statmodeling.stat.columbia.edu

https://sites.stat.columbia.edu/gelman/research/unpublished/Bayesian_Workflow_article.pdf

sites.stat.columbia.edu

What is “workflow” and why is it important? Read at the source ↗

statmodeling.stat.columbia.edu

Regularized Prediction and Poststratification (the generalization of Mister P) Read at the source ↗

statmodeling.stat.columbia.edu

Survey Statistics: 3 flavors of survey weights Read at the source ↗

statmodeling.stat.columbia.edu

https://www.cambridge.org/core/journals/political-analysis/article/abs/recalibration-of-predicted-probabilities-using-the-logit-shift-why-does-it-work-and-when-can-it-be-expected-to-work-well/67B3C222EB34BBA376AD730F34038CA4

cambridge.org

https://www.sciencedirect.com/science/article/abs/pii/S0895435615005818

sciencedirect.com

Yes, checking calibration of probability forecasts is part of Bayesian statistics Read at the source ↗

statmodeling.stat.columbia.edu

https://doi.org/10.1017/S0003055423000436

doi.org

https://proceedings.mlr.press/v70/guo17a/guo17a.pdf

proceedings.mlr.press

https://sites.stat.columbia.edu/gelman/book/

sites.stat.columbia.edu

https://mlstory.org/

mlstory.org

When is calibration enough? Read at the source ↗

statmodeling.stat.columbia.edu

https://sites.stat.columbia.edu/gelman/research/published/bayes-vis.pdf

sites.stat.columbia.edu