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Survey Statistics: 2 flavors of calibration

Andrew Gelman · published 3 June 2025

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https://sites.stat.columbia.edu/gelman/research/published/gelman_breiman.pdf

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https://doi.org/10.1017/S0003055423000436

doi.org

https://en.wikipedia.org/wiki/Law_of_total_expectation

en.wikipedia.org

https://onlinelibrary.wiley.com/doi/book/10.1002/9780470580066

onlinelibrary.wiley.com

https://www.cambridge.org/core/journals/political-analysis/article/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.cambridge.org/core/journals/political-analysis/article/abs/voter-registration-databases-and-mrp-toward-the-use-of-largescale-databases-in-public-opinion-research/C6C428EB05DC7132678215896F38B6B7

cambridge.org

https://sites.stat.columbia.edu/gelman/research/published/misterp.pdf

sites.stat.columbia.edu

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

sciencedirect.com

https://mlstory.org/

mlstory.org

https://statmodeling.stat.columbia.edu/author/jessica/

statmodeling.stat.columbia.edu

When is calibration enough? Read at the source ↗

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