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Bayesian Inference under Cluster Sampling with Probability Proportional to Size
Susanna Makela, Yajuan Si, Andrew Gelman
Cluster sampling is common in survey practice, and the corresponding inference has been predominantly design-based. We develop a Bayesian framework for cluster sampling and account…
Large Scale Replication Projects in Contemporary Psychological Research
Blakeley B. McShane, Jennifer L. Tackett, Ulf Bockenholt +1
Replication is complicated in psychological research because studies of a given psychological phenomenon can never be direct or exact replications of one another, and thus effect s…
Abandon Statistical Significance
Blakeley B. McShane, David Gal, Andrew Gelman +2
We discuss problems the null hypothesis significance testing (NHST) paradigm poses for replication and more broadly in the biomedical and social sciences as well as how these probl…
Visualization in Bayesian workflow
Jonah Gabry, Daniel Simpson, Aki Vehtari +2
Bayesian data analysis is about more than just computing a posterior distribution, and Bayesian visualization is about more than trace plots of Markov chains. Practical Bayesian da…
The prior can generally only be understood in the context of the likelihood
Andrew Gelman, Daniel Simpson, Michael Betancourt
A key sticking point of Bayesian analysis is the choice of prior distribution, and there is a vast literature on potential defaults including uniform priors, Jeffreys' priors, refe…
Bayesian hierarchical weighting adjustment and survey inference
Yajuan Si, Rob Trangucci, Jonah Sol Gabry +1
We combine Bayesian prediction and weighted inference as a unified approach to survey inference. The general principles of Bayesian analysis imply that models for survey outcomes s…