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- Statistical learning and scientific decisions.
(Andrew Gelman and Blakeley B. McShane)
- Bayesian inference under cluster sampling with probability proportional to size.
(Susanna Makela, Yajuan Si, and Andrew Gelman)
- Abandon statistical significance.
(Blakeley B. McShane, David Gal, Andrew Gelman, Christian Robert, and Jennifer L. Tackett)
- Visualization in Bayesian workflow.
(Jonah Gabry, Daniel Simpson, Aki Vehtari, Michael Betancourt, and Andrew Gelman)
- R-squared for Bayesian regression models.
(Andrew Gelman, Ben Goodrich, Jonah Gabry, and Imad Ali)
- The political impact of social penumbras.
(Andrew Gelman and Yotam Margalit)
- Bayesian hierarchical weighting adjustment and survey inference.
(Yajuan Si, Rob Trangucci, Jonah Gabry, and Andrew Gelman)
- Using stacking to average Bayesian predictive distributions.
(Yuling Yao, Aki Vehtari, Daniel Simpson, and Andrew Gelman)
- Pareto smoothed importance sampling.
(Aki Vehtari, Andrew Gelman, and Jonah Gabry)
- The statistical significance filter leads to overconfident expectations of replicability.
(Shravan Vasishth and Andrew Gelman)
- Attitudes toward amalgamating evidence in statistics.
(Andrew Gelman and Keith O'Rourke)
- NO TRUMP!: A statistical exercise in priming.
(Jonathan Falk and Andrew Gelman)
- Disentangling bias and variance in election polls.
(Houshmand Shirani-Mehr, David Rothschild, Sharad Goel, and Andrew Gelman)
- Age-aggregation bias in mortality trends.
(Andrew Gelman and Jonathan Auerbach)
- Causal inference with small samples and incomplete baseline for the Millennium Villages Project.
(Shira Mitchell, Rebecca Ross, Susanna Makela, Elizabeth A. Stuart, Avi Feller, Alan M. Zaslavsky, and Andrew Gelman)
- Expectation propagation as a way of life: A framework for Bayesian inference on partitioned data.
(Andrew Gelman, Aki Vehtari, Pasi Jylanki, Tuomas Sivula, Dustin Tran, Swupnil Sahai, Paul Blomstedt, John Cunningham, David Schiminovich, and Christian Robert)
- The Great Society, Reagan's revolution, and generations of presidential voting.
(Yair Ghitza and Andrew Gelman)
- The problem with p-values is how they're used.
- The garden of forking paths: Why multiple comparisons can be a problem, even when there is no ``fishing expedition'' or ``p-hacking'' and the research
hypothesis was posited ahead of time
(Andrew Gelman and Eric Loken)
- Why ask why? Forward causal inference and reverse causal questions.
(Andrew Gelman and Guido Imbens)
- Visualizing distributions of covariance matrices.
(Tomoki Tokuda, Ben Goodrich, Iven Van Mechelen, Andrew Gelman, and Francis Tuerlinckx)
- Thoughts on new statistical procedures for age-period-cohort analyses.
- One vote, many Mexicos: Income and vote choice in the 1994, 2000, and 2006 presidential elections.
(Jeronimo Cortina, Andrew Gelman, and Narayani Lasala)
- Sampling for Bayesian computation with large datasets.
(Zaiying Huang and Andrew Gelman)
- Fitting multilevel models when predictors and group effects correlate.
(Joseph Bafumi and Andrew Gelman)
- Fully Bayesian computing.
(Jouni Kerman and Andrew Gelman)
- Moderation in the pursuit of moderation is no vice: The clear but limited advantages to being a moderate for Congressional elections.
(Andrew Gelman and Jonathan Katz)