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• The ladder of abstraction in statistical graphics (Andrew Gelman)

• BISG: When inferring race or ethnicity, does it matter that people often live near their relatives? (Philip Greengard and Andrew Gelman)

• Causal quartets: Different ways to attain the same average treatment effect. (Andrew Gelman, Jessica Hullman, and Lauren Kennedy)

• I love this paper but it’s barely been noticed. (Andrew Gelman)

• Simulation-based calibration checking for Bayesian computation: The choice of test quantities shapes sensitivity. (Martin Modrák, Angie H. Moon, Shinyoung Kim, Paul Bürkner, Niko Huurre, Kateřina Faltejsková, Andrew Gelman, and Aki Vehtari)

• How democracies polarize: A multilevel perspective. (Sihao Huang, Alexander Siegenfeld, and Andrew Gelman)

• Nested R-hat: Assessing the convergence of Markov chain Monte Carlo when running many short chains. (Charles C. Margossian, Matthew D. Hoffman, Pavel Sountsov, Lionel Riou-Durand, Aki Vehtari, and Andrew Gelman)

• Using leave-one-out cross-validation (LOO) in a multilevel regression and poststratification (MRP) workflow: A cautionary tale. (Swen Kuh, Lauren Kennedy, Qixuan Chen, and Andrew Gelman)

• Delivering data differently. (S. Gwynn Sturdevant, A. Jonathan R. Godfrey, and Andrew Gelman)

• Making the most of imprecise measurements: Changing patterns of arsenic concentrations in shallow wells of Bangladesh from laboratory and field data. (Yuling Yao, Rajib Mozumder, Benjamin Bostick, Brian Mailloux, Charles F. Harvey, Andrew Gelman, and Alexander van Geen)

• The piranha problem: Large effects swimming in a small pond. (Christopher Tosh, Philip Greengard, Ben Goodrich, Andrew Gelman, and Daniel Hsu)

• Bayesian workflow. (Andrew Gelman, Aki Vehtari, Daniel Simpson, Charles C. Margossian, Bob Carpenter, Yuling Yao, Paul-Christian Bürkner, Lauren Kennedy, Jonah Gabry, Martin Modrák)

• He, she, they: Using sex and gender in survey adjustment. (Lauren Kennedy, Katharine Khanna, Daniel Simpson, Andrew Gelman, Yajun Jia, and Julien Teitler)

• Adaptive path sampling in metastable posterior distributions. (Yuling Yao, Collin Cademartori, Aki Vehtari, and Andrew Gelman)

• A generational voting model for forecasting the 2020 American presidential election. (Jonathan Auerbach, Yair Ghitza, and Andrew Gelman)

• The future is data literate: A guide to developing a statistics course for students in non-quantitative fields. (Alexis Lerner and Andrew Gelman)

• Validating Bayesian inference algorithms with simulation-based calibration. (Sean Talts, Michael Betancourt, Daniel Simpson, Aki Vehtari, and Andrew Gelman)

• Pareto smoothed importance sampling. (Aki Vehtari, Daniel Simpson, Andrew Gelman, Yuling Yao, and Jonah Gabry)

• Many perspectives on Deborah Mayo’s ``Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars.'' (Andrew Gelman, Brian Haig, Christian Hennig, Art Owen, Robert Cousins, Stan Young, Christian Robert, Corey Yanofsky, E. J. Wagenmakers, Ron Kenett, and Daniel Lakeland)

• Abandoning statistical significance is both sensible and practical. (Valentin Amrhein, Andrew Gelman, Sander Greenland, and Blakeley B. McShane)

• Using multilevel regression and poststratification to estimate dynamic public opinion. (Andrew Gelman, Jeffrey Lax, Justin Phillips, Jonah Gabry, and Robert Trangucci)

• Voting patterns in 2016: Exploration using multilevel regression and poststratification (MRP) on pre-election polls. (Robert Trangucci, Imad Ali, Andrew Gelman, and Doug Rivers)

• Statistical learning and scientific decisions. (Andrew Gelman and Blakeley B. McShane)

• Attitudes toward amalgamating evidence in statistics. (Andrew Gelman and Keith O'Rourke)

• NO TRUMP!: A statistical exercise in priming. (Jonathan Falk 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)

• Design of the Millennium Villages Project sampling plan: A simulation study for a multi-module survey. (Shira Mitchell, Rebecca Ross, Susanna Makela, Elizabeth A. Stuart, Avi Feller, Alan M. Zaslavsky, and Andrew Gelman)

• The problem with p-values is how they're used. (Andrew Gelman)

• Review of {\em New Explorations into International Relations: Democracy, Foreign Investment, Terrorism, and Conflict}, by Seung-Whan Choi. (Andrew Gelman)

• 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. (Andrew Gelman)

• Who wants school vouchers? (Yu-Sung Su and Andrew Gelman)

• 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)

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