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• Efficient scenario analysis in real-time Bayesian election forecasting via sequential meta-posterior sampling. (Geonhee Han, Andrew Gelman, and Aki Vehtari)

• Adaptive sequential Monte Carlo for structured cross validation in Bayesian hierarchical models. (Geonhee Han and Andrew Gelman)

• Continuous adaptive path sampling for efficient multimodal sampling and marginalization. (Yuling Yao, Collin Cademartori, Aki Vehtari, and Andrew Gelman)

• Conformal prediction and human decision making. (Jessica Hullman, Yifan Wu, Dawei Xie, Ziyang Guo, and Andrew Gelman)

• When fiction is presented as real: The case of the burly boatmen. (Andrew Gelman)

• MRPW: Regression, poststratification, and small-area estimation with sampling weights. (Andrew Gelman, Yajuan Si, and Brady T. West)

• Forking paths and workflow in statistical practice and communication. (Andrew Gelman)

• Model validation for aggregate inferences in out-of-sample prediction. (Lauren Kennedy, Aki Vehtari, and Andrew Gelman)

• Approximate posterior recalibration. (Tiffany Cai, Philip Greengard, Ben Goodrich, and Andrew Gelman)

• Bayesian workflow for time-varying transmission in stratified compartmental infectious disease transmission models. (Judith A. Bouman, Anthony Hauser, Simon L. Grimm, Martin Wohlfender, Samir Bhatt, Elizaveta Semenova, Andrew Gelman, Christian L. Althaus, and Julien Riou)

• The ladder of abstraction in statistical graphics. (Andrew Gelman and Kaiser Fung)

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

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

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

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

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

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

• 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)

• 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)

• Thoughts on new statistical procedures for age-period-cohort analyses. (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)