Here are videos of some of my presentations and podcast interviews:

• Four Decades of Bad Ideas, Blind Alleys, Misunderstandings, and Errors (Columbia University, 30 May 2026)

• Beyond Cargo Cult Science: Fixing Ritualistic Statistical Practice (Interview for Faculti.net, 3 Feb 2026)

• Russian Roulette: The Need for Stochastic Potential Outcomes when Utilities Depend on Counterfactuals (Behind-the-Scenes Seminar, 24 Feb 2025)

• A Selective History of Political Polling and Election Forecasting (Columbia University, 30 Oct 2025)

• What's Going On In There? Bayesian Tools for Understanding a Fitted Model (New York Data Science and AI Conference, 26 Aug 2025)

• Bayesian Workflow (Isaac Newton Institute, 26 Jun 2025)

• Generalizing for Sampling and Causal Inference (Joint Program in Survey Methodology Distinguished Lecture, 28 Apr 2025)

• Ethical AI Principles for Statistical Practitioners: Experiences and Challenges (American Statistical Association webinar, 6 Feb 2025)

• Bayesian Workflow (Conversation with Charles Margossian for the MetrumRG podcast, 22 Oct 2024)

• Tough Choices in Election Forecasting: All the Things That Can Go Wrong (Washington Statistical Society, 11 Oct 2024)

• The Political Content of Unreplicable Research (Stanford Classical Liberalism Seminar, 3 Oct 2024)

• Fooling Yourself Less: The Art of Statistical Thinking in AI (Conversation for the High Signal podcast, 18 Sep 2024)

• Effective Number of Parameters in a Statistical Model (Osaka University, 10 Sep 2024)

• Holes in Bayesian Statistics (International Society for Bayesian Analysis meeting, 3 Jul 2024)

• Beyond the Black Box: Toward a New Paradigm of Statistics in Science (Alan Turing Institute, 20 Jun 2024)

• It's About Time (New York R Conference, 16 May 2024)

• Active Statistics, Two Truths and a Lie (Learning Bayesian Statistics podcast, 2 Apr 2024)

• Learning from Mistakes (American Statistical Association webinar, 30 Jan 2024)

• Better than Difference-in-Differences (Online Causal Inference Seminar, 19 Sep 2023)

• Educating the Future Statisticians: Bringing Our Teaching Up-to-date (University College London, 13 June 2023)

• Social Science: From Prediction to Modeling to Understanding (Collège de France, 29 June 2022)

• When You Do Applied Statistics, You're Acting Like a Scientist. Why Does This Matter? (New York R Conference, 9 June 2022)

• Bayesian Methods in Causal Inference and Decision Making (Criteo Labs, 7 Mar 2022)

• Discussion of Ivermectin Trials for Covid-19 (Conversation with Greg Kellogg, 4 Mar 2022)

• Modeling and Poststratification for Descriptive and Causal Inference (CUNY Graduate School of Public Health and Health Policy, 2 Feb 2022)

• Wrong Again! 30+ Years of Statistical Mistakes (New York R Conference, 10 June 2021)

• Statistics and American Politics (Great Battlefield podcast, 31 May 2021)

• Information, Incentives, and Goals in Election Forecasts (DIMACS Workshop on Forecasting, 1 Apr 2021)

• The Most Important Statistical Ideas in the Past 50 Years (Boston chapter of the American Statistical Association, 17 Mar 2021)

• It Doesn't Work, But The Alternative is Even Worse: Living With Approximate Computation ( Neural Information Processing Systems conference, 12 Dec 2020)

• Election Forecasting: How We Succeeded Brilliantly, Failed Miserably, or Landed Somewhere in Between (Dana-Farber Cancer Institute, 10 Nov 2020)

• Modeling the US Presidential Elections (Learning Bayesian Statistics podcast, 1 Nov 2020)

• Reflections on Breiman's Two Cultures of Statistical Modeling, and An Updated Dynamic Bayesian Forecasting Model for the 2020 Election (UCLA, 13 Oct 2020)

• Election Forecasts: The Math, the Goals, and the Incentives (Cornell Center for Applied Math, 18 Sep 2020)

• Truly Open Science: From Design and Data Collection to Analysis and Decision Making (New York R Conference, 13 Aug 2020)

• 100 Stories of Causal Inference (Online Causal Inference Seminar, 4 Aug 2020)

• Regression and Other Stories (Learning Bayesian Statistics podcast, 30 Jul 2020)

• Statistics is the Least Important Part of Data Science (Artists of Data Science podcast, 23 Jul 2020)

• Data, Modeling, and Uncertainty Amidst the Forking Paths (The Filter podcast, 21 Jul 2020)

• Embracing Variation and Accepting Uncertainty (Australian Research Council Training Centre in Data Analytics for Resources & Environments, 20 Jul 2020)

• Scientific Reasoning for Practical Data Science (Philosophy of Data Science podcast, 24 Jun 2020)

• Embracing Variation and Accepting Uncertainty: Implications for Science and Metascience (Metascience Symposium, 6 Sep 2019)

• Solve All Your Statistics Problems Using P-Values (New York R Conference, 9 May 2019)

• Significanct Science (Neoliberal Podcast, 18 Jan 2019)

• Bayesian Workflow (University of Vienna, 9 Nov 2018)

• Election Forecasting and Polling (Datacamp, 8 Oct 2018)

• Evidence-Based Practice Is a Two-Way Street (Society for Research on Educational Effectiveness conference, 28 Feb 2018)

• Bayes, Statistics, and Reproducibility (Rutgers University, 29 Jan 2018)

• Data Science Workflow (PyData, New York, 2017)

• The Statistical Crisis in Science and How to Move Forward (Columbia University, 13 Nov 2017)

• Theoretical Statistics is the Theory of Applied Statistics: How to Think About What We Do (New York R Conference, 21 Apr 2017)

• Social Science, Small Samples, and the Garden of the Forking Paths (EconTalk podcast, 20 Mar 2017)

• Introduction to Bayesian Data Analysis and Stan (Generable, 25 Oct 2016)

• Crimes Against Data (ERCC Research Methods Festival, 5 July 2016)

• The Political Impact of Social Penumbras (New York R Conference, 8 Apr 2016)

• Taking Bayesian Inference Seriously (Harvard University, 2016)

• Why Do Americans Vote the Way They Do? (Rationally Speaking podcast, 11 Nov 2015)

• But When You Call Me Bayesian, I Know I'm Not the Only One (New York R Conference, 24 Apr 2015)

• Living with Uncertainty but Still Learning (Simons Foundation, New York, 10 Sep 2014)

• Weakly Informative Priors (AIStats conference, 23 Apr 2014)

• Active Learning in Statistics Classes (Quantitiative Methods teaching project, 24 Oct 2013)

• Creating Structured and Flexible Models: Some Open Problems (New York City R meetup, 7 Oct 2010)

• Why We (Usually) Don't Have to Worry About Multiple Comparisons (Columbia University workshop on estimating effects and correlations in neuroimaging data, 15 July 2009)

• Red States, Blue States, and Political Polarization (10th anniversary celebration of the Center for Statistics and Social Sciences, University of Washington, 5 June 2009)

• Rich State, Poor State, Red State, Blue State: Why Americans Vote the Way They Do (Authors@Google series, Mountain View, California, 20 March 2008)

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