Manned Mars Mission Miscellanea | Statistical Modeling, Causal Inference, and Social Science

statmodeling.stat.columbia.edu

Survey Statistics: structured MRP to smooth survey weights | Statistical Modeling, Causal Inference, and Social Science

statmodeling.stat.columbia.edu

Walnutpie version 0.0.1 Released | Statistical Modeling, Causal Inference, and Social Science

statmodeling.stat.columbia.edu

Stand-up comedy—like teaching, and book writing—requires “a collaboration with the audience.” | Statistical Modeling, Causal Inference, and Social Science

statmodeling.stat.columbia.edu

“Placebo tests deserve a model, not just a glance.” | Statistical Modeling, Causal Inference, and Social Science

statmodeling.stat.columbia.edu

What gets you is not what you don’t know but what you don’t know you don’t know. | Statistical Modeling, Causal Inference, and Social Science

statmodeling.stat.columbia.edu

Why quantitative understanding of effect sizes matters, even if all you care about is the presence of the effect | Statistical Modeling, Causal Inference, and Social Science

statmodeling.stat.columbia.edu

What do we learn from bestseller regressions? | Statistical Modeling, Causal Inference, and Social Science

statmodeling.stat.columbia.edu

Posterior predictive checking is for non-Bayesians too! | Statistical Modeling, Causal Inference, and Social Science

statmodeling.stat.columbia.edu

“Over-coverage caught by pre-registration: 47 of 56 inside a stated 50% interval” | Statistical Modeling, Causal Inference, and Social Science

statmodeling.stat.columbia.edu

Eleven Kinds of Loneliness: Richard Yates and the tragedy of agency | Statistical Modeling, Causal Inference, and Social Science

statmodeling.stat.columbia.edu

Survey Statistics: equivalent models, equivalent weights (locally) | Statistical Modeling, Causal Inference, and Social Science

statmodeling.stat.columbia.edu

Selection effects can go both ways (for taxi drivers as well as the rest of us) | Statistical Modeling, Causal Inference, and Social Science

statmodeling.stat.columbia.edu

He fit the same statistical models with three different software and got much different estimates. It’s another dimension of the multiverse. | Statistical Modeling, Causal Inference, and Social Scie...

statmodeling.stat.columbia.edu

People sometimes talk about “the Jewish vote,” but what’s relevant is not really the Jewish vote or Jewish public opinion; it’s really about campaign contributions and the news media. Also similar ...

statmodeling.stat.columbia.edu

I don’t see journal review as a gatekeeping process that will keep erroneous articles from being published | Statistical Modeling, Causal Inference, and Social Science

statmodeling.stat.columbia.edu

How generic language shapes the development of social thought | Statistical Modeling, Causal Inference, and Social Science

statmodeling.stat.columbia.edu

Pretty maps of the NY mayoral election vote. (The meta-point here is that people have a (false) intuition that any complicated piece of information can be conveyed in a single plot.) | Statistical M...

statmodeling.stat.columbia.edu

He “washed his hands in a can of tetraethyl lead at a press conference, claiming he was ‘not taking any chance whatever’. He knew this to be a lie, having already succumbed to a bout of lead poisonin...

statmodeling.stat.columbia.edu

Survey Statistics: poststratification without population level information | Statistical Modeling, Causal Inference, and Social Science

statmodeling.stat.columbia.edu

Was this USDA survey really “redundant, costly, politicized, and extraneous”? | Statistical Modeling, Causal Inference, and Social Science

statmodeling.stat.columbia.edu

Rebecca Makkai points out: Fancy three-dimensional sets are easier to construct in books than in movies, but harder to explain | Statistical Modeling, Causal Inference, and Social Science

statmodeling.stat.columbia.edu

Ted-talking University of California professor asks sex trafficker for $3,000,000 because he thinks there’s a “50% chance” he’ll make “important discoveries” in telepathy | Statistical Modeling, Caus...

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It’s all about the Super Pacs: How the New York Times completely misreported campaign contributions in the Maine Senate race | Statistical Modeling, Causal Inference, and Social Science

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More scientists in the Epstein files, including a roboticist and an ESP researcher | Statistical Modeling, Causal Inference, and Social Science

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Herman Chernoff | Statistical Modeling, Causal Inference, and Social Science

statmodeling.stat.columbia.edu

Reviews of our Bayesian Workflow book from Bin Yu, David Spiegelhalter, Brad Efron, Christian Robert, Rohan Alexander, and Mine Doğucu! | Statistical Modeling, Causal Inference, and Social Science

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A ranked-choice election in Maine: Using voting data to understand preferences | Statistical Modeling, Causal Inference, and Social Science

statmodeling.stat.columbia.edu

Survey Statistics: quantifying uncertainty in ranked choice voting polls | Statistical Modeling, Causal Inference, and Social Science

statmodeling.stat.columbia.edu

“Making Statistics Work: Information Theory and Bayesian Inference” | Statistical Modeling, Causal Inference, and Social Science

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