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Archive of posts filed under the Political Science category.

Using statistical prediction (also called “machine learning”) to potentially save lots of resources in criminal justice

John Snow writes: Just came across this paper [Human Decisions and Machine Predictions, by Jon Kleinberg, Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig, and Sendhil Mullainathan] and I’m wondering if you’ve been following the debate/discussion around these criminal justice risk assessment tools. I haven’t read it carefully or fully digested the details. On the surface, their […]

Fake polls. Not new.

Mark Palko points me to this article by Harry Enten about a possibly nonexistent poll that was promoted by an organization or group or website called Delphi Analytica. Enten conjectures that the reported data were not fabricated but they’re not a serious poll either but rather some raw undigested output from a Google poll. This […]

Irish immigrants in the Civil War

I was cc-ed on a series of emails on a topic I know nothing about, maybe because I’m on the political science faculty here, I don’t know. Anyway, there was some statistical content here so I thought I’d share with you. The email is from James McManus: Analysis of the Civil War Immigrant problem McPherson’s […]

The Pandora Principle in statistics — and its malign converse, the ostrich

The Pandora Principle is that once you’ve considered a possible interaction or bias or confounder, you can’t un-think it. The malign converse is when people realize this and then design their studies to avoid putting themselves in a position where they have to consider some potentially important factor. For example, suppose you’re considering some policy […]

“Social Media and Fake News in the 2016 Election”

Gur Huberman asks what I think about this paper, “Social Media and Fake News in the 2016 Election,” by Hunt Allcott and Matthew Gentzkow. I haven’t looked at in detail my quick thought is that they’re a bit too “mechanistic” as the effect of fake news is not just the belief in each individual story […]

The Supreme Court can’t do statistics. And, what’s worse, they don’t know what they don’t know.

Kevin Lewis points us to this article by Ryan Enos, Anthony Fowler, and Christopher Havasy, who write: This article examines the negative effect fallacy, a flawed statistical argument first utilized by the Warren Court in Elkins v. United States. The Court argued that empirical evidence could not determine whether the exclusionary rule prevents future illegal […]

Letter to the Editor of Perspectives on Psychological Science

[relevant cat picture] tl;dr: Himmicane in a teacup. Back in the day, the New Yorker magazine did not have a Letters to the Editors column, and so the great Spy magazine (the Gawker of its time) ran its own feature, Letters to the Editor of the New Yorker, where they posted the letters you otherwise […]

Delegate at Large

Asher Meir points to this delightful garden of forking paths, which begins: • Politicians on the right look more beautiful in Europe, the U.S. and Australia. • As beautiful people earn more, they are more likely to oppose redistribution. • Voters use beauty as a cue for conservatism in low-information elections. • Politicians on the […]

How does a Nobel-prize-winning economist become a victim of bog-standard selection bias?

Someone who wishes to remain anonymous writes in with a story: Linking to a new paper by Jorge Luis García, James J. Heckman, and Anna L. Ziff, an economist Sue Dynarski makes this “joke” on facebook—or maybe it’s not a joke: How does one adjust standard errors to account for the fact that N of […]

“The ‘Will & Grace’ Conjecture That Won’t Die” and other stories from the blogroll

From sociologist Jay Livingston: The “Will & Grace” Conjecture That Won’t Die From sociologist David Weakliem: Why does Trump try to implement the unpopular ideas he’s proposed, and not the popular ideas? History professor who wrote award-winning book about 1970-era crime, is misinformed about the history of 1970s-era crime “West Virginia, which was a lock […]

Slaying Song

I came across this article by Joseph Bernstein, “Why Is A Top Harvard Law Professor Sharing Anti-Trump Conspiracy Theories?”: On April 22, Tribe shared a story from a website called the Palmer Report — a site that has been criticized for spreading hyperbole and false claims — entitled “Report: Trump gave $10 million in Russian […]

Night Hawk

Sam Harper writes: Not sure whether you saw the NYT story a couple of days ago about the declining prospects for democracy in rich countries (based on a recently published paper by Roberto Foa (University of Melbourne) and Yascha Mounk (Harvard). This graph, showing differences in the fraction of individuals reporting that it is “essential” […]

Statisticians and economists agree: We should learn from data by “generating and revising models, hypotheses, and data analyzed in response to surprising findings.” (That’s what Bayesian data analysis is all about.)

Kevin Lewis points us to this article by economist James Heckman and statistician Burton Singer, who write: All analysts approach data with preconceptions. The data never speak for themselves. Sometimes preconceptions are encoded in precise models. Sometimes they are just intuitions that analysts seek to confirm and solidify. A central question is how to revise […]

From Whoops to Sorry: Columbia University history prof relives 1968

I haven’t had much contact with the history department here at Columbia. A bunch of years ago I co-taught a course with Herb Klein and some others, and the material from that class went into my book co-edited with Jeronimo Cortina, A Quantitative Tour of the Social Sciences. More recently, I’ve had some conversations with […]

Maternal death rate problems in North Carolina

Somebody named Jerrod writes: I though you might find this article [“Black moms die in childbirth 3 times as often as white moms. Except in North Carolina,” by Julia Belluz] interesting as it relates to some of your interests in health data and combines it with bad analysis and framing. My beef with the article: […]

Bad Numbers: Media-savvy Ivy League prof publishes textbook with a corrupted dataset

[cat picture] I might not have noticed this one, except that it happened to involve Congressional elections, and this is an area I know something about. The story goes like this. I’m working to finish up Regression and Other Stories, going through the examples. There’s one where we fit a model to predict the 1988 […]

Analyze all your comparisons. That’s better than looking at the max difference and trying to do a multiple comparisons correction.

[cat picture] The following email came in: I’m in a PhD program (poli sci) with a heavy emphasis on methods. One thing that my statistics courses emphasize, but that doesn’t get much attention in my poli sci courses, is the problem of simultaneous inferences. This strikes me as a problem. I am a bit unclear […]

Incentives Matter (Congress and Wall Street edition)

[cat picture] Thomas Ferguson sends along this paper. From the summary: Social scientists have traditionally struggled to identify clear links between political spending and congressional voting, and many journalists have embraced their skepticism. A giant stumbling block has been the challenge of measuring the labyrinthine ways money flows from investors, firms, and industries to particular […]

Time-sharing Experiments for the Social Sciences

Jamie Druckman writes: Time-sharing Experiments for the Social Sciences (TESS) is an NSF-funded initiative. Investigators propose survey experiments to be fielded using a nationally representative Internet platform via NORC’s AmeriSpeak® Panel (see http:/ for more information). In an effort to enable younger scholars to field larger-scale studies than what TESS normally conducts, we are pleased to announce a Special […]

Statistical Challenges of Survey Sampling and Big Data (my remote talk in Bologna this Thurs, 15 June, 4:15pm)

Statistical Challenges of Survey Sampling and Big Data Andrew Gelman, Department of Statistics and Department of Political Science, Columbia University, New York Big Data need Big Model. Big Data are typically convenience samples, not random samples; observational comparisons, not controlled experiments; available data, not measurements designed for a particular study. As a result, it is […]