Author: Andrew

Causal inference workshop at NeurIPS 2019 looking for submissions

Nathan Kallus writes: I wanted to share an announcement for a causal inference workshop we are organizing at NeurIPS 2019. I think the readers of your blog would be very interested, and we would be eager to have them interact/attend/submit. And here it is: The NeurIPS 2019 Workshop on “Do the right thing”: machine learning […]

This one goes in the Zombies category, for sure.

Paul Alper writes: I was in my local library and I came across this in Saturday’s WSJ: The Math Behind Successful Relationships Nearly 30 years ago, a mathematician and a psychologist teamed up to explore one of life’s enduring mysteries: What makes some marriages happy and some miserable? The psychologist, John Gottman, wanted to craft […]

Causal Inference and Generalizing from Your Data to the Real World (my talk tomorrow, Sat., 6pm in Berlin)

For the Berlin Bayesians meetup, organized by Eren Elçi: Causal Inference and Generalizing from Your Data to the Real World Andrew Gelman, Department of Statistics and Department of Political Science, Columbia University Learning from data involves three stages of extrapolation: from sample to population, from treatment group to control group, and from measurement to the […]

The garden of forking paths

Bert Gunter points us to this editorial: So, researchers using these data to answer questions about the effects of technology [screen time on adolescents] need to make several decisions. Depending on the complexity of the data set, variables can be statistically analysed in trillions of ways. This makes almost any pattern of results possible. As […]

“This is a case where frequentist methods are simple and mostly work well, and the Bayesian analogs look unpleasant, requiring inference on lots of nuisance parameters that frequentists can bypass.”

Nick Patterson writes: I am a scientist/data analyst, still working, who has been using Bayesian methods since 1972 (getting on for 50 years), I was initially trained at the British code-breaking establishment GCHQ, by intellectual heirs of Alan Turing. I’ve been accused of being a Bayesian fanatic, but in fact a good deal of my […]

Just forget the Type 1 error thing.

John Christie writes: I was reading this paper by Habibnezhad, Lawrence, & Klein (2018) and came across the following footnote: In a research program seeking to apply null-hypothesis testing to achieve one-off decisions with regard to the presence/absence of an effect, a flexible stopping-rule would induce inflation of the Type I error rate. Although our […]

The Economist does Mister P

Elliott Morris points us to this magazine article, “If everyone had voted, Hillary Clinton would probably be president,” which reports: Close observers of America know that the rules of its democracy often favour Republicans. But the party’s biggest advantage may be one that is rarely discussed: turnout is just 60%, low for a rich country. […]

Plaig!

Tom Scocca discusses some plagiarism that was done by a former New York Times editor: There was no ambiguity about it; Abramson clearly and obviously committed textbook plagiarism. Her text lifted whole sentences from other sources word for word, or with light revisions, presenting the same facts laid out in the same order as in […]

“Guarantee” is another word for “assumption”

I always think it’s funny when people go around saying their statistical methods have some sort of “guaranteed” performance. I mean, sure, guarantees are fine—but a guarantee comes from an assumption. If you want to say that your method has a guarantee but my method doesn’t, what you’re really saying is that you’re making an […]

What’s published in the journal isn’t what the researchers actually did.

David Allison points us to these two letters: Alternating Assignment was Incorrectly Labeled as Randomization, by Bridget Hannon, J. Michael Oakes, and David Allison, in the Journal of Alzheimer’s Disease. Change in study randomization allocation needs to be included in statistical analysis: comment on ‘Randomized controlled trial of weight loss versus usual care on telomere […]

Gendered languages and women’s workforce participation rates

Rajesh Venkatachalapathy writes: I recently came across a world bank document claiming that gendered languages reduce women’s labor force participation rates. It is summarized in the following press release: Gendered Languages May Play a Role in Limiting Women’s Opportunities, New Research Finds. This sounds a lot like the piranha problem, if there is any effect […]

A rise in premature publications among politically engaged researchers may be linked to Trump’s election, study says

A couple people pointed me to this news story, “A rise in premature births among Latina women may be linked to Trump’s election, study says,” and the associated JAMA article, which begins: Question Did preterm births increase among Latina women who were pregnant during the 2016 US presidential election? Findings This population-based study used an […]