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Fitting big multilevel regressions in Stan?

Joe Hoover writes: I am a social psychology PhD student, and I have some questions about applying MrP to estimation problems involving very large datasets or many sub-national units. I use MrP to obtain sub-national estimates for low-level geographic units (e.g. counties) derived from large data (e.g. 300k-1 million+). In addition to being large, my […]

“Why we sleep” data manipulation: A smoking gun?

In his post, Matthew Walker’s “Why We Sleep” Is Riddled with Scientific and Factual Errors” (see our discussions here, here, and here), Alexey Guzey added the following stunner: We’ve left “super-important researcher too busy to respond to picky comments” territory and left “well-intentioned but sloppy researcher can’t keep track of citations” territory and entered “research […]

Lorraine Daston (1994): “How Probabilities Came to Be Objective and Subjective”

Sander Greenland points us to a paper by Lorraine Daston from 1994, How Probabilities Came to Be Objective and Subjective. Also relevant are the papers by Glenn Shafer and Michael Cowles and Caroline Davis that we linked to a few months ago.

Whassup with Why We Sleep?

Last month we reported on the book Why We Sleep, which had been dismantled in a long and detailed blog post by Alexey Guzey. A week later I looked again, and Walker had not responded to Guzey in any way. In the meantime, Why We Sleep has also been endorsed by O.G. software entrepreneur Bill […]

The long pursuit

In a comment on our post, Using black-box machine learning predictions as inputs to a Bayesian analysis, Allan Cousins writes: I find this combination of techniques exceedingly useful when I have a lot of data on an indicator that informs me about the outcome of interest but where I have relatively sparse data about the […]

How did our advice about research ethics work out, four years later?

OK, here’s an exam question for you: Someone comes up to you and reminds you that three years ago he asked you for advice, you gave him advice, he followed it, and he’s been doing very well since. You’d like to conclude that your advice helped. Give three reasons why the data conveyed in this […]

How many lobsters would you trade off for a human?

Neil Dullaghan writes: I have a strange set of correlations and am wondering if they are due to some oddity of statistics rather than real associations, but I am quite lost as to an answer. The study in brief: 3 independent surveys asking respondents how many [insert animal] would they trade for 1 human. e.g […]

They’re looking to hire a Bayesian.

Ty Beal writes: The Global Alliance for Improved Nutrition (GAIN) is looking for an analyst with expertise in Bayesian methods. Could you share this post with qualified and interested candidates. The job is based in Washington DC.

Is it true that “Most polls misrepresent the Democratic electorate” and that this “skews the results”?

Someone pointed me to this post in the Monkey Cage, a political science blog that I participate in. The post was about non-representativeness of political polls, and it had one good point and one bad point. Overall I think the claims in the post were overstated. Before getting into the details I’ll copy out the […]

New Democratic Primary Debate Rules

They have all these weird rules now about number of donors, polls, etc. It’s just a mess. We need something simpler. How bout this: Current poll average (in percentage) + (favorability – unfavorable rating)/2 + net worth (in billions) + age/50 + (# major pundits who like you)/5 + (# facebook likes)*(# twitter followers)/(#people who […]

“But when we apply statistical models, do we need to care about whether a model can retrieve the relationship between variables?”

Tongxi Hu writes: Could you please answer a question about the application of statistical models. Let’s take regression models as an example. In the real world, we use statistical models to find out relationships between different variables because we do not know the true relationship. For example, the crop yield, temperature, and precipitation. But when […]

“What if your side wins?”

Bill Harris writes: Thanks for posting my question the other day. Here’s another, somewhat related question. What if “your side” wins? What if, starting today, every analysis is done properly? Null hypothesis significance testing is something you read about only in history of statistics books. When binary decisions are made, they are supported with real […]

External vs. internal validity of causal inference from natural experiments: The example of charter school lottery studies

Alex Hoffman writes: I recently was discussing/arguing about the value of charter schools lottery studies. I suggested that their validity was questionable because of all the data that they ignore. (1) They ignore all charter schools (and their students) that are not so oversubscribed that they need to use lotteries for admission. (2) They ignore […]

The opposite of “black box” is not “white box,” it’s . . .

In disagreement with X, I think the opposite of black box should be clear box, not white box. A black box is called a black box because you can’t see inside of it; really it would be better to call it an opaque box. But in any case the opposite is clear box.

Causal inference, adjusting for 300 pre-treatment predictors

Linda Seebach points to this post by Scott Alexander and writes: A recent paper on increased risk of death from all causes (huge sample size) found none; it controlled for some 300 cofounders. Much previous research, also with large (though much smaller) sample sizes found very large increased risk, but used under 20 confounders. This […]

Bipartisanship!

This is from January 2018, but still: Six in 10 Americans, 61%, say they now have a favorable view of [George W. Bush] . . . nearly double the 33% who gave him a favorable mark when he left the White House in January 2009. . . . His mark is lower than Barack Obama’s […]

Horns! Have we reached a new era in skeptical science journalism? I hope so.

Pointing us to this news article from Aylin Woodward, “No, we’re probably not growing horns from our heads because of our cellphone use — here’s the real science,” Jordan Anaya writes: I haven’t looked into it, but seems like your basic terrible study with an attention grabbing headline. Pretty much just mention cell phone use […]

Elon Musk and George Lucas

Seeing another step in the Musk foolishness cycle, I thought of an analogy to another young-middle-aged-guy who was looked on with awe for a long time after his signature accomplishments were over. George Lucas made American Graffiti in 1973 and Star Wars in 1978, and the mystique from those two films lasted a long time. […]

Response to criticisms of Bayesian statistics

I just happened to reread this article of mine from 2008, and I still like it! So I’m linking to it here. Enjoy.

‘Sumps and rigor

Assumps and rigor, ‘sumps and rigor Go together with utmost vigor. This I tell you brother You can’t have one without the other. Assumps and rigor, ‘sumps and rigor It’s an institute you can’t figger. Ask the local gentry And they will say it’s elementary. Try, try, try to separate them It’s an illusion. Try, […]