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Luis Zambrano writes: Here’s a little “fake stats in the wild” gem that I think you and your followers will find at the least somewhat amusing. Amid strong allegations of fraud in the recent Venezuelan elections, a curious “statistical” fact, that by itself seems to be a strong indicator of blatant fraud, has been thrown around lately on Twitter. On Sunday night, more than 6 hours after ballots we
Andrew and I were talking about coding up some sequential designs for A/B testing in Stan the other week. I volunteered to do the legwork and implement some examples. The literature is very accessible these days—it can be found under the subject heading “multi-armed bandits.” There’s even a Wikipedia page on multi-armed bandits that lays out the basic problem formulation. It didn’t take long to im
Joshua Vogelstein writes: I know you’ve discussed this on your blog in the past, but I don’t know exactly how you’d answer the following query: Suppose you run an analysis and obtain a p-value of 10^-300. What would you actually report? I’m fairly confident that I’m not that confident :) I’m guessing: “p-value \approx 0.” One possibility is to determine the accuracy with this one *could* in theory
Dear Major Academic Publisher, You just sent me, unsolicited, an introductory statistics textbook that is 800 pages and weighs about 5 pounds. It’s the 3rd edition of a book by someone I’ve never heard of. That’s fine—a newcomer can write a good book. The real problem is that the book is crap. It’s just the usual conventional intro stat stuff. The book even has a table of the normal distribution o
As a project for Andrew’s Statistical Communication and Graphics graduate course at Columbia, a few of us (Michael Andreae, Yuanjun Gao, Dongying Song, and I) had the goal of giving RStan’s print and plot functions a makeover. We ended up getting a bit carried away and instead we designed a graphical user interface for interactively exploring virtually any Bayesian model fit using a Markov chain M
Robert Grant has a list. I’ll just give the ones with more than 10,000 Google Scholar cites: Cox (1972) Regression and life tables: 35,512 citations. Dempster, Laird, Rubin (1977) Maximum likelihood from incomplete data via the EM algorithm: 34,988 Bland & Altman (1986) Statistical methods for assessing agreement between two methods of clinical measurement: 27,181 Geman & Geman (1984) Stochastic r
Following up on our discussion the other day, Andrew Ng writes: Looking at the “typical” ML syllabus, I think most classes do a great job teaching the core ideas, but that there’re two recent trends in ML that are usually not yet reflected. First, unlike 10 years ago, a lot of our students are now taking ML not to do ML research, but to apply it in other research areas or in industry. I’d like to
The New York Times has a feature in its Tuesday science section, Take a Number, to which I occasionally contribute (see here and here). Today’s column, by Nicholas Balakar, is in error. The column begins: When medical researchers report their findings, they need to know whether their result is a real effect of what they are testing, or just a random occurrence. To figure this out, they most common
From Laura Wattenberg’s always-thoughtful Baby Name blog: Quick question: which of these names is not like the others? Molly, Elsie, Sadie, Lucy At first glance, the four make a natural style group. They’re all cozy, old-fashioned girls’ names that have returned to popularity in the 21st Century. But Molly, Sadie and Millie are traditional nicknames. Lucy is not. Lucy is the English form of Lucia,
The American Statistical Association organizes a program in which young researchers can submit writing samples and get comments from statisticians who are more experienced writers. I agreed to participate in this program, as long as the authors were willing to have their articles and my comments posted here. I’m going to start with my general advice after reading and commenting on the two articles
These are all important methods and concepts related to statistics that are not as well known as they should be. I hope that by giving them names, we will make the ideas more accessible to people. (The date above is when the first version of this list was posted; I continue to update it regularly.) Mister P: Multilevel regression and poststratification. The Secret Weapon: Fitting a statistical mod
An incredibly useful method is to fit a statistical model repeatedly on several different datasets and then display all these estimates together. For example, running a regression on data on each of 50 states (see here as discussed here), or running a regression on data for several years and plotting the estimated coefficients over time. Here’s another example: The idea is to fit a separate model
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