Author: Andrew

Problems with surrogate markers

Paul Alper points us to this article in Health News Review—I can’t figure out who wrote it—warning of problems with the use of surrogate outcomes for policy evaluation: “New drug improves bone density by 40%.” At first glance, this sounds like great news. But there’s a problem: We have no idea if this means the […]

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Francis Spufford writes just like our very own Dan Simpson and he also knows about the Australia paradox!

Golden Hill was just great—a book that truly lived up to its reviews—so when I was in the bookstore the other day and saw this book of Spufford’s collected nonfiction, I snapped it up. I was reading the chapter on Red Plenty (a book that I’ve not yet read), I was struck by how similar […]

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He has a math/science background and wants to transition to social science. Should he get a statistics degree and do social science from there, or should he get a graduate degree in social science or policy?

Someone who graduated from college a couple years ago writes: My educational background is almost entirely science and math. However, since graduating and thinking about what I do, I’ve realized that I’ve always found demographics, geography, urban planning more interesting – and I’d like to pursue research in social science. I’m currently applying to MS […]

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Yes, but did it work? Evaluating variational inference

That’s the title of a recent article by Yuling Yao, Aki Vehtari, Daniel Simpson, and myself, which presents some diagnostics for variational approximations to posterior inference: We were motivated to write this paper by the success/failure of ADVI, the automatic variational inference algorithm devised by Alp Kucukelbir et al. The success was that ADVI solved […]

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The “Psychological Science Accelerator”: it’s probably a good idea but I’m still skeptical

Asher Meir points us to this post by Christie Aschwanden entitled, “Can Teamwork Solve One Of Psychology’s Biggest Problems?”, which begins: Psychologist Christopher Chartier admits to a case of “physics envy.” That field boasts numerous projects on which international research teams come together to tackle big questions. Just think of CERN’s Large Hadron Collider or […]

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Multilevel modeling in Stan improves goodness of fit — literally.

John McDonnell sends along this post he wrote with Patrick Foley on how they used item-response models in Stan to get better clothing fit for their customers: There’s so much about traditional retail that has been difficult to replicate online. In some senses, perfect fit may be the final frontier for eCommerce. Since at Stitch […]

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Regression to the mean continues to confuse people and lead to errors in published research

David Allison sends along this paper by Tanya Halliday, Diana Thomas, Cynthia Siu, and himself, “Failing to account for regression to the mean results in unjustified conclusions.” It’s a letter to the editor in the Journal of Women & Aging, responding to the article, “Striving for a healthy weight in an older lesbian population,” by […]

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Trying to make some sense of it all, but I can see it makes no sense at all . . . stuck in the middle with you

“Mediation analysis” is this thing where you have a treatment and an outcome and you’re trying to model how the treatment works: how much does it directly affect the outcome, and how much is the effect “mediated” through intermediate variables. Fabrizia Mealli was discussing this with me the other day, and she pointed out that […]

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Ways of knowing in computer science and statistics

Brad Groff writes: Thought you might find this post by Ferenc Huszar interesting. Commentary on how we create knowledge in machine learning research and how we resolve benchmark results with (belated) theory. Key passage: You can think of “making a a deep learning method work on a dataset” as a statistical test. I would argue […]

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Answering the question, What predictors are more important?, going beyond p-value thresholding and ranking

Daniel Kapitan writes: We are in the process of writing a paper on the outcome of cataract surgery. A (very rough!) draft can be found here, to provide you with some context:  https://www.overleaf.com/read/wvnwzjmrffmw. Using standard classification methods (Python sklearn, with synthetic oversampling to address the class imbalance), we are able to predict a poor outcome […]

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When does the quest for beauty lead science astray?

Under the heading, “please blog about this,” Shravan Vasishth writes: This book by a theoretical physicist [Sabine Hossenfelder] is awesome. The book trailer is here. Some quotes from her blog: “theorists in the foundations of physics have been spectacularly unsuccessful with their predictions for more than 30 years now.” “Everyone is happily producing papers in […]

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Data science teaching position in London

Seth Flaxman sends this along: The Department of Mathematics at Imperial College London wishes to appoint a Senior Strategic Teaching Fellow in Data Science, to be in post by September 2018 or as soon as possible thereafter. The role will involve developing and delivering a suite of new data science modules, initially for the MSc […]

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What is the role of qualitative methods in addressing issues of replicability, reproducibility, and rigor?

Kara Weisman writes: I’m a PhD student in psychology, and I attended your talk at the Stanford Graduate School of Business earlier this year. I’m writing to ask you about something I remember you discussing at that talk: The possible role of qualitative methods in addressing issues of replicability, reproducibility, and rigor. In particular, I […]

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Power analysis and NIH-style statistical practice: What’s the implicit model?

So. Following up on our discussion of “the 80% power lie,” I was thinking about the implicit model underlying NIH’s 80% power rule. Several commenters pointed out that, to have your study design approved by NSF, it’s not required that you demonstrate that you have 80% power for real; what’s needed is to show 80% […]

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Bayesians are frequentists

Bayesians are frequentists. What I mean is, the Bayesian prior distribution corresponds to the frequentist sample space: it’s the set of problems for which a particular statistical model or procedure will be applied. I was thinking about this in the context of this question from Vlad Malik: I noticed this comment on Twitter in reference […]

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Chasing the noise in industrial A/B testing: what to do when all the low-hanging fruit have been picked?

Commenting on this post on the “80% power” lie, Roger Bohn writes: The low power problem bugged me so much in the semiconductor industry that I wrote 2 papers about around 1995. Variability estimates come naturally from routine manufacturing statistics, which in semicon were tracked carefully because they are economically important. The sample size is […]

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Stan goes to the World Cup

Leo Egidi shares his 2018 World Cup model, which he’s fitting in Stan. But I don’t like this: First, something’s missing. Where’s the U.S.?? More seriously, what’s with that “16.74%” thing? So bogus. You might as well say you’re 66.31 inches tall. Anyway, as is often the case with Bayesian models, the point here is […]

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About that quasi-retracted study on the Mediterranean diet . . .

Some people asked me what I thought about this story. A reporter wrote to me about it last week, asking if it looked like fraud. Here’s my reply: Based on the description, there does not seem to be the implication of fraud. The editor’s report mentioned “protocol deviations, including the enrollment of participants who were […]

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