Author: Andrew

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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