Category: Miscellaneous Statistics

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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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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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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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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One good and one bad response to statistics’ diversity problem

(This is Dan) As conference season rolls into gear, I thought I’d write a short post contrasting some responses by statistical societies to the conversation that the community has been having about harassment of women and minorities at workshops and conferences. ISI: Do what I say, not what I do Let’s look at a different diversity […]

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