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Archive of posts filed under the Decision Theory category.

From no-data to data: The awkward transition

I was going to write a post with the above title, but now I don’t remember what I was going to say!

Where that title came from

I could not think of a good title for this post. My first try was “An institutional model for the persistence of false belief, but I don’t think it’s helpful to describe scientific paradigms as ‘true’ or ‘false.’ Also, boo on cheap laughs at the expense of academia,” and later attempts were even worse. At […]

Data-based ways of getting a job

Bart Turczynski writes: I read the following blog with a lot of excitement: Then I reread it and paid attention to the graphs and models (which don’t seem to be actual models, but rather, well, lines.) The story makes sense, but the science part is questionable (or at least unclear.) Perhaps you’d like to have […]

The persistence of bad reporting and the reluctance of people to criticize it

Mark Palko pointed to a bit of puff-piece journalism on the tech entrepreneur Elon Musk that was so extreme that it read as a possible parody, and I wrote, “it could just be as simple as that [author Neil] Strauss decided that a pure puff piece would give him access to write a future Musk […]

On deck through the rest of the year

July: The Ponzi threshold and the Armstrong principle Flaws in stupid horrible algorithm revealed because it made numerical predictions PNAS forgets basic principles of game theory, thus dooming thousands of Bothans to the fate of Alderaan Tutorial: The practical application of complicated statistical methods to fill up the scientific literature with confusing and irrelevant analyses […]

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 […]

What is the role of statistics in a machine-learning world?

I just happened to come across this quote from Dan Simpson: When the signal-to-noise ratio is high, modern machine learning methods trounce classical statistical methods when it comes to prediction. The role of statistics in this case is really to boost the signal-to-noise ratio through the understanding of things like experimental design.

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 […]

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 […]

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 […]

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 […]

Forking paths come from choices in data processing and also from choices in analysis

Michael Wiebe writes: I’m a PhD student in economics at UBC. I’m trying to get a good understanding of the garden of forking paths, and I have some questions about your paper with Eric Loken. You describe the garden of forking paths as “researcher degrees of freedom without fishing” (#3), where the researcher only performs […]

Comments on Limitations of Bayesian Leave-One-Out Cross-Validation for Model Selection

There is a recent pre-print Limitations of Bayesian Leave-One-Out Cross-Validation for Model Selection by Quentin Gronau and Eric-Jan Wagenmakers. Wagenmakers asked for comments and so here are my comments. Short version: They report a known limitation of LOO when it’s used in a non-recommended way for model selection. They report that their experiments show that […]

Ambiguities with the supposed non-replication of ego depletion

Baruch Eitam writes: I am teaching a seminar for graduate students in the social track and I decided to dedicate the first 4-6 classes to understanding the methodological crises in psychology, its reasons and some proposed solutions. In one of the classes I had the students read this paper which reports an attempt to reproduce […]

Against Screening

Matthew Simonson writes: I have a question that may be of interest to your readers (and even if not, I’d love to hear your response). I’ve been analyzing a dataset of over 100 Middle Eastern political groups (MAROB) to see how these groups react to government repression. Observations are at the group-year level and include […]

Some experiments are just too noisy to tell us much of anything at all: Political science edition

Sointu Leikas pointed us to this published research article, “Exposure to inequality affects support for redistribution.” Leikas writes that “it seems to be a really apt example of “researcher degrees of freedom.’” Here’s the abstract of the paper: As the world’s population grows more urban, encounters between members of different socioeconomic groups occur with greater […]

Garden of forking paths – poker analogy

[image of cats playing poker] Someone who wishes to remain anonymous writes: Just wanted to point out an analogy I noticed between the “garden of forking paths” concept as it relates to statistical significance testing and poker strategy (a game I’ve played as a hobby). A big part of constructing a winning poker strategy nowadays […]

Slow to update

This post is a placeholder to remind Josh Miller and me to write our paper on slow updating in decision analysis, with the paradigmatic examples being pundits being slow to update their low probabilities of Leicester City and Donald Trump in 2016. We have competing titles for this paper. Josh wants to call it, “The […]

Should Berk Özler spend $2 million to test a “5 minute patience training”?

Berk Özler writes: Background: You receive a fictional proposal from a major foundation to review. The proposal wants to look at the impact of 5 minute “patience” training on all kinds of behaviors. This is a poor country, so there are no admin data. They make the following points: A. If successful, this is really […]

“We continuously increased the number of animals until statistical significance was reached to support our conclusions” . . . I think this is not so bad, actually!

Jordan Anaya pointed me to this post, in which Casper Albers shared this snippet from a recently-published paper from an article in Nature Communications: The subsequent twitter discussion is all about “false discovery rate” and statistical significance, which I think completely misses the point. The problems Before I get to why I think the quoted […]