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Archive of posts filed under the Miscellaneous Science category.

Using black-box machine learning predictions as inputs to a Bayesian analysis

Following up on this discussion [Designing an animal-like brain: black-box “deep learning algorithms” to solve problems, with an (approximately) Bayesian “consciousness” or “executive functioning organ” that attempts to make sense of all these inferences], Mike Betancourt writes: I’m not sure AI (or machine learning) + Bayesian wrapper would address the points raised in the paper. […]

Type M errors in the wild—really the wild!

Jeremy Fox points me to this article, “Underappreciated problems of low replication in ecological field studies,” by Nathan Lemoine, Ava Hoffman, Andrew Felton, Lauren Baur, Francis Chaves, Jesse Gray, Qiang Yu, and Melinda Smith, who write: The cost and difficulty of manipulative field studies makes low statistical power a pervasive issue throughout most ecological subdisciplines. […]

“How conditioning on post-treatment variables can ruin your experiment and what to do about it”

Brendan Nyhan writes: Thought this might be of interest – new paper with Jacob Montgomery and Michelle Torres, How conditioning on post-treatment variables can ruin your experiment and what to do about it. The post-treatment bias from dropout on Turk you just posted about is actually in my opinion a less severe problem than inadvertent […]

Bird fight! (Kroodsma vs. Podos)

Donald Kroodsma writes: Birdsong biologists interested in sexual selection and honest signalling have repeatedly reported confirmation, over more than a decade, of the biological significance of a scatterplot between trill rate and frequency bandwidth. This ‘performance hypothesis’ proposes that the closer a song plots to an upper bound on the graph, the more difficult the […]

Wolfram on Golomb

I was checking out Stephen Wolfram’s blog and found this excellent obituary of Solomon Golomb, the mathematician who invented the maximum-length linear-feedback shift register sequence, characterized by Wolfram as “probably the single most-used mathematical algorithm idea in history.” But Golomb is probably more famous for inventing polyominoes. The whole thing’s a good read, and it […]

Reproducing biological research is harder than you’d think

Mark Tuttle points us to this news article by Monya Baker and Elie Dolgin, which goes as follows: Cancer reproducibility project releases first results An open-science effort to replicate dozens of cancer-biology studies is off to a confusing start. Purists will tell you that science is about what scientists don’t know, which is true but […]

Iceland education gene trend kangaroo

Someone who works in genetics writes: You may have seen the recent study in PNAS about genetic prediction of educational attainment in Iceland. the authors report in a very concerned fashion that every generation the attainment of education as predicted from genetics decreases by 0.1 standard deviations. This sounds bad. But consider that the University […]

Recently in the sister blog

This research is 60 years in the making: How “you” makes meaning “You” is one of the most common words in the English language. Although it typically refers to the person addressed (“How are you?”), “you” is also used to make timeless statements about people in general (“You win some, you lose some.”). Here, we […]

How to design future studies of systemic exercise intolerance disease (chronic fatigue syndrome)?

Someone named Ramsey writes on behalf of a self-managed support community of 100+ systemic exercise intolerance disease (SEID) patients. He read my recent article on the topic and had a question regarding the following excerpt: For conditions like S.E.I.D., then, the better approach may be to gather data from people suffering “in the wild,” combining […]

They want help designing a crowdsourcing data analysis project

Michael Feldman writes: My collaborators and myself are doing research where we try to understand the reasons for the variability in data analysis (“the garden of forking paths”). Our goal is to understand the reasons why scientists make different decisions regarding their analyses and in doing so reach different results. In a project called “Crowdsourcing […]

“The Null Hypothesis Screening Fallacy”?

[non-cat picture] Rick Gerkin writes: A few months ago you posted your list of blog posts in draft stage and I noticed that “Humans Can Discriminate More than 1 Trillion Olfactory Stimuli. Not.” was still on that list. It was about some concerns I had about a paper in Science (http://science.sciencemag.org/content/343/6177/1370). After talking it through […]

Capitalist science: The solution to the replication crisis?

Bruce Knuteson pointed me to this article, which begins: The solution to science’s replication crisis is a new ecosystem in which scientists sell what they learn from their research. In each pairwise transaction, the information seller makes (loses) money if he turns out to be correct (incorrect). Responsibility for the determination of correctness is delegated, […]

PhD student fellowship opportunity! in Belgium! to work with us! on the multiverse and other projects on improving the reproducibility of psychological research!!!

[image of Jip and Janneke dancing with a cat] Wolf Vanpaemel and Francis Tuerlinckx write: We at the Quantitative Psychology and Individual Differences, KU Leuven, Belgium are looking for a PhD candidate. The goal of the PhD research is to develop and apply novel methodologies to increase the reproducibility of psychological science. More information can […]

Why I’m not participating in the Transparent Psi Project

I received the following email from psychology researcher Zoltan Kekecs: I would like to ask you to participate in the establishment of the expert consensus design of a large scale fully transparent replication of Bem’s (2011) ‘Feeling the future’ Experiment 1. Our initiative is called the ‘Transparent Psi Project’. [https://osf.io/jk2zf/wiki/home/] Our aim is to develop […]

The (Lance) Armstrong Principle

If you push people to promise more than they can deliver, they’re motivated to cheat.

“Bombshell” statistical evidence for research misconduct, and what to do about it?

Someone pointed me to this post by Nick Brown discussing a recent article by John Carlisle regarding scientific misconduct. Here’s Brown: [Carlisle] claims that he has found statistical evidence that a surprisingly high proportion of randomised controlled trials (RCTs) contain data patterns that cannot have arisen by chance. . . . the implication is that […]

How has my advice to psychology researchers changed since 2013?

Four years ago, in a post entitled, “How can statisticians help psychologists do their research better?”, I gave the following recommendations to researchers: – Analyze all your data. – Present all your comparisons. – Make your data public. And, for journal editors, I wrote, “if a paper is nothing special, you don’t have to publish […]

Using external C++ functions with PyStan & radial velocity exoplanets

Dan Foreman-Mackey writes: I [Mackey] demonstrate how to use a custom C++ function in a Stan model using the Python interface PyStan. This was previously only possible using the R interface RStan (see an example here) so I hacked PyStan to make this possible in Python as well. . . . I have some existing […]

Theoretical Statistics is the Theory of Applied Statistics: How to Think About What We Do

Above is my talk at the 2017 New York R conference. Look, no slides! The talk went well. I think the video would be more appealing to listen to if they’d mixed in more of the crowd noise. Then you’d hear people laughing at all the right spots. P.S. Here’s my 2016 NYR talk, and […]

How is a politician different from a 4-year-old?

A few days ago I shared my reactions to an op-ed by developmental psychologist Alison Gopnik. Gopnik replied: As a regular reader of your blog, I thought you and your readers might be interested in a response to your very fair comments. In the original draft I had an extra few paragraphs (below) that speak […]