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Archive of posts filed under the Zombies category.

Stephen Wolfram invented a time machine but has been too busy to tell us about it

Following up on our previous posts here and here . . . I came across this interview of John Conway and Siobhan Roberts, Conway’s biographer: Siobhan, from the book I felt that there was a strong sense of competitiveness and ego in the maths world. In the research that you did, and conversations that you […]

Reverse-engineering the problematic tail behavior of the Fivethirtyeight presidential election forecast

We’ve been writing a bit about some odd tail behavior in the Fivethirtyeight election forecast, for example that it was giving Joe Biden a 3% chance of winning Alabama (which seemed high), it was displaying Trump winning California as in “the range of scenarios our model thinks is possible” (which didn’t seem right), and it […]

An odds ratio of 30, which they (sensibly) don’t believe

Florian Wickelmaier and Katharina Naumann write: In a lab course, we came across a study on the influence of “hemispheric activation” on the framing effect in decision making by Todd McElroy and John J. Seta [Brain and Cognition 55 (2004) 572-580, doi:10.1016/j.bandc.2004.04.002]: Two experiments were conducted to determine whether the functional specializations of the left […]

Piranhas in the rain: Why instrumental variables are not as clean as you might have thought

Woke up in my clothes again this morning I don’t know exactly where I am And I should heed my doctor’s warning He does the best with me he can He claims I suffer from delusion But I’m so confident I’m sane It can’t be a statistical illusion So how can you explain Piranhas in […]

Estimated “house effects” (biases of pre-election surveys from different pollsters) and here’s why you have to be careful not to overinterpret them:

Elliott provides the above estimates from our model. As we’ve discussed, as part of our fitting procedure we estimate various biases, capturing in different ways the fact that surveys are not actually random samples of voters from an “urn.” One of these biases is the “house effect.” In our model, everything’s on the logit scale, […]

Whassup with the dots on our graph?

The above is from our Economist election forecast. Someone pointed to me that our estimate is lower than all the dots in October. Why is that? I can come up with some guesses, but it’s surprising that the line is below all the dots. Merlin replied: That happened a bunch of times before as well. […]

Between-state correlations and weird conditional forecasts: the correlation depends on where you are in the distribution

Yup, here’s more on the topic, and this post won’t be the last, either . . . Jed Grabman writes: I was intrigued by the observations you made this summer about FiveThirtyEight’s handling of between-state correlations. I spent quite a bit of time looking into the topic and came to the following conclusions. In order […]

Calibration problem in tails of our election forecast

Following up on the last paragraph of this discussion, Elliott looked at the calibration of our state-level election forecasts, fitting our model retroactively to data from the 2008, 2012, and 2016 presidential elections. The plot above shows the point prediction and election outcome for the 50 states in each election, showing in red the states […]

Don’t ever change, social psychology! You’re perfect just the way you are

Richard Juster points us to this article, “Vocal characteristics predict infidelity intention and relationship commitment in men but not in women,” where by “men,” they meant 88 male college students, and by “women,” then meant 128 female college students, and by “predict,” they meant not very well. The story was featured in various classy news […]

“Everybody wants to be Jared Diamond”

As the saying goes, “Everybody wants to be Jared Diamond, that’s the problem.” (See also here and here.) The funny thing is, this principle also applies to . . . Jared Diamond himself! See this review by Anand Giridharadas, sent to me by Mark Palko.

“Stop me if you’ve heard this one before: Ivy League law professor writes a deepthoughts think piece explaining a seemingly irrational behavior that doesn’t actually exist.”

Under the heading, “Stop me if you’ve heard this one before,” Palko writes: An Ivy League law professor writes a deepthoughts think piece explaining a seemingly irrational behavior that doesn’t actually exist. (see here and here) My favorite bit is this, from the Ivy League law professor in question: What’s more, macroeconomists have typically spent […]

Misrepresenting data from a published source . . . it happens all the time!

Following up on yesterday’s post on an example of misrepresentation of data from a graph, I wanted to share a much more extreme example that I wrote about awhile ago, about some data misrepresentation in an old statistics textbook: About fifteen years ago, when preparing to teach an introductory statistics class, I recalled an enthusiastic […]

Alexey Guzey plays Stat Detective: How many observations are in each bar of this graph?

How many data points are in each bar of the top graph above? (See here for background.) It’s from this article: Milewski MD, Skaggs DL, Bishop GA, Pace JL, Ibrahim DA, Wren TA, Barzdukas A. Chronic lack of sleep is associated with increased sports injuries in adolescent athletes. Journal of Pediatric Orthopaedics. 2014 Mar 1;34(2):129-33. […]

Social science and the replication crisis (my talk this Thurs 8 Oct)

My talk at the WZB Berlin Social Science Center 3pm (Central European Time): Social science and the replication crisis The replication crisis is typically discussed in the context of particular silly claims, or in terms of the sociology of science, or with regard to controversies in statistical practice. Here we discuss the content of unreplicated […]

The view that the scientific process is “red tape,” just a bunch of hoops you need to jump through so you can move on with your life

Summary Awhile ago I hypothesized that many researchers “think they already know the truth, and they think of discussions of evidence, data quality, statistics, etc., as a sort of ‘red tape’ or distraction from the larger issues.” But now I’m thinking that it’s not just statistics but really the entire scientific process that they view […]

Covid-19 -> Kovit-17 (following the himmicanes principle)

We all want to do our part to contain the virus. In particular, cognitive and social scientists have been encouraged to apply their special expertise to better direct social behavior. I was thinking about this after reading this recent comment by someone named Jim: What if we just spell “cancer” with a “k”? Wow!! Instant […]

It’s kinda like phrenology but worse. Not so good for the “Nature” brand name, huh? Measurement, baby, measurement.

Federico Mattiello writes: I thought you might find this thread interesting, it’s about a machine learning paper building a “trustworthiness score” from faces databases and historical (mainly British) portraits. It checks many bias boxes I believe, but my biggest complaint (I know it shouldn’t be) is the linear regression of basically spherical clouds of points: […]

“Postmortem of a replication drama in computer science”

Rik de Kort writes: This morning I stumbled across a very interesting blog post, dissecting some drama related to a non-replicating paper in computer science land. The question the paper tries to answer is whether some programming languages are more error prone than others. For a paper in computer science I would expect all their […]

If something is being advertised as “incredible,” it probably is.

This post was originally titled, “Asleep at the wheel: Junk science gets promoted by Bill Gates, NPR, Ted, University of California, Google, etc.,” but I decided the above quote had better literary value. We’ve had a few posts now about discredited sleep scientist Matthew Walker; see here and here, and here, with the disappointing but […]

(1) The misplaced burden of proof, and (2) selection bias: Two reasons for the persistence of hype in tech and science reporting

Palko points to this post by Jeffrey Funk, “What’s Behind Technological Hype?” I’ll quote extensively from Funk’s post, but first I want to make a more general point about the burden of proof in scientific discussions. What happens is that a researcher or team of researchers makes a strong claim that is not well supported […]