Wes Bonifay
@wesbonifay.bsky.social
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Reposted by Wes Bonifay
pixelatedboat.bsky.social
In my opinion it’s time to retire the Nobel prizes for fields where most of the important discoveries have already been made, like physics, and add prizes for newer fields where substantial innovations occur every year, like speedrunning
Reposted by Wes Bonifay
bayesianboy.bsky.social
I think it is an incredible privilege to get paid a living wage in order to think. Most jobs in existence today are all but prohibitive of thinking, let alone “thinking jobs.” I am baffled every day by the number of people who openly attest to being incapable of or averse to thinking a thought.
ouinne.bsky.social
The only correct take on a fundamentally anti-human technology.
Screenshot of David Simon interview 
SHAPIRO: OK, so you've spent your career creating television without Al, and I could imagine today you thinking, boy, I wish I had had that tool to solve those thorny problems...
SIMON: What?
SHAPIRO: ...Or saying...
SIMON: You imagine that?
SHAPIRO: ...Boy, if that had existed, it would have screwed me over.
SIMON: I don't think Al can remotely challenge what writers do at a fundamentally creative level.
SHAPIRO: But if you're trying to transition from scene five to scene six, and you're stuck with that transition, you could imagine plugging that portion of the script into an Al and say, give me 10 ideas for how to transition this.
SIMON: I'd rather put a gun in my mouth.
Reposted by Wes Bonifay
barrycsmith.bsky.social
Next Friday, talk, discussion and reception at the Francis Crick Institute with philosopher, Hasock Chang talking about the tricky role of measurement in science. Free and open to all

Part of School of Advanced Study and Crick Institute Being Human Lectures

www.eventbrite.co.uk/e/measuremen...
Measurement and the search for meaningful scientific concepts
Professor Hasok Chang, Professor of History and Philosophy of Science at the University of Cambridge, delivers the eight Being Human talk.
www.eventbrite.co.uk
wesbonifay.bsky.social
Using printable protractors (though I own a real one (of course)). I will lead this exercise today and report back on how much they hated it/thought it was a waste of time
wesbonifay.bsky.social
As part of my ongoing battle against chatGPT, I have asked my students to perform factor rotation manually, with tracing paper and a protractor
Reposted by Wes Bonifay
srstudent.bsky.social
Out today in BRM!

We investigate the small(er) sample performance of an MCMC method for checking whether item response data produce an interval scale using the Rasch model. These checks are viable at achievable sample sizes in survey research.

Open access: link.springer.com/article/10.3...
Applying Bayesian checks of cancellation axioms for interval scaling in limited samples - Behavior Research Methods
Interval scales are frequently assumed in educational and psychological research involving latent variables, but are rarely verified. This paper outlines methods for investigating the interval scale assumption when fitting the Rasch model to item response data. We study a Bayesian method for evaluating an item response dataset’s adherence to the cancellation axioms of additive conjoint measurement under the Rasch model, and compare the extent to which the axiom of double cancellation holds in the data at sample sizes of 250 and 1000 with varying test lengths, difficulty spreads, and levels of adherence to the Rasch model in the data-generating process. Because the statistic produced by the procedure is not directly interpretable as an indicator of whether an interval scale can be established, we develop and evaluate procedures for bootstrapping a null distribution of violation rates against which to compare results. At a sample size of 250, the method under investigation is not well powered to detect the violations of interval scaling that we simulate, but the procedure works quite consistently at N = 1000. That is, at moderate but achievable sample sizes, empirical tests for interval scaling are indeed possible.
link.springer.com
Reposted by Wes Bonifay
kevinjkircher.com
Sometimes I think about how from 1935-1975ish, Bell Labs produced an insane amount of revolutionary science and technology, including 11 Nobel Prizes, the transistor, UNIX, C, the laser, the solar cell, information theory, etc. The secret? Provide scientists with ample, steady, no-strings funding.
sites.stat.columbia.edu
Reposted by Wes Bonifay
mraginsky.bsky.social
Rudolf Kalman put it nicely (and provocatively): link.springer.com/chapter/10.1...
Quote from R. Kalman, "Identification from real data": For an objective outsider, much of the historical development
of statistics is a long series of attempts to dodge the inevitable
implications of uncertainty. Whenever the conventional statistical
treatment of a problem gives a unique (certain) answer, as in
maximum likelihood estimation, in least squares, ... , common sense
should tell us that such a miracle is possible only if additional
assumptions (deus ex machina) are imposed on the data which
somehow succeed in neutralizing the intrinsic uncertainty. We
shall use the technical term "prejudice" for such assumptions.
In other words, statistical methodology has been handicapped
because statisticians have become mesmerized by the deep-seated
hope of giving certain answers to problems where the uncertainty
is intrinsic. This is politics, not science. For example, least
squares is very popular because it always gives a unique answer.
But this is exactly where its fatal weakness lies; when we pose
Nature a question, as in identification problems, we must not
phrase that question in such terms that the answer is
predetermined regardless of the nature of the data.
Reposted by Wes Bonifay
andyzax.bsky.social
While going through text messages from Kaleb—insert an image of Charlie Brown saying “sigh” here—I found this, from about a year ago. The point he was making applies to all of us.
I occasionally check in with Al, not just because there's filthy gig money in Al training but also to see where the tech is at, and ask it to write an essay about California by me and the results are always so bad, so unfixable, so obviously counterfeit, that it gives me motivation to write again. It just reminds me l'm the only entity that does what I do. Never mind getting more validation than that, it tells me l'm not a statistic.
wesbonifay.bsky.social
I struggle to relate to researchers who push back against abandonment/replacement when their model/method is proven to be wrong (probably because I don't cherish anything about my work)
wesbonifay.bsky.social
Regularly recalling this passage from @krispreacher.bsky.social (2006). Kris was writing specifically about fitting propensity (FP) but I think his term "cherished models" applies to so many debates in psych research
Preacher (2006): "The good fit of a hypothesized model to observed data, although desirable, can result from the model’s inherent ability to predict data patterns and may have little to do with its value as a scientific tool. Cherished models may have to be abandoned or replaced if their past successes can be ascribed more to FP than to any insight they lend into the process that actually generated the data. Adopting a model selection perspective and explicitly considering FP can help researchers avoid these problems."
wesbonifay.bsky.social
What if I'm not strong in either
Reposted by Wes Bonifay
jonbois.bsky.social
writing is such a sophisticated and varied art form that i hesitate to prescribe any immutable rules of writing, except for this one: no matter what you're writing, be it fiction, personal narrative, an academic paper, or a patent application, you should begin every paragraph with "Erm ..."
wesbonifay.bsky.social
Now do psychometrics
beenwrekt.bsky.social
I'm a bit more radical about statistics than you... statistical theory is messed up because it's always vibes. There's beautiful calculations that follow from assumptions, but the assumptions are always metaphysical and unverifiable. 1/2
Reposted by Wes Bonifay
richarddmorey.bsky.social
Simonsohn has now posted a blog response to our recent paper about the poor statistical properties of the P curve. @clintin.bsky.social and I are finishing up a less-technical paper that will serve as a response. But I wanted to address a meta-issue *around* this that may clarify some things. 1/x
datacolada.bsky.social
Would p-curve work if you dropped a piano on it?
datacolada.org/129
PIano being dropped on car in car testing facility
Reposted by Wes Bonifay
robertkelchen.com
ED is requesting public comments on the direction that they should take the Institute of Education Sciences. Feedback is due October 15.

public-inspection.federalregister.gov/2025-18608.pdf
public-inspection.federalregister.gov
wesbonifay.bsky.social
how it feels to use Teams
Reposted by Wes Bonifay
nposegay.bsky.social
I'm sorry, worldwide, irrevocable, non-exclusive, transferable permission to my voice and likeness? For what now? In any manner for any purpose???

This is in academia/.edu's new ToS, which you're prompted to agree to on login. Anyway I'll be jumping ship. You can find my stuff at hcommons.org.
By creating an Account with Academia.edu, you grant us a worldwide, irrevocable, non-exclusive, transferable license, permission, and consent for Academia.edu to use your Member Content and your personal information (including, but not limited to, your name, voice, signature, photograph, likeness, city, institutional affiliations, citations, mentions, publications, and areas of interest) in any manner, including for the purpose of advertising, selling, or soliciting the use or purchase of Academia.edu's Services.
Reposted by Wes Bonifay
Reposted by Wes Bonifay
jamiecummins.bsky.social
Can large language models stand in for human participants?
Many social scientists seem to think so, and are already using "silicon samples" in research.

One problem: depending on the analytic decisions made, you can basically get these samples to show any effect you want.

THREAD 🧵
The threat of analytic flexibility in using large language models to simulate human data: A call to attention
Social scientists are now using large language models to create "silicon samples" - synthetic datasets intended to stand in for human respondents, aimed at revolutionising human subjects research. How...
arxiv.org