i like the approach here tbh it's rather extreme, but warranted if you can't be bothered to do some basic due diligence on papers whose purpose is to spread knowledge there are plenty of forums for non fact checked, sloppy writing anyway

same but influencer -> psych researcher 4 humors -> big 5

even if llms are only good at local interpolation, i think it's easy to lose sight of how vast (and sparse) the sample space is, and therefore how useful even minor interpolation can be

some Bayesian cognitive modeling students are having trouble seeing the industry-relevance of the methods (my worked out examples are research based, since it's what I do). Tips for good industry examples? @ajordannafa.com @natehaines.bsky.social @rmcelreath.bsky.social @avehtari.bsky.social

📣 The difference between replicable and not replicable is not itself scientifically replicable. 📣 New work with Erkan Buzbas, showing that verdicts such as "X% of results replicated" are based on an inferential machinery that doesn't work. arxiv.org/abs/2604.26268

disqus just started lighting up my blog comment section with ads unfortunate probably going to just delete the comment section this weekend 😬

TBF this is a trend that has been going on for quite some time. Statistical software has become so user friendly that you can do some rather complex analyses these days without truly understanding your model Is this a net bad? Hard to say. It’s on users to do their due diligence IMO

one day I will convince folks to actually use their reliability estimates and not simply report them... one day...

it’s hard to overstate how important this is for quantitative research (especially psychology/neuroscience)- computing individual-level stats independently often robs you of hard earned statistical power. highly recommended reading *both* of these wonderful blog posts!

And in what is my favorite figure ever, we look into how shrinkage impacts person-level means in both the univariate and multivariate case:

Part 2 of my shrinkage estimator series is out! Part 1 covered the univariate case, but now we dive into multivariate shrinkage 🤓 We cover Spearman's classic correlation disattenuation formula, multivariate James-Stein estimators, and hierarchical methods too haines-lab.com/post/how-to-...

guys i got a review request for a paper exploring if LLMs can instantiate "computations of psychopathology" wut

Nutpie: state-of-the-art mass matrix adaptation for HMC statmodeling.stat.columbia.edu/2026/03/20/n...

One of my main scientific regrets is not having used my time to learn more statistics. Learning new statistical approaches next to general postdoc work is not very sustainable. However, fantastic blog posts such as this one by @natehaines.bsky.social really help! haines-lab.com/post/how-to-...

good bayesians don't let their friends do NHST with bayes factors

my favorite part of this table is the implication that the best estimators are essentially those that leverage priors frequentists try really hard to avoid the concept of a prior, and yet... if you have ever fit a hierarchical model you are practically Bayesian 😈

New blog just dropped! This one is all about estimators—we cover James-Stein, classical test theory, empirical Bayes, penalized regression, and hierarchical models, showing how they all can be used to do a better job than sample stats alone 🤓 haines-lab.com/post/how-to-...

An aside—LLMs have basically solved data viz for me. What used to take a good bit of tinkering in ggplot or plotly now can be one-shotted with a data snippet/schema as context and a plain English description of what I want. The bar for data viz is now higher 🤓

In case you missed it 🤓

1/9 New blog is live! This is part 2 of a series—last time we looked at the Dunning-Kruger effect, now we are digging in to Implicit vs Explicit attitudes and the Implicit Association Test. To start, of course we need a good meme... haines-lab.com/post/part-2-...

coming soon! part 2 of the series (finally..), this time looking at implicit attitudes instead of the Dunning-Kruger effect—here is a sneak peek 🤓

when McElreath drops new videos you watch, and it is always so worth it look at this awesome graph, no better way to illustrate how cool hierarchical Bayes is 😍 youtu.be/jh3RltVrQ-Q?...

tank mom

New paper & a thread on the results 👇 ‘Reward-specific learning parameters change across normative adolescent development and are blunted in youth with high risk for depression’ acamh-onlinelibrary-wiley-com.ezp3.lib.umn.edu/doi/full/10....

eyyy survived another year! go team, your citations are appreciated 🙏

cmon guys 2025 doesn't need to be the inflection point, help me out

2026, the year of the landlord special time.com/7338176/pant...

hell yeah

TLDR; The PSF has made the decision to put our community and our shared diversity, equity, and inclusion values ahead of seeking $1.5M in new revenue. Please read and share. pyfound.blogspot.com/2025/10/NSF-... 🧵

just got a restitution check for my auto deductible from back when I got kia boi'd a few years ago.. what a pleasant surprise

awesome post, and couldn't agree more with the thesis R is great because it "just works" most of the time

Job update! It has been a little over a month now, but I'm excited to share that I have joined Oscar Health, where I'm leading a team focused on our clinical risk adjustment process 🤓

wooahh! need to give this a read 🤓

simply amazing

It has been quite a while since I posted my blog on the modeling the Dunning-Kruger effect. I intended for this to be the first of a series on modeling some classic effects in psychology/cognitive science. Any recommendations for the second post in the series? haines-lab.com/post/2021-01...

last of us S2 E2, more like last episode amirite

the moment some unintuitive simulation and empirical results finally click and you know what needs to be done 🤌 a feeling vibe coders can only dream of

SIX YEARS after the initial blog post, this paper is finally published.. what a wild ride - the blog: bit.ly/3GbOqQa - original tweet thread: x.com/Nate__Haines... - published (open access) paper: doi.org/10.1037/met0...

sounds like a black mirror episode

Using this as an excuse to share a great post by @natehaines.bsky.social on modelling and Dunning-Kruger. haines-lab.com/post/2021-01...

yes Yes YES! Feel your worries melt away as you watch the "Raccoon Whisperer" feed 25 loving raccoons in freezing weather. Clip below. Link to full 20 min video: www.youtube.com/watch?v=Ofp2...

1/6 Hello Bluesky! 👋 Excited to join this community and share my new blog. First post: Using Bayesian hierarchical models to rescue "unreliable" cognitive tasks, with the dot-probe task as my case study. cogpsychreserve.netlify.app/posts/dotpro...

this album slaps

I am surprised that many data scientists (perhaps without a stats background) do not recognize that CUPED is regression. In this post, I show you why you can't spell CUPED without FWL dpananos.github.io/posts/2025-0...

My team at Ledger just released `bermuda`, an open-source library for insurance triangle data manipulation! Check out the repo and docs below to see if it would be useful for your own work: GitHub: github.com/LedgerInvest... Docs: ledger-investing-bermuda-ledger.readthedocs-hosted.com/en/latest/

Please send me your fave blog post you ever wrote. Need some inspiration

The answer to this is that you get something that looks like: results = { 10: 1.3, 25: 1.3, 50: 1.3, 75: 1.3, 90: 1.3, } Why? It is due to late binding of the iterator value (p) in the lambda function within a list/dict comprehension ☠️

must be no pythonistas in bsky huh

time for a valentines day pop-quiz! without cheating, what will the output of `results` look like?