Ed Yong once wrote "science is the teamiest of team sports"

we already have estimands and predictimands, so I guess "descriptimands" is just citations on the floor at this point

the estimands for MBA curriculum looks lit 🔥

No book recs but just good luck and swift recovery

For what it's worth, here we usually pronounce it with a proper, yet wrong, German "ch" (we don't have that soft ch-sh-like sound but our local "ch" sounds similar to the harder German "ch").

A frame from there movie Inglorious Bastards, where a German officer sits at a bar and the man impersonating a German officer in front of him signs "3" with his hand using the 3 middle fingers.

A tweet from May 2024 (part of a discussion about academic uses of quarto): Another hurdle for writing big papers will require to ease collaborations by creating good real-time browser-based online collab environments. The current solution Posit suggests is (not well known and) a bit insufficient - not as easy as just opening an Overleaf or google docs.

at last!

Research ❤️ Software Development | Ehud Karavani In a world where science becomes inseparable from coding, code becomes king.

one inescapable truth of modern science conduct is that if you're a researcher then you're a software engineer.
but if you make the most out of it, it can make you a better researcher.
just had to get some stuff out of the system and into yours 🫶

ehud.co/blog/2026/01...

the upside is that the amount of information you can encode depends on the number of dimensions you have, so this near-orthogonality allows you to store more information in practice with fewer actual dimensions. you win some you lose some 💅

bayesian pooling >> bayesian updating

I second this. Isn't this an equivalent discussion of intention-to-treat effects for policy makers vs. per-protocol effects for the people?

similar to overlap weighting (or trimming), sometimes the data forces your hand. but if you're clear about whom your conclusions should apply to, then it is likely to be better than knowing nothing.

Not to mention that a lot of prediction tasks are actually anticausal in nature: disease (Y) produces the symptoms (X), objects cause image pixels, intent causes words...

do many hundreds of pull request reviews count as professional critic? 😅

In my experience, many of the culprits are due to data entry (and organization) directly in excel. In participant-facing studies, I used to advocate research assistants to enter data through a shared Google form (or equivalent). Makes entry much more structured for relatively little work.

I think the more important benchmark in such long hauls is how well your battery synchronizes with your bladder 🙃

i'm a simple man: some 6-length codes are best remembered in 3 pairs and others are better remembered as 2 triplets. which type comes more often is left for astrology.

STAT saw and raised

always use a Mondrian theme in your ggplots
@statsepi.bsky.social

Note of reflection (March 5, 2020) This model was conceived in 2010, now more than 10 years ago, and not very long after Git itself came into being. In those 10 years, git-flow (the branching model laid out in this article) has become hugely popular in many a software team to the point where people have started treating it like a standard of sorts — but unfortunately also as a dogma or panacea. During those 10 years, Git itself has taken the world by a storm, and the most popular type of software that is being developed with Git is shifting more towards web apps — at least in my filter bubble. Web apps are typically continuously delivered, not rolled back, and you don't have to support multiple versions of the software running in the wild. This is not the class of software that I had in mind when I wrote the blog post 10 years ago. If your team is doing continuous delivery of software, I would suggest to adopt a much simpler workflow (like GitHub flow) instead of trying to shoehorn git-flow into your team. If, however, you are building software that is explicitly versioned, or if you need to support multiple versions of your software in the wild, then git-flow may still be as good of a fit to your team as it has been to people in the last 10 years. In that case, please read on. To conclude, always remember that panaceas don't exist. Consider your own context. Don't be hating. Decide for yourself.

To his credit, he states as much as the very beginning of the post. The type of software this approach aims for is rarely seen nowadays, and (as someone who once thought 𝘵𝘩𝘪𝘴 𝘪𝘴 𝘵𝘩𝘦 𝘸𝘢𝘺) it should be judged accordingly. no need to be too harsh on it.

📣 NEW! I’ve just released the BIGGEST and perhaps most creative project I’ve ever worked on!

“Searching for Birds” searchingforbirds.visualcinnamon.com 🐤

A project, an article, an exploration that dives into the data that connects humans with birds, by looking at how we search for birds.

of course, this meme is magnetic 🧲 🧭

Robocalypse: The Revival of the Mechanical Turk

it's a good view, don't get me wrong, but I also find it a bit limiting - because you can benefit from using random effects to pool estimates / shrink effects of finite factors, too, especially in imbalanced settings

that whammy bar on the guitar is excellent detail 👌

funny, I wasn't aware he had other fame aside from his spanning trees fame

Flexible use of a multi-purpose tool by a cow Osuna-Mascaró and Auersperg report flexible, multipurpose tool use in a cow, expanding the known range of mammalian tool users and underscoring overlooked cognitive capacities in livestock.

If you haven't watched the video abstract about Veronika the tool-using cow, you really should, especially the message from her owner at the end! It cleanses the timeline a bit.
🧪

www.cell.com/current-biol...

Quarto's yaml autocomplete was my sole inspiration for coding a json schema for some internal package we developed that has a yaml/hydra interface.
was a fun Pydantic exercise but I wouldn't recommend to a friend...

*doesn't have to necessarily write all of the code, but thoughtful use of llms to contemplate different approaches rather than blindly accepting code shows care and more detail-oriented mentality and critical thinking, which are broader traits of interest in an era where everyone can now code.

this. "why" questions are the key.
someone writing their own code* will have in depth considerations of choices made because they struggled with deciding on different implementations. but delegating coding to an llm makes them only a reader of the code, not so different than you reviewing it.