Ah, glad you liked it. I use it in my teaching as well and often feel grateful for the wonderful illustrations that @rpsychologist.com has done over the years!
03.03.2026 22:18 — 👍 2 🔁 0 💬 0 📌 0Ah, glad you liked it. I use it in my teaching as well and often feel grateful for the wonderful illustrations that @rpsychologist.com has done over the years!
03.03.2026 22:18 — 👍 2 🔁 0 💬 0 📌 0I'm looking for a cite on the potential of hidden moderators as responsible for failed replications. This was a regular dialog in peak replication crisis, but can't find/recall a specific cite arguing or laying out this possibility. Maybe a perspectives piece?
03.03.2026 15:50 — 👍 6 🔁 1 💬 5 📌 0
Now out in the American Sociological Review
We present the first large-scale assessment of the structure and evolution of temporalities expressed in U.S. climate change news coverage (2000 to 2021). For this, we analyzed more than 23,000 statements about climate change effects and actions. 🧵 1/
Not a YouTube video, but I liked this explainer from @rpsychologist.com
rpsychologist.com/likelihood/
A minimalist isometric 3D illustration on a dark background showing a central glowing cube labeled ".dotfiles" with a git icon. Glowing neon lines radiate from the cube to icons representing "Shell Config," "Editor & Tooling," and "AI Agents (Claude & OpenCode)." A sidebar labeled "Skills Marketplace" displays specialized icons for R, Shiny, Quarto, and writing voices, illustrating a unified development environment.
My dotfiles now manage my AI coding agents 🤖 Same instructions for Claude Code and OpenCode, symlinked from one repo. Here's how I set it up: https://drmo.site/CCrf37
02.03.2026 13:16 — 👍 7 🔁 2 💬 1 📌 0
Your model is only as good as its assumptions. 📊 But what happens when your data breaks the rules? Let’s dive into how to check your model assumptions—and exactly how to fix those pesky violations: 🧵👇
easystats.github.io/performance/...
#rstats #easystats #performance
in my decision making class I run a version of The Endowment Effect that goes like this:
When students come in, sealed envelopes are waiting on their chair-arms.
The main slide that greets them says 'NO TOUCHING'
The critique of unmeasured confounding is often levied in a lazy/broad way. It is trivially true in any observational study. But if the critic can't think of a plausible such confounder and posit a reasonable direction/magnitude of its bias then they're not doing productive science.
01.03.2026 18:47 — 👍 110 🔁 19 💬 7 📌 8
All your p-values are wrong
Nice blog by Guillaume Rousselet garstats.wordpress.com/2026/02/27/p...
Javascript code for a noncentral t cdf produced by Claude. It is simply a call to the normal CDF, which is not correct (though will be a decent approximation with large N).
I've been testing Claude to see how well it can "vibe out" a stat. power app that I've already coded completely myself - so I know what I want. It mostly gets things right with animations (those are easily verifiable) but looking into the backend stats code is nightmare inducing (see pic).
28.02.2026 09:22 — 👍 79 🔁 23 💬 4 📌 10
Mein Buch #Einsamkeit erscheint am 25. März! Die ersten Termine für Lesungen stehen bereits fest. (Details im Thread)
📅 17.03.2026 📍München
📅 18.03.2026 📍Berlin
📅 19.03.2026 📍Leipzig
📅 20.03.2026 📍Leipzig
📅 26.03.2026 📍Hannover
www.fischerverlage.de/buch/prof-dr...
#Lesereise #Sachbuch
Great new paper led by @bringmannlaura.bsky.social, highlight the need to collect qualitative data in ESM / EMA research.
#PsychSciSky 🧪
www.nature.com/articles/s44...
An illustration of selection on significance for a test with 50% power
I wrote a short blog post that describes selection on significance in plain language and then proposes and criticizes two alternatives (selection on precision and registered reports). This is me working through my thoughts online, so feedback is very welcome.
ryancbriggs.net/blog/the-pro...
New Paul Meehl Graduate School course: Advanced Basic Statistics Workshop: Deepening Your Understanding of Standard Statistical Analyses
March 27, 2026
Sign up:
paulmeehlschool.github.io/2026-02-23-a...
Aside from the bonkers horse analogy, this argument belies an unfortunately common perspective that the reason why teachers and professors assign homework is because it somehow benefits us for that work to get done.
25.02.2026 21:32 — 👍 18 🔁 3 💬 2 📌 0
Am I understanding this right? The standard grading scale in Denmark goes –3, 00, 02, 4, 7, 10, 12 !
This is amazing. I kinda love it. I mean I hate it. But I love it.
Lots to say on this, but one thing recently came to my mind is the isomorphism between “wide format” & “long format” for panel datasets. The latter treats time as a variable, which confuses the issue, at least conceptually. DiD with wide format is clear and here: journals.sagepub.com/doi/10.1177/...
25.02.2026 00:55 — 👍 4 🔁 5 💬 1 📌 0
The northeast blizzard ain't stopping us!
You won't want to miss the Moral Psychology Pre-conference at @spspnews.bsky.social in Chicago this week! It should be an incredible day of sharing research and networking. Hope to see you there.
I'm hiring a postdoc at @cmu.edu (w/ far.ai & @dgrand.bsky.social + @gordpennycook.bsky.social)!
How do LLMs shape human beliefs — and what do we do about it? AI safety meets behavioral science.
Open to technical and social science backgrounds.
There's promise in using LLMs for code review, but it's tricky things to make sure it's not overwhelming.
I was looking at this new experimental package by Simon Couch and I really love how it allows you to review code iteratively. #rstats #ai #llms
github.com/simonpcouch/...
Interesting start to the week:
Paper rejected with the justification that the journal does not accept pre-registrations or manuscripts that are based on pre-registrations 🤔🤪
#Registration #PreRegistration #RegisteredReport
The lack of uptake of sequential analysis shows how irrational scientists are, and how their methods are driven by norms. Sequential analyses give you more flexibility and are more efficient than a single hypothesis test, and yet, they are still very rare. lakens.github.io/statistical_...
23.02.2026 16:59 — 👍 11 🔁 4 💬 2 📌 0
Does your R-script work on your computer? Want to make it fully reproducible on ANY system, now and in the future? In this video, I demonstrate how to turn R scripts into reproducible, version-controlled projects with worcs, renv, targets, and testthat to verify reproducibility. youtu.be/3mgRFMr5APU
23.02.2026 09:03 — 👍 6 🔁 2 💬 0 📌 0
DAG representing the causal structure of a standard difference-in-differences design with two locations and two time periods—units in one location in the post-period receive treatment. $L$ = group or location indicator (treated vs. untreated location); $T$ = time indicator (pre vs. post period); $U$ = unobserved time-invariant confounders (e.g., GDP per capita, general health status, public health infrastructure). $X \leftarrow T \rightarrow Y$ represents a common time trend affecting both locations equally. The causal effect of $X$ on $Y$ is identified by conditioning on $\{L, T\}$, which corresponds to using location and time indicator variables in a regression like `y ~ location * period`.
DAG representing the causal structure of a standard difference-in-differences design, but with explicit pre- and post-treatment outcomes. $L$ = group or location indicator (treated vs. untreated location); $T_\text{post}$ = post-period measurement (indicator that the observation occurs after the intervention); $X_\text{post}$ = treatment (which only occurs for treated locations in the post period); $Y_\text{pre}$ and $Y_\text{post}$ = outcome measured before and after the intervention. $U$ = unobserved time-invariant confounders (e.g., GDP per capita, general health status, public health infrastructure). $Y_\text{pre} \rightarrow Y_\text{post}$ represents outcome persistence (e.g. autocorrelation or slow-moving changes); $X_\text{post} \leftarrow T_\text{post} \rightarrow Y_\text{post}$ represents a common time trend affecting both locations equally. The causal effect of $X_\text{post}$ on $Y_\text{post}$ is identified by conditioning on $\{L, T_\text{post}\}$, which corresponds to using location and time indicator variables in a regression like `y ~ location * period`.
spending my sunday evening once again attempting to draw a DAG for diff-in-diff
23.02.2026 04:08 — 👍 85 🔁 12 💬 10 📌 4
Remember that you compute power for the smallest effect size of interest. Not the effect you hope for. The smallest effect that you do not want to miss.
This is in line with the basic idea of conditional error control: *If* there is an effect large enough to matter, we are likely to detect it.
Wonderful to see this replication effort in the physical sciences using the models of many labs, preregistration, and transparency that have benefitted other fields.
And, an investment of $9.5 million to do it!
www.nature.com/articles/d41...
After watching Richard McElreath's lecture on measurment models, I got comparatively more excited about my working paper with Yaroslav on accounting for non-classical measurement error in belief-updating experiments and my foray into measurement and time-series econometrics.
A thread.
#EconSky
📣 Join @katiecorker.bsky.social (ASAPbio), @thewilkybarkid.bsky.social, and @neurosarda.bsky.social (@prereview.bsky.social) on March 5th for the Community Call!
Speakers will discuss the ins and outs of doing open peer review as part of a team🤝
Register 👇️
buff.ly/5I0wC4l
I've been interested when we were going to reach the capability threshold for this.
Once the tires have been kicked, all social science journals should adopt computational reproducibility on submission.
Huge reduction in reviewer burden by eliminating a whole class of errors up front.
Very good and a nice demonstration that tracing rules can still be helpful for DAGs. The new napkin problem by Pearl can also be recast using tracing rules from which an IV estimator emerges (assuming linearity).
20.02.2026 01:27 — 👍 1 🔁 0 💬 0 📌 0