Tessera

Folgen

Winston Lin

@linstonwin.bsky.social

senior lecturer in statistics, penn NYC & Philadelphia https://www.stat.berkeley.edu/~winston

42 Posts2035 Follower 1635 Folgt

David Evans @daveevansphd.bsky.social · 2mo ago

"The Illusion of Comparability Among Standardised Effect Sizes: Why Education Evaluations Should Report Raw Effects" New @cgdev.org working paper (with Rossiter, Hares, and Henny) cgdev.org/publication/... Summary blog post cgdev.org/blog/standar...

Jae Yeon Kim @jaeyeonkim.bsky.social · 3mo ago

When I reflect on what shaped me most as a scholar, one seminar stands out: Ruth Collier’s dissertation writing workshop at Berkeley. I wrote about its influence on my research, teaching, and scholarship in general. jaeyeonkim.substack.com/p/the-ruth-c...

The Ruth Collier Method

Think Clearly and Write Honestly

jaeyeonkim.substack.com

Julia M. Rohrer @dingdingpeng.the100.ci · 4mo ago

Good news everyone 🥳 Our (w @vincentab.bsky.social) primer on models as prediction machines (with the marginaleffects package) is finally officially published!> journals.sagepub.com/doi/10.1177/...

Models as Prediction Machines: How to Convert Confusing Coefficients Into Clear Quantities - Julia M. Rohrer, Vincent Arel-Bundock, 2026

Psychological researchers usually make sense of regression models by interpreting coefficient estimates directly. This works well enough for simple linear model...

journals.sagepub.com

Brendan Nyhan @brendannyhan.bsky.social · 3mo ago

Journal editors - the status quo on preregistration is not working! You need to check submissions vs. preregistrations before sending articles out for review. *56%* of experiments I reviewed in last year have severe problems with non-disclosure, undocumented deviations, & more - see Claude summary ↓

Ryan Briggs @ryancbriggs.net · 4mo ago

You guys @carlislerainey.bsky.social has a free textbook online and it seems really useful pos5747.github.io/notes/

A screenshot showing:
Introduction

These are notes for my class on probability models. In these notes, I walk through the concepts and computation that support modern probability modeling in political science using both maximum likelihood and Bayesian approaches.

The Goal

There are many excellent books on probability models. But I felt the need to write my own. Why? I saw three problems.

First, some classes assign a huge textbook. It might be possible for the strongest and most motivated students to become familiar with the range of topics covered in these textbook, but impossible to master. Instead, these textbooks seem like references, something you’re supposed to constantly be referring back to throughout your career. I know this because many of these books have instructors’ guides that suggest what should be covered in a single semester, what should be skipped, and how one might jump around. Instead, I want a book that students can work through beginning to end and master each idea.
Second, some classes assign a variety of sections from several books and a collection of articles. But then the story told in the readings isn’t coherent. The styles are changing, the author’s tastes are changing, and the notation is changing. Switching among authors can feel like whiplash when learning a difficult subject. Instead, I want a book that tells a continuous story with consistent style, tastes, and notation.
Third, some classes assign readings that support the lecture material, without exact alignment between the two. For better or worse, the content covered by the instructor in class feels like the most important material. Thus, I want a book that exactly aligns with the material I cover in class.

Winston Lin @linstonwin.bsky.social · 4mo ago

Bilingual joke? French Wikipedia says the Poisson distribution is "not to be confused with Fisher's distribution" (the F-distribution) fr.wikipedia.org/wiki/Loi_de_...

Screenshot from "Loi de Poisson" (French Wikipedia article). It says, "Ne doit pas être confondu avec Loi de Fisher."

Stephen Wild @stephenjwild.bsky.social · 4mo ago

Rosenbaum's Observation and Experiment is great too. I have sadly not read his more technical books yet. www.hup.harvard.edu/books/978067...

Observation and Experiment — Harvard University Press

A daily glass of wine prolongs life—yet alcohol can cause life-threatening cancer. Some say raising the minimum wage will decrease inequality while others say it increases unemployment. Scientists onc...

hup.harvard.edu

Julia M. Rohrer @dingdingpeng.the100.ci · 5mo ago

You need to bring in the same toolkit as in studies that try to establish causality without randomization. I know it sounds unfair, but I don’t make the rules. These situations are instances of post-treatment bias, if you want to read up on it as a psychologist:

Causal inference for psychologists who think that causal inference is not for them

Correlation does not imply causation and psychologists' causal inference training often focuses on the conclusion that therefore experiments are needed—without much consideration for the causal infer...

compass.onlinelibrary.wiley.com

Winston Lin @linstonwin.bsky.social · 6mo ago

Speaking truth to power

Valentin Amrhein@vamrhein.bsky.social · 6mo ago

Giving less power to statistical power (Higgs & Amrhein) doi.org/10.1177/0023...

Data Colada @datacolada.bsky.social · 6mo ago

A more user friendly t-test regression variable description frequency plots, and more. datacolada.org/132

Andrea Howard @drandreahoward.bsky.social · 6mo ago

🚨SOLUTIONS🚨 Desk reject more stuff with actionable feedback. Don’t request second reviews Build larger editorial boards of volunteers Wait to submit your work until it’s ready; a.k.a don’t send in your half-baked trash hoping for feedback 6/7

Drew Stommes @drewstommes.bsky.social · 6mo ago

After years in academia, I’m exploring data science and research roles in industry. I'm a quant. social scientist (PhD Yale ’24, NYU) focused on causal inference, experiments, and large-scale data. Feel free to get in touch or share; all leads appreciated. dwstommes@gmail.com

Julia M. Rohrer @dingdingpeng.the100.ci · 6mo ago

This quote also reminds me of something that we wrote in our paper on path analysis (journals.sagepub.com/doi/10.1177/...). People are just expecting *way* too much of a single study, literally new discoveries exceeding Gregor Mendel's.

We believe that to improve practices, some fundamental rethinking of what we consider a publishable scientific contribution may be necessary. Currently, researchers may feel pressured to do “everything” in a single article—summarize and synthesize the existing literature, suggest a new theory or at least modify an existing one, hypothesize moderation and/or mediation, and provide (preferably positive) empirical evidence through statistical analyses that they run themselves, maybe even across multiple studies they conducted themselves. It is perhaps unsurprising that they end up cutting corners when it comes to causal inference—a hard topic, for which psychologists often receive little training—and rely on out-of-the-box statistical models.

Jamie Cummins @jamiecummins.bsky.social · 7mo ago

Comparing registrations to published papers is essential to research integrity - and almost no one does it routinely because it's slow, messy, and time-demanding. RegCheck was built to help make this process easier. Today, we launch RegCheck V2. 🧵 regcheck.app

RegCheck

RegCheck is an AI tool to compare preregistrations with papers instantly.

regcheck.app

Andrew Gelman et al.@statmodeling.bsky.social · 7mo ago

“Coding for humans: Best practices for writing software people can read” statmodeling.stat.columbia.edu/2026/01/17/c...

“Coding for humans: Best practices for writing software people can read” | Statistical Modeling, Causal Inference, and Social Science

statmodeling.stat.columbia.edu

Julia M. Rohrer @dingdingpeng.the100.ci · 7mo ago

Accessibility is *absolutely* key but also hard because of the curse of knowledge. I've written down some writing advice here: www.the100.ci/2024/12/01/w.... If you're more of a technical person, consider teaming up with a substantive researcher for instant audience access.>

Writing about technical topics in an accessible manner

A wise man – I’m quite sure it was Brian Wansink – once pointed out that it is impossible to both read and write a lot. So, maybe reading a post about how to write just steals time from the more urgen...

the100.ci

Julia M. Rohrer @dingdingpeng.the100.ci · 7mo ago

Some people bring up (1) the cost of criticism and (2) that a lot of criticism has already been voiced but ignored. Both points are valid, so here are some suggestion for (1) reducing backlash and (2) increasing impact (from this talk of mine: juliarohrer.com/wp-content/u...

CIIG Seminar: Julia Rohrer | Making Rigorous Causal Inference More Mainstream | 20 Oct 2025

YouTube video by the Causal Inference Interest Group (CIIG)

youtube.com

Julia M. Rohrer@dingdingpeng.the100.ci · 7mo ago

Here's a suggestion for a New Year's resolution: If you see influential bad research, say something. One part of the whole replication crisis story is that a lot of psychological researchers privately knew that a lot of stuff was bad, but it wasn't discussed publicly.

Andrew Heiss @andrew.heiss.phd · 8mo ago

Some closing thoughts for my students this semester on LLMs and learning #rstats datavizf25.classes.andrewheiss.com/news/2025-12...

Will you incorporate LLMs and AI prompting into the course in the future?
No.

Why won’t you incorporate LLMs and AI prompting into the course?
These tools are useful for coding (see this for my personal take on this).

However, they’re only useful if you know what you’re doing first. If you skip the learning-the-process-of-writing-code step and just copy/paste output from ChatGPT, you will not learn. You cannot learn. You cannot improve. You will not understand the code.

In that post, it warns that you cannot use it as a beginner:

…to use Databot effectively and safely, you still need the skills of a data scientist: background and domain knowledge, data analysis expertise, and coding ability.

There is no LLM-based shortcut to those skills. You cannot LLM your way into domain knowledge, data analysis expertise, or coding ability.

The only way to gain domain knowledge, data analysis expertise, and coding ability is to struggle. To get errors. To google those errors. To look over the documentation. To copy/paste your own code and adapt it for different purposes. To explore messy datasets. To struggle to clean those datasets. To spend an hour looking for a missing comma.

This isn’t a form of programming hazing, like “I had to walk to school uphill both ways in the snow and now you must too.” It’s the actual process of learning and growing and developing and improving. You’ve gotta struggle.

This Tumblr post puts it well (it’s about art specifically, but it applies to coding and data analysis too):

Contrary to popular belief the biggest beginner’s roadblock to art isn’t even technical skill it’s frustration tolerance, especially in the age of social media. It hurts and the frustration is endless but you must build the frustration tolerance equivalent to a roach’s capacity to survive a nuclear explosion. That’s how you build on the technical skill. Throw that “won’t even start because I’m afraid it won’t be perfect” shit out the window. Just do it. Just start. Good luck. (The original post has disappeared, but here’s a reblog.)

It’s hard, but struggling is the only way to learn anything.

You might not enjoy code as much as Williams does (or I do), but there’s still value in maintaining codings skills as you improve and learn more. You don’t want your skills to atrophy.

As I discuss here, when I do use LLMs for coding-related tasks, I purposely throw as much friction into the process as possible:

To avoid falling into over-reliance on LLM-assisted code help, I add as much friction into my workflow as possible. I only use GitHub Copilot and Claude in the browser, not through the chat sidebar in Positron or Visual Studio Code. I treat the code it generates like random answers from StackOverflow or blog posts and generally rewrite it completely. I disable the inline LLM-based auto complete in text editors. For routine tasks like generating {roxygen2} documentation scaffolding for functions, I use the {chores} package, which requires a bunch of pointing and clicking to use.

Even though I use Positron, I purposely do not use either Positron Assistant or Databot. I have them disabled.

So in the end, for pedagogical reasons, I don’t foresee me incorporating LLMs into this class. I’m pedagogically opposed to it. I’m facing all sorts of external pressure to do it, but I’m resisting.

You’ve got to learn first.

Julia M. Rohrer @dingdingpeng.the100.ci · 8mo ago

Gentle reminder that a correlation coefficient isn’t a particularly great way to quantify the effect of a dichotomous treatment. See also www.the100.ci/2025/07/28/w...

What’s in a correlation?

Correlation may not imply causation, but let’s just ignore that for a second. Correlations are standardized effect size metrics and as such have some quirks by design. These are benign enough when you...

the100.ci

Jay Van Bavel, PhD@jayvanbavel.bsky.social · 9mo ago

Most scientists don't understand how effect sizes work and are therefore far too quick to dismiss "small" effects. A correlation of .03 between taking aspirin & prevention of future heart attacks implied the prevention of 85 attacks in a sample of 10,845 people journals.sagepub.com/doi/10.1177/...

Valentin Amrhein @vamrhein.bsky.social · 10mo ago

Excellent new editorial and guideline on interpreting p values and interval estimates bjsm.bmj.com/content/earl...

Interpreting p values and interval estimates based on practical relevance: guidance for the sports medicine clinician

Statistical methods are employed in medical research to estimate effects of treatments or health conditions across populations.1 2 This paper presents a framework to avoid common misinterpretations th...

bjsm.bmj.com

John V. Kane @uptonorwell.bsky.social · 11mo ago

Are you or one of your students considering doing a Ph.D. in a social science? I've spent a lot of time talking about this w/ students & finally wrote something up. IMO, there are only 3 good reasons to do it. One of them needs to be true--otherwise, don't. medium.com/the-quantast...

The Only Three Reasons to Do a Ph.D. in the Social Sciences

If none are true, don’t do it.

medium.com

Noah Greifer @noahgreifer.bsky.social · last yr.

A nice recent article on why you should abandon hazard ratios. #statssky #episky

How hazard ratios can mislead and why it matters in practice - European Journal of Epidemiology

Hazard ratios are routinely reported as effect measures in clinical trials and observational studies. However, many methodological works have raised concerns about the interpretation of hazard ratios ...

doi.org

Coalition for Evidence-Based Policy @coalition4evidence.bsky.social · last yr.

See our No-Spin report on a widely-covered NBER study of Medicaid expansion. In brief: Despite the abstract's claims that expansion reduced adult mortality 2.5%, the study found much smaller effects that fell short of statistical significance in its main preregistered analysis.🧵

Noah Greifer @noahgreifer.bsky.social · last yr.

Starting to look like I might not be able to work at Harvard anymore due to recent funding cuts. If you know of any open statistical consulting positions that support remote work or are NYC-based, please reach out! 😅

Ethan Mollick @emollick.bsky.social · last yr.

Issues with interpreting p-values haunts even AI, which is prone to same biases as human researchers. ChatGPT, Gemini & Claude all fall prey to "dichotomania" - treating p=0.049 & p=0.051 as categorically different, and paying too much attention to significance. www.cambridge.org/core/journal...

Brennan Kahan @brennankahan.bsky.social · last yr.

NEW: CONSORT 2025 now published! Some notable changes: -items on analysis populations, missing data methods, and sensitivity analyses -reporting of non-adherence and concomitant care -reporting of changes to any study methods, not just outcomes -and lots of other things www.bmj.com/content/389/...

CONSORT 2025 explanation and elaboration: updated guideline for reporting randomised trials

Critical appraisal of the quality of randomised trials is possible only if their design, conduct, analysis, and results are completely and accurately reported. Without transparent reporting of the met...

bmj.com

Andrew Vickers @vickersbiostats.bsky.social · last yr.

How to write a response to reviewers. www.sciencedirect.com/science/arti...

How To Write a Response to Reviewers

sciencedirect.com

Winston Lin @linstonwin.bsky.social · last yr.

Very nice explainer by @economictricks.bsky.social www.econometrics.blog/post/why-eco...

Why Econometrics is Confusing Part 1: The Error Term | econometrics.blog

“Suppose that \(Y = \alpha + \beta X + U\).” A sentence like this is bound to come up dozens of times in an introductory econometrics course, but if I had my way it would be stamped out completely.

econometrics.blog

Rafe Meager (they/them)@economeager.bsky.social · last yr.

today we will all read imbens 2021 on statistical significance and p values, which is a strong contender for having the best opening paragraph of any stats paper pubs.aeaweb.org/doi/pdf/10.1...

Carlisle Rainey 👨‍💻📊📚@carlislerainey.bsky.social · last yr.

Here's some older, related stuff (from me) aimed at political scientists. Related paper #1 "Arguing for a Negligible Effect" Journal: onlinelibrary.wiley.... PDF: www.carlislerainey.c...