More thoughts about the normal prior vs the student's t prior dpananos.github.io/posts/2026-0...

Adaptive Shrinkage and the Student’s t Prior – Demetri Pananos Ph.D

dpananos.github.io

Right! Our efforts were in fact guided by a model of brain size [1], which was originally motivated by primates, but also makes predictions for an asocial path to big brains that seems to characterise the cephs [2] [1] journals.plos.org/ploscompbiol... [2] inference-review.com/letter/the-e...

The Evolution of Big Brains | The Evolution of Big Brains | Inference

The cultural brain hypothesis predicts two main paths to intelligence and large brains in animals: a social learning path taken by humans at one end, and an asocial learning path taken by cephalopods ...

inference-review.com

Proposal and sample chapters submitted to publisher and sent out for peer review 📚 I'm also still open for suggestions and perspectives to make this maximally useful, so let me hear all the good ideas! ✨

Theiss Bendixen@theissbendixen.bsky.social · 3mo ago

Imagine a short book (~200 p.) introducing Bayesian statistics in the context of clinical trials and drug development. Scope would be introductory -- sort of "your first short course on Bayes". But practical enough to be applied out of the box. What would you like to see covered in such a text? 👇

This arrived just in time for Danish summer weather (rain). A few chapters in and it does not dissappoint! Remarkable mix of theory and practice, so many good points to absorb -- hope it's widely read. Thanks @statmodeling.bsky.social, @avehtari.bsky.social, @rmcelreath.bsky.social, et al.!

New research! 📚 We built the largest database to date of cephalopod species - octopuses, squids and cuttlefish - and their brains, habitats and behaviors 🐙🧠📈 What did we find?👇 Press release: www.lse.ac.uk/news/ecologi... Paper: www.sciencedirect.com/science/arti...

New research! 📚 We built the largest database to date of cephalopod species - octopuses, squids and cuttlefish - and their brains, habitats and behaviors 🐙🧠📈 What did we find?👇 Press release: www.lse.ac.uk/news/ecologi... Paper: www.sciencedirect.com/science/arti...

New interactive blog! "Why Adjusted Regression Coefficients Are Less Descriptive Than They Look" rpsychologist.com/descriptive-...

Brilliant read! I'd add that several of the big COVID trials took an explicit Bayesian approach, which facilitates interim looks at the data and allows a trial to stop if the treatment is clearly working (or not). @statberry.bsky.social gives a readable overview here: www.mdpi.com/2077-0383/14...

Saloni@scientificdiscovery.dev · last mo.

New post! Most people didn’t think it was possible to develop Covid vaccines fast enough to be useful. In the summer of 2020, I came to a different conclusion. Now I wanted to look back on how it happened, and suggest boldly that we should ask whether that speed should've been the norm all along.

Thanks so much for spotlighting "The Data Analyst's Guide to Cause and Effect"! 📚🙌

“The Data Analyst’s Guide to Cause and Effect” statmodeling.stat.columbia.edu/2026/06/10/t...

It's alive! 🎉 𝗧𝗵𝗲 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁'𝘀 𝗚𝘂𝗶𝗱𝗲 𝘁𝗼 𝗖𝗮𝘂𝘀𝗲 𝗮𝗻𝗱 𝗘𝗳𝗳𝗲𝗰𝘁 is out -- an introduction to causal inference in practice. The first two chapters are available for free here: theissbendixen.com/dag-book/ More below 👇

Instead, we cut to the chase and emphasize a practical workflow using step-by-step explanations and real data examples in R. The companion website lives here theissbendixen.com/dag-book and holds: - All data and code used in the book - Free sample chapters - Bonus material!

It took us three years to write this thing. But the good news is you can read it in three days! We cover fairly advanced methods -- counterfactuals, g-computation, inverse probability of treatment weighting, poststratification, missing data imputation, etc. -- without dense formal notation.

"Strongly application-focused... an effective tool for getting data analysts into the world of causal inference and immediately into a workable project." -- Nick Huntington-Klein, @nickchk.com

"An excellent, comprehensive, yet accessible introduction to causal inference... an invaluable guide for analysts seeking to move beyond mere correlation." -- Julia Rohrer, @dingdingpeng.the100.ci

First, we're very lucky that some very impressive people have already said some very nice words about the book! "A clear and readable book with broad coverage of many ideas and methods in causal inference." -- Andrew Gelman, @statmodeling.bsky.social

It's alive! 🎉 𝗧𝗵𝗲 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁'𝘀 𝗚𝘂𝗶𝗱𝗲 𝘁𝗼 𝗖𝗮𝘂𝘀𝗲 𝗮𝗻𝗱 𝗘𝗳𝗳𝗲𝗰𝘁 is out -- an introduction to causal inference in practice. The first two chapters are available for free here: theissbendixen.com/dag-book/ More below 👇

New blog post! 🚨 "From Bucher to Bayes: A Brief Introduction to Bayesian Model-Based Network Meta-Analysis for Indirect Treatment Comparisons using R" theissbendixen.com/mbnma/

New blog post! 🚨 "From Bucher to Bayes: A Brief Introduction to Bayesian Model-Based Network Meta-Analysis for Indirect Treatment Comparisons using R" theissbendixen.com/mbnma/

Nice! Similar phenomenon to assurance (or marginal/average power), where a prior is placed over the effect and power is integrated over it to account for uncertainty, rather than conditioning on a single point estimate. Assurance is also always lower than power in practice.

Dr Mircea Zloteanu 🌺🌞🍃@mzloteanu.bsky.social · 2mo ago

I made a #Shiny app about post hoc power and effect-size uncertainty. If you accept an observed effect, you also need to accept its uncertainty. The app shows how power wobbles when you consider the CI, not just the estimate. Embrace the #wobble #samplesize open.substack.com/pub/mzlotean...

Here's my current tentative and very much in progress outline. Comments of all kind much appreciated!

Imagine a short book (~200 p.) introducing Bayesian statistics in the context of clinical trials and drug development. Scope would be introductory -- sort of "your first short course on Bayes". But practical enough to be applied out of the box. What would you like to see covered in such a text? 👇

Yes, very cool! It's a class of methods often referred to as "Bayesian dynamic borrowing," and it's not well-known outside the clinical trial literature (and even there it's not very common). I give a brief intro to one particular approach here: theissbendixen.com/bayesian-dyn...

In the Fall I'll be teaching a new MA-level methods course entitled "Applied Statistical Evaluation of Development Projects". It will be 12 weeks, in R, and aimed around RCT evaluations. This is a draft outline. What am I missing? What seems redundant?

🚨 Blog post: When Using OLS Hurts 😩 I replicated a high-profile study on racial bias in tenure decisions and show the authors weakened their own findings by using OLS instead of ordered beta regression 🤯. Use ordered beta and live your best life 👍 #rstats www.robertkubinec.com/post/ord_bet...

When Using OLS Hurts – Homepage

People often use OLS for bounded continuous variables even though we know it isn’t the correct model. Ordered beta regression is a better model–but hard to predict when the results will change. For th...

robertkubinec.com

This gives a flavor of the style: theissbendixen.com/bayesian-dyn... The scope (short, introductory, applied) is also similar to our forthcoming causal inference book: us2.sagepub.com/en-us/nam/th...

Imagine a short book (~200 p.) introducing Bayesian statistics in the context of clinical trials and drug development. Scope would be introductory -- sort of "your first short course on Bayes". But practical enough to be applied out of the box. What would you like to see covered in such a text? 👇

Yeah, Bayes is usually thought of as particularly useful with sparse data (because priors can do some of the work), but I think it's equally true that Bayes is useful when there's a lot of good data on e.g. a drug, because Bayes is very well-suited to exploit all that information in a principled way

Bayes is sometimes used at various stages in drug development. For instance in fancy meta-analysis: dmphillippo.github.io/multinma/ Borrowing in clinical trial analysis (worked example and some literature): theissbendixen.com/bayesian-dyn... Adaptive trials: hbiostat.org/doc/bayes/wh...

The Bayesian Workflow book is coming! statmodeling.stat.columbia.edu/2026/04/16/t...