The future is bleak: AI "researchers" will send questionaires to AI "respondents" to collect data on what they "think" about Topic, poorly summarise the results and generate a paper for a mill. AI peers will review and leave comments. Other AI will cite the paper (and nonexistent ones). Wooooo.

New meta-analysis of effects of 20mph speed limit in the UK shows 𝙣𝙤 statistically significant impact on killed or serious injured (KSI) crashes or injuries on sign-only schemes. t.co/WSvmnbLJ1n

I think it was a target trial emulation of difference in differences for a propensity score matched analysis with Bayesian borrowing of historical controls of digital twins based on synthetic data from the real world.

Once you truly understand causal inference theory, it's very hard to win an honest applied grant because: * Many questions can't be answered * Almost everything that sounds big and exciting is snake oil * Most reviewers won't understand * you're competing with the bandwagon-jumping cynics

More on “Simple method works equally well…” and do you really need SuperLearner? drwangstatsconsulting.wordpress.com/2025/06/17/m...

Half the data science influencers i followed in 2019 are now LLM/AI influencers, which leads me to believe late 2010s data science scene was all hype

New blog post ““Zombie statistics” and strivers” drwangstatsconsulting.wordpress.com/2025/05/19/z...

7 habits that make you look like a professor.

If the goal is to describe or to explain, true high colinearity between important covariates may be a fact of life and difficult to overcome. Quick-fix solutions may result in bad confounding corrections. Bottom-line: quick fix solutions like PCA are often a poor man's solutions to colinearity

I get the impression that polisci is now pretty sold on LPM over logit. What's the go to citation making the case for this?