oh that is slick!

I know it’s often not identifiable and challenging to fit but I get very nervous about the exclusion of the time|id random slope in these models based on the 2013 Barr paper.

Oh you know I assumed you were plotting the RE estimates like this. If its just the observed data, probably min/max if few estimates/group and Q3/Q1 if many. You could probably even do tiny box plots if you didn't have too many groups.

I think to some extent the knee jerk reaction against the strong claim in the paper is due to the muddiness that (unfortunately) exists between prediction and causal claims. "Who is most as risk" as you state vs. why.

If they were bars it would be a caterpillar plot right? What about blupergram. Has a nice ring to it

Pretty sure this is one of those sexy offers two very smart podcasters told me to run away from. So I’m going to say maybe 😂

Love it! Will you be sharing data? (You know… for those of us teaching stats to CSD PhD students struggling to find cool and salient datasets)

In all fairness, glmer does spit out a warning about non integers.

Anything can be an integer with round(x, 0)!

I’m very curious about what the third one is doing. Modeling a proportion without weighting by the number of trials? Could this could be useful if the proportion is not built out of independent Bernoulli trials?

So I should just ask students to explain each meme for their stats midterm right?

fantastic! Straight into the reading list for graduate stats. One thing that might be useful is a conceptual paragraph about how statistical power/sample size estimation changes. I can imagine (enthusiastic) students stuck on how to adjust what they know about study planning.

Oooh and what does the AI boilerplate section look like?

This is such a thorough study - what a great example of quality science putting an existing paradigm up to a rigorous test. A-priori power analysis. Preregistered. Open data and code. You can tell the authors made sure to anticipate every “but what if…” response to the null evidence.

Sounds a little viffy

This is awesome! In a weird coincidence I created something similar a few weeks (github.com/rbcavanaugh/..., mostly with Claude because I’m course prepping 2.5 courses and have enough to do already). I’m glad someone has created something with a bit more intention.

And where might be put multicolinearity on this figure? 🛋️🍿