Wanted: Statistical success stories

Bill Harris writes:

Sometime when you get a free moment, it might be great to publish a post that links to good, current exemplars of analyses. There’s a current discussion about RCTs on a program evaluation mailing list I monitor. I posted links to your power=0.06 post and your Type S and Type M post, but some still seem to think RCTs are the foundation. I can say “Read one of your books” or “Read this or that book,” or I could say “Peruse your blog for the last, oh, eight-ten years,” but either one requires a bunch of dedication. I could say “Read some Stan examples,” but those seem mostly focused on teaching Stan. Some published examples use priors you no longer recommend, as I recall. I think I’ve noticed a few models with writeups on your blog that really did begin to show how one can put together a useful analysis without getting into NHST and RCTs, but I don’t recall where they are.

Relatedly, Ramiro Barrantes-Reynolds writes:

I would be very interested in seeing more in your blog about research that does a good job in the areas that are most troublesome for you: measurement, noise, forking paths, etc; or that addresses those aspects so as to make better inferences. I think after reading your blog I know what to look for to see when some investigator (or myself) is chasing noise (i.e. I have a sense of what NOT to do), but I am missing good examples to follow in order to do better research – I would consider myself a beginning statistician so examples of research that is well done and addresses the issues of forking paths, measurement, etc help. I think blog posts and the discussion that arises would be beneficial to the community.

So, two related questions. The first one’s about causal inference beyond simple analyses of randomized trials; the second is about examples of good measurement and inference in the context of forking paths.

My quick answer is that, yes, we do have examples in our books, and it doesn’t involve that much dedication to order them and take a look at the examples. I also have a bunch of examples here and here.

More specifically:

Causal inference without a randomized trial: Millennium villages, incumbency advantage (and again)

Measurement: penumbras, assays

Forking paths: Millennium villages, polarization

I guess the other suggestion is that we post on high-quality new work so we can all discuss, not just what makes bad work bad, but also what makes good work good. That makes sense. To start with, you should start pointing me to some good stuff to post on.