[spekcular]

I admit I'm not familiar with the model used to aggregate the boson data. But there's an entire community of nonparametric/semiparametric statisticians that works on problems just like this. It seems crazy to me that that millions of dollars are spent to build the machines to collect this data, yet the papers are written using statistical models with distributional/independence assumptions known to be false. (The tweet linked above seems to be saying something similar.)

Is there a concrete reason we can't be naive and just bootstrap confidence intervals for example? Of course I defer to the physicists here – but I'm curious whether there's some simple high-level reason the usual tricks don't work.

[nabla9]

Don't worry. High energy physics has been at the bleeding edge of statistical methods forever.

[spekcular]

Sure, but I'm not talking about high energy physics in general, I'm talking about the estimates in this specific paper. Which the guy who is doing bleeding edge stuff seems a little suspicious of (see tweet).

See also: https://twitter.com/pietrovischia/status/1512174848558219270

This does not seems like bleeding edge, it seems like "Gaussian approximations for everything."

In fact, this is the reference for the technique they used: https://cds.cern.ch/record/183996/files/OUNP-88-05.pdf. (Although the criticism seems to be leveled at the way they estimate the correlations, not that linear estimator specifically?)