Agreed. In fact I haven't checked HN since I posted that and fully expected to be downvoted to all hell for that comment when I logged back in just now. I'm really impressed with myself. Made my week.
Something i find really fun is that every specific field of study has developed its own completely general statistical tools. There's no reason they couldn't be used in other fields. They just aren't.
Kringing is the same thing as gaussian process regression, and astronomers use GPs a fair bit. I'm not sure whether they are used more widely.
My favourite forgotten/isolated statistical method is MCMC. These were first used by nuclear physicists at Los Alamos in the 40s/50s, but weren't really recognized more widely until the 80s. This is probably partly because only people working on bombs had access to the computing power before then, but still.
Astronomy is also an interesting case because this law doesn't necessarily hold true. The power spectrum, or Fourier transform of the autocorrelation function, isn't monotonic for matter on large scales. For example, baryonic acoustic oscillations at early times in the Universe get "imprinted" into the cosmic structure, such that galaxies have a preferential separation between each other.
How is this not just common sense? Not trying to sound cynical... I don't have background in this space. Honestly trying to understand why "things that are close together and more related than things farther apart" is considered so profound.
Has allowed me to make some pretty awesome spatial interpolations of water surveying and wifi quality data using unmanned robots, especially given the massive isotropic bias of the collection method that makes many other methods nonviable.
Also known in a number of other fields like psychology or sociology, under some catchier rubrics like "everything is correlated": https://www.gwern.net/Correlation
I wish inverse distance scaling worked better in other domains - e.g. weighted nearest neighbor estimators seem to not generally perform better than unweighted ones.
