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.<p>Geography apparently has Kriging:<p><a href="https://en.wikipedia.org/wiki/Kriging" rel="nofollow">https://en.wikipedia.org/wiki/Kriging</a><p>Economists have LOESS (okay, used beyond economics, i admit it):<p><a href="https://en.wikipedia.org/wiki/Local_regression" rel="nofollow">https://en.wikipedia.org/wiki/Local_regression</a><p>Maybe geographers refuse to use LOESS because to them, that's a boring rock?<p>Astronomers have sophisticated deconvolution algorithms that are completely unrelated to the ones microscopists use, etc.
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.
My favourite "application" of this law is Kriging:<p><a href="https://en.wikipedia.org/wiki/Kriging" rel="nofollow">https://en.wikipedia.org/wiki/Kriging</a><p>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": <a href="https://www.gwern.net/Correlation" rel="nofollow">https://www.gwern.net/Correlation</a>
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.