Earlier quoted context omitted.
Null hypothesis significance testing. It is a "method" of statistics that resulted from confusion between Ronald Fisher's significance testing with Jerzy Neyman and Egon Pearson's hypothesis testing sometime in the 1940s. None of those three are free of blame either. While there is all sorts of things wrong with NHST, the primary thing is that researchers never actually test their own hypothesis. Instead they test a…
>Instead they test a "null" hypothesis that acts as a strawman, usually that some parameter equals exactly zero How is "there is no difference between my groups/conditions" a strawman? This is by far the most common H0.
For nearly all use cases many plausible models/theories/ideas will predict "not exactly zero difference", so rejection of the hypothesis "difference = zero" is useless to the scientist. Mathematicians apparently don't get this, which is what inspired the Fisher paper I linked to.
And yes, it is the most common H0. No argument there. The problem is so widespread and destructive that the mind instinctively rejects its existence as unbelievable.