Perhaps a possible counterpoint is to argue that fancy stats knowledge is not really necessary for biologists. The reason they have to do it in their papers may be more due to bureaucracy / cargo cult / numerical theater etc. Look at the scatterplot etc. If the details of the fancy evaluation make or break the discovery, then it's probably a fluke anyway. Yeah, most papers are uninteresting and are purely done for funding and CV reasons. Those who pay expect papers, but aren't really sure why. Certainly not to read them themselves.
> Unless we're going to start saying biology isn't real.
It probably helps to recognize that statisticians caught one of the first modern biology researchers fudging his numbers: https://en.wikipedia.org/wiki/Gregor_Mendel#Mendelian_parado...
Except they didn't; they alleged that the numbers had been fudged on rather scant evidence.
As I understand it, Fisher had two complaints. First, that the trifactorial data was just wildly too accurate (using a chi-squared approach). This seems to be resolved in the scientific community as an off-by-one error: if instead of 10 seeds, Gregor had used 11 on occasion, that's enough to resolve the issue.
The other issue is the chi-squared analysis of Mendel's experiments in aggregate, which when pooled also too neatly fit the pattern he wanted to illustrate. And as best I know, nobody has adaquately disputed that analysis.
So we've got a serious problem with how science is being done. Probably doesn't affect whether or not expertise exists, or which fields it exists in. Unless we're going to start saying biology isn't real.