Just to chip in, I'm strongly in favour of stats questions being fine given a biological context because they'll be a bioinformatics solution somewhere.
Modern bioinformatics appears to have become removed from its "statistically pure" origins but it moves cycles. At present many think (perhaps correctly at present) the volume of genomics data renders "the statistical model" irrelevant, but things change. When "the statistical model" is king, the reality is the method was shoe-horned from another area of stats/maths. The "shoe-horning" process is quite important, because your trying to place a generic solution into a biological context and the translation isn't always neat.
For example, we just had a question asking whether "clustering analysis" can replace molecular phylogeny ... the answer is no and this question was asked about 40 years ago. However, a general statistician wouldn't be aware of the biological rational as to why, nor aware of the huge row broke out in evolution about "the correct method to build a phylogeny" and took a decade to resolve.
A good example of "statistical methodological cycles" is machine learning (ML), which is currently fashionable, but has been on the go since the '50s. Purists describe "AI winters" (sounds like Game of Thrones, but before GoT). This is where funders pull out because the original PIs overstated their case. Without AI winters its application to bioinformatics would be much more mainstream (possibly). The reality is that many major genomics packages Sauret (whatever its called), don't implement "full-blooded" ML solution, so there's lots of ground for change.
Finally an older but brilliant example (brilliant man) of the importance of empirical statistics specifically applied to bioinformatics was Ron Fisher's F-statistics (e.g. Fst). Fisher was a statistical purist who founded large chunks of modern statistical theory, but also applied that expertise to biological questions (just don't ask about his political views). F-statistics are used in vast numbers of bioinformatics packages and population genomics including R and I've no doubt they'll be a BioPython solution somewhere.
My advice "resist popularism" :-)