It is so annoying how people in my field persistently seem to think that non-parametric tests are a solution to low cell sizes in studies. I still read this in papers to this day, and even see it in student tests (not as answers, but as part of the test items).
Let's be clear: They are not. They can fill in for missing distributional alignment (mostly residual non-normality). Bootstrapping will also not save your tiny cell sizes, even if you do it.
If you know your cell size is bad, just use the parametric tests (if this is all you know otherwise) and DEFINITELY report your barn-door sized CIs, or incorporate sensible priors and use Bayes (which is what you should be doing in the first place, IMHO).
To me, this is rejection-worthy stuff. It's 2026. Science has progressed past "I wasn't taught that in school". Do better.

