This is not an answer and might be better thought of as a comment, but I'm sure that it will get too long.
I know from personal experience that researchers often know a great deal about the data generating systems on which they work before they gather the final data that are subject to statistical analysis in support of their inferences. Much more than can be gleaned from those final datasets alone by even the most sophisticated statistician. Choosing the statistical analysis on the basis of that prior knowledge will not affect the error rate properties of any subsequent testing, will it?
In experimental pharmacology there are often several times as many preliminary datasets than final as the project is developed and a biological system is explored prior to the final experimental design being formalised. In many cases far, far more given that research laboratories will often work with particular biological systems for many years on end. Those pre-existing datasets and experience would usually allow the researchers to know with some confidence the distributional form of the final data before it is gathered. Furthermore, the researchers will usually know how reasonable it might be to assume iid directly from the experimental design.
I do not often see answers that suggest that prior information be used in the choice of test procedure (or even for thinking about what inferences might be drawn) and so I suspect that the papers being suggested here equivalently ignore the possibility of using that preliminary information (researchers' knowledge). I may be mistaken in that, particularly as I have read only one relevant paper in detail (Zimmerman 2004, cited by Frans Rodenberg) and skimmed another (Shamsudheen, Iqbal, and Christian Hennig. 2023), so please correct me if I'm wrong.