Unfortunately, the computational time would explode if the ML method's optimization is performed naively. Precise measurements of the statistical significance would crowd out researchers except for Big Tech.
Holm-Šidák would be better--but something like false discovery rate might be easier to interpret.
I interpret your criticism to mean that ML folks tend to re-use a test set multiple times without worrying that doing so reduces the meaning of the results. If that's what you mean, then I do agree.
Informally, some researchers are aware of this and aim to use a separate validation data set for all parameter tuning, and would like to use a held out test set as few times as possible — ideally just once. But it gets more complicated than that because, for example, different subsets of the data may not really be independent samples from the run-time distribution (example: data points = medical data about patients who lived or died, but only from three hospitals; the model can learn about different success rates per hospital successfully but it would not generalize to other hospitals). In other words, there are a lot of subtle ways in which a held out test set can result in overconfidence, and I always like to learn of better ways to resist that overconfidence.
A concrete improvement would be only being allowed to change 1 thing at a time per paper, and measure the impact of changing that one thing. But then you couldn't realistically publish anything outside of megacorps. Another solution might be banning corporate papers, or at least making a separate track...from reviewing papers, it seems like single authors or small teams in academia need to compete with Google where multiple teams might share aspects of a project, one doing the architecture, the other a new training algorithm etc...which won't be disclosed, you'll just read a paper where for some reason a novel architecture is introduced using a baseline which is a bit exotic but also used in another paper that came out close to this one, and a regulariser which was introduced just before that ...
If you limit the pools, you can put much higher standards on experiments on corporate where you have the budget, while giving academia more points for novelty and creativity
I mean, it's nice to know that the p-values of coefficients on models you are submitting for publication are appropriately reported under the conservative approach Bonferroni applies, but I would think making it a _default_ is an inappropriate forcing function when the performance on holdout is more appropriate. Data leakage would be a much, much larger concern IMHO. Variance of the performance metrics is also important.
What am I missing?
I should also note 1) this is about testing whether the performance of a model is meaningfully different from another, not the coefficient of the models 2) I don't reject papers just because they lack this, or if they fail to achieve a statistical significance, I just want it in the paper so the reader can use that to judge (and it also helps suss out cherry picked results)
t-test with Bonferroni isn't necessarily the best test for all metrics either.
You (probably) don't care about test-set performance per se but instead want to be able to claim that one model works better _in general_ than another. For that, you need to bust out the tools of statistical inference.
This isn't always true (of course, especially in aggregate series over time) and of course statistical measures are used to report model performance. But a Bonferroni correction struck me as a weird place to apply this specifically, but after the other comments from yesterday I saw where they were taking it.