Is there a book that explains why there aren't better standard techniques than Tukey and ANOVA, for example?
For comparison consider for example I read about the null hypothesis one-sample $t$ test and didn't even bother considering that there could be better tests. But that is probably just a biased belief that there isn't anything more mathematically sophisticated that can be done with the Gaussian to improve on the one-sample $t$ test.
However, Tukey and ANOVA are mathematically more complicated and it didn't feel obvious to me why they should even be considered in the first place. For example in a previous question I asked about why Tukey's method is needed over all pairwise two-sample $t$-tests. The answer I got for that question is that all pairwise two-sample $t$-tests suffers from false positives. But its not intuitive to me how Tukey's method is the best way to evade that problem. How does one intuitively see that generically Tukey and ANOVA are reasonably very good techniques among all possibilities?