I have (almost exclusively in the finance space). I found it rejecting too many null hypotheses to work in practice (e.g. choosing a distribution that you coul

I have (almost exclusively in the finance space). I found it rejecting too many null hypotheses to work in practice (e.g. choosing a distribution that you could generate reasonable simulations with). For choosing distributions, we ended up plotting observations versus various simulated realities and going with the "eye test". Now, of course, there will be those out that that say this is not rigorous enough, but for most phenomena in the social sciences its very hard to find a distribution that fits reality per the K-S test. So you're left with either bootstrapping (which has its own flaws) a distribution or choosing one that's good enough (e.g. passing the eye test).