I use both depending on need. Histogram + mandatory rugs plot below for quick exploration of the distributions, as my mind finds it easier. For the assessment of "subtle issues" in assumptions - QQ. Recently I'm trying more the empirical vs. theoretical CDFs plots to see how well quantiles overlap.

Far too often hist. fooled me in small data (clinical trials) when assessing assumptions, missing "holes", kurtosis and other issues. Different binning rules (FD, Sturges) only worsened it. The grey region is the pointwise CI for the theoretical N. (here as an example; any necessary can be used).

Yes, that's essential for the proper interpretation. Thank you for making this comment for our readers!

Thank you very much for our kind support! I thought about #statistics or #stats but I was running out of characters. I've never used Twitter/X before (only LinkedIn) and I need to learn how to fit thoughts in 300 characters 😂

PS: I thought you might find this orange book interesting. It was a big eye-opener to me years ago. The cover in the figure from my post on the LinkedIn, where I have a series on the rank-based methods, like MWW, KW (both special cases of ordinal logistic regression), Brunner-Munzel, ATS/WTS, ART.

Testing for stochastic superiority means that they will be sensitive to anything that "induces" the superiority. Effectively it translates also to dispersions (only in some patterns, as you suggested) and shapes. But far too many people (ab)use them for comparing medians naively (only holds for IID)

I'm totally and happily! surprised. I thought I'm the only one in the whole Universe who's been doing
𝗱𝗮𝘁𝗮 %>%
𝗰𝗵𝗮𝗶𝗻_𝗼𝗳_𝗰𝗮𝗹𝗹𝘀() %>%
𝗺𝗼𝗿𝗲_𝗰𝗮𝗹𝗹𝘀() -> 𝗿𝗲𝘀𝘂𝗹𝘁
for over a decade. Yet most of the time, reactions of those who saw it varied between "you weirdo" and 🤮. Thanks for making my day!

Mee too, since I found this quote. PS: but I'm guilty of another crime - for more than decade I've been using the right arrow with pipes 😅 (data %>% chain_of_verbs %>% more_verbs -> result).

A very important remark! This is essentially the idea of combining statistical discernibility and practical relevance, like in non-inferiority, equivalence and superiority (aka "minimum meaningful magnitude") studies (common in #clinicaltrials)

This package is so underrated, while exposing a rich collection of awesome "hacks"! For example, I can control "breaks" for Y axis in each panel independently. Multiple colour scales, point-paths, advanced guides + axis features (eg nesting), stat_theodensity() working with faceting and many more!

So good to see you here, Justin!