Indeed, although Q-Q plots are very unlikely to be understood by people who don't have a good grasp of statistics, whereas a misleading histogram will be (and probably without knowledge of the caveats behind histograms).
To the credit of the shadier individuals in my profession, this histogram subtlety nicely highlights how it can be quite easy to bend the data to your argument using ad-hoc procedures (KDEs, hists, QQs, boxplots). A carefully chosen bin width, smoothing parameter, or covariate can present a different view of the data than some other parameter/covariate. That's why it's nice to have other statisticians capable of reproducing and disseminating the work.