Yeah; there are a bunch of other variables I'd love to see thrown into a broader multivariate analysis, if somebody was going to really dig into that topic in

Yeah; there are a bunch of other variables I'd love to see thrown into a broader multivariate analysis, if somebody was going to really dig into that topic in a real (i.e.: more than 2 hours) way. "Diffusion of Responsibility" provides a rational, well-understood mechanism by which setting more reviewers might result in longer review times, as the post suggests. But we're still talking about relatively small numbers of people here; who knows how strong the DoR effect is in this case, relative to other variables, such as the complexity of the PR? My intuition is that number of reviewers is more likely to turn out to be a multiplier on other factors affecting code review duration; that having lots of reviewers on a tiny trivial change might make the review go even faster (since it's easy, and more eyes means it might be spotted earlier), whereas having lots of reviewers on a massive complicated change might slow it way down (since everyone puts it off, hoping that someone else will handle it). It would be really interesting to crank up a big statistical analysis with lots of extra columns of data, to see the strength and amount of correlations between other factors. My money is on "bigger changes lead to longer code reviews and also independently lead to more reviewers being set on the pull request". Which.. would be kind of a boring result which everyone already intuitively knew, admittedly. But at least then we'd know for sure. Or at least, for p<0.05 sure.