I don't have a source I can link to or share. But cache outliers are a real thing. If you aggregate Resource Timing results, you'll find some surprising outliers in that dataset where transferSize=0 (aka cached load on Chrome). You'll have users with a slow/contended disk where as they might have a fast link, but you'll also have the reverse where you'll have users with a fast cache and a slow network link (high latency, low bandwidth or both).

There's no universal answer here and I feel like the above poster tries to oversimplify a complex problem into one-size-fits-all answers. You'll have different users making up your distribution and you'll have to decide how you weight optimizations. This could very much depend on your product, the expectations and if your user are power users running a complex SaaS frontend, or a news site supporting a range of mobile devices.

A few years ago I traced and notice that Chrome has a pseudo O(n^2) behavior when pulling a bunch of sequential resources from its cache. I reported it but I'm not sure if it got fixed.