He's taking a very pragmatic position that you will experience directly if you're ever responsible for the SLA of a "web scale" service. His 5% / 95% observati

He's taking a very pragmatic position that you will experience directly if you're ever responsible for the SLA of a "web scale" service. His 5% / 95% observation is simply that you should not be focusing on the average, or even majority case if that's not what your users actually experience. One interpretation of the specific example you don't like is that more requests fell into a given range, thereby giving a distribution that the graph reflects. Another potential interpretation is just what he describes, where you have a set of especially poor performing requests at that point in time which effectively skew the distribution so that the 95th is "pulled up". His broader point is that you don't know based on this graph what you're dealing with, and he demonstrates a better visualization technique to determine the precise distribution of responses without having to look at every request.