What I'm saying is that it's not error. I'm reporting a distribution that the values may take on. Ideally I would show all of the data, but that's not possible

What I'm saying is that it's not error. I'm reporting a distribution that the values may take on. Ideally I would show all of the data, but that's not possible given both presentation and human cognitive limits, so we characterize that distribution with two dimensions: a mean (the "middle") and the standard deviation (how much it "spreads" from that middle). This is, I think, a philosophical difference from, say, measuring the charge of an electron. The electron has a charge. The mean of independent measurements is, we hope, very close to that true value. Deviations from that mean are indeed error. But when measuring performance in a computer system, all observable values are valid. We're trying to figure out not what the "true" value is (there isn't one), but what the range of values your performance can take on, and where in that range you're likely to fall. (I almost wrote something about excusing "real" errors such as memory corruption, disk failures and segfaults, but sometimes you want to include that! If you're doing performance analysis of a distributed system that scales to hundreds of thousands of compute nodes, real runs of an application are likely to encounter such things, so your system better be resilient to them, and they will have an impact on real performance.)