In my experience people think/hope that the minimum tells them the performance of their program when all noise is removed. However, this is based on the assumption that programs have a single performance point which, as the Stabilizer paper shows, is rarely the case on modern systems. Rather, what the minimum tells you is the behaviour of your program at one given point in the non-deterministic performance space. This might seem like a minor, perhaps even a pedantic, difference, but I have found it to be a profound observation.

Imagine, for example, your program has 100 possible ASLR states: in 99 of those states it takes 1s to run and in the 1 remaining state it takes 0.8s to run. Using the minimum, you will think your program takes 0.8s to run even though there's only a 1% of chance of observing that performance in practise. That's bad, IMHO, but the problem compounds when you compare different versions of the same program. Imagine that I optimise the program such that in those 99 slow states the program now takes 0.9s to run, but in the remaining state it still takes 0.8s. The minimum will tell me that my optimisation made no difference (and thus should probably be removed) even though in 99% of cases I've improved performance by 10%.