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In this paper, we use statistical theory to study the choice of sampling unit size with ideal warm-up. We find that most benchmarks exhibit positive intracluster correlation for most metrics. As a result, using larger sampling units is not as effective as using many small sampling units at improving the accuracy. We provide insight into the inherent property of the benchmarks that causes the positive intracluster correlation. Using the microarchitecture independent basic block vector distance we identified a generalized temporal locality that most benchmarks possess. We also observed that the uncommon benchmarks which lack the general code locality are candidates for using larger sampling units for some metrics. This work provides guidance for selecting sampling unit sizes in future research and offers evidence against the trend of simulating one larger and larger chunk of instructions.