We have it easy, but do we have it right?

Summary form only given. To evaluate an innovation in computer systems, performance analysts measure execution time or other metrics using one or more standard workloads. The performance analyst may carefully minimize the amount of measurement instrumentation, control the environment in which measurement takes place, and repeat each measurement multiple times. Finally, the performance analyst may use statistical techniques to characterize the data. Unfortunately, even with such a responsible approach, the collected data may be misleading due to measurement bias and observer effect. Measurement bias occurs when the experimental setup inadvertently favors a particular outcome. Observer effect occurs if data collection alters the behavior of the system being measured. This talk demonstrates that observer effect and measurement bias are (i) large enough to mislead performance analysts; and (ii) common enough that they cannot be ignored. While these phenomenon are well known to the natural and social sciences this talk will demonstrate that research in computer systems typically does not take adequate measures to guard against measurement bias and observer effect.