Have you ever been responsible for a data collection where any resemblance between what you designed and what you got back was purely coincidental? When that happens, yet again, I say to myself, “Well, it was perfectly clear to me what I meant.”

Consider the use of statistics as a data process, or rather, four processes: measurement, collection, analysis, and interpretation. Each of these has six sources of process inputs: people, methods, machines, materials, measurements, and environments. Each also has an inherent “quality” associated with it and is subject to the influences of outside variation to compromise this quality. Unless the variation in these processes is minimized, there is a danger of reacting to the variation in the data process instead of the process you are trying to understand and improve.

What is the biggest danger? Human variation, which includes our perception of the variation (“measurement”) and our executing the “methods” of measurement, collection, analysis, and interpretation.

In that context, let’s consider each of these four data processes:

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