A colleague of mine made an interesting point about how we teach and learn experimental design techniques, and I thought I'd explore the subject further. He observed that the order that we teach statistics is almost exactly opposite of how one would actually use them. So this month I will describe the different tools and how they would be used in the actual order you might use them.

Let’s say that we have defined the opportunity, figured out or validated a way to measure the current state, and have come up with a list of potential sources in the analyze the causes step. At this point, you are potentially faced with a large number of variables that might influence the process and cause the problem.

Now, let’s say that the process is fairly mature, and the problem is a tough one and has been viewed as unsolvable. In this case, all the process experts usually know the answer, but none of them agree with each other. In my experience, this means that you are dealing with one or more interactions between the process variables. These are almost impossible for someone just running the process to catch; to them it appears that for no apparent reason things go OK for a while and then go bad. But the real question is, “Which of the gazillion variables are interacting to cause the problem?”

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