I hope this little diversion into design of experiments (DOE) that I’ve explored in my last few columns has helped clarify some things that may have been confusing. Even if you don’t use DOE, there are still some good lessons about understanding the ever-present, insidious, lurking cloud of variation.

Building on my June column, consider another of C. M. Hendrix’s “ways to mess up an experiment”: Insufficient data to average out random errors (aka, a failure to appreciate the toxic effect of variation).

This is where the issue of sample size comes in, and it’s by no means trivial.

How many experiments should I run? It depends.

The ability to detect effects depends on your process’s standard deviation, which in the tar scenario from my May column simulation was +/– 4 (the real process was actually +/– 8).

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