In one recent online forum, a Six Sigma Black Belt asked a question about validating samples—how to ensure that when they are taken, they would reflect (i.e., represent) the population parameter. His purpose: to understand the baseline for a project. He said he had six months of data regarding cycle times for handling maintenance tickets.

Among the early suggestions to the query were an assortment of options, including cross validation using regression (comparing randomly selected subsets) and t-tests on small samples. Another person suggested testing the data for normality: “Then any sampling technique will do,” he claimed.

Someone suggested plotting the data on an XmR or XbarR chart. Someone else suggested simply taking the average and then using process maps and lean techniques to reduce the cycle time. This person asserted that, “Random sampling is all that is needed to have a representative sample—by definition.” He went on to suggest that stability doesn’t matter; with six months of data, you can just number the tickets from 1 to k, and use a random number generator to select a sample. His justification? Classical statistical texts don’t require you to check for stability before taking a random sample.

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