Data distribution, explained.

In my December column I listed three items to watch out for when evaluating capability study results: Cp versus Pp, the distribution of data, and sample size. I hopefully cast some light on the differences between the two measures of capability, Cp and Pp.

In this column I will dive deep into the distribution of data. The thing to remember is the standard capability study assumes the data are normally distributed. This assumption of normality, while not so critical in other statistical tools, is very important in capability studies.

Cp and Pp give us predictions based on a sample of how our population will behave in the far tails of the normal curve. These measures use mean and standard deviation to create a normal distribution, and, from this, predict how many of our parts, over the entire population of parts, will fall outside the tolerance limits.

APQP techniques for identifying and eliminating bottlenecks.

APQP techniques for identifying and eliminating bottlenecks.

When you ask for Cp (or Cpk), are you really getting Pp (or Ppk)?

OK, I admit it: I was trying to be funny in the title. But the issue is how capable is a capability study? Or, to state it another way: When should we be careful in how much we trust our capability study results?

Here are three items we should be aware of when designing, running and calculating a capability study: