Measurement systems analysis (MSA) for attributes, or attribute agreement analysis, is a lot like eating broccoli or Brussels sprouts. We must often do things we don't like because they are necessary or good for us.

To date, this series focused on relatively simple data analyses, such as learning one summary statistic about our data at a time.

For more than 40 years it has been common to use the precision to tolerance ratio (P/T ratio) to compare the standard deviation of measurement error with the specified tolerance for a particular product.

In our last article, we discussed how to determine how many people drink pumpkin spice lattes in a given time period without learning t

In my article, “Tightened 100% Inspection” (Quality Digest, March 29, 2021), we found that the excess costs associated with tightened specification limits a

How many people drink pumpkin spice lattes in October, and how would you calculate this without learning specifically who is drinking them, and who is not?

Inspection sounds simple. Screen out the bad stuff and ship the good stuff. However, measurement error will always create problems of misclassification where good stuff is rejected, and bad stuff gets shipped.

A quick Google search returns many instances of the saying, “A man with a watch knows what time it is.

Traditional statistical methods for computing the process performance index (Ppk) and control limits for process-control purposes assume that measurements are available for all items or parts.

There is a type of error that occurs when conducting statistical testing: to work very hard to correctly answer the wrong question. This error occurs during the formation of the experiment.