The success run theorem is one of the most common statistical rationales for sample sizes used for attribute data.

A simple approach for quantifying measurement error that has been around for over 200 years has recently been packaged as a “Type 1 repeatability study.” This column considers various questions surrounding this technique.

Since 2010, citations for insufficient corrective action and preventive action (CAPA) procedures have been at the top of the list of the most common issues within the U.S.

Chunky data can distort your computations and result in an erroneous interpretation of your data. This column explains the signs of chunky data, outlines the nature of the problem that causes it, and suggests what to do when it occurs.

The keys to effective process behavior charts are rational sampling and rational subgrouping. As implied by the word rational, we must use our knowledge of the context to collect and organize data in a way that answers the interesting questions.

I’m looking at a topic in statistics.

Ever since 1935 people have been trying to fine-tune Walter Shewhart’s simple but sophisticated process behavior chart. One of these embellishments is the use of two-sigma “warning” limits.

As the foundations of modern science were being laid, the need for a model for the uncertainty in a measurement became apparent. Here we look at the development of the theory of measurement error and discover its consequences.

In memory of Al Phadt, Ph.D.

The shape parameters for a probability model are called skewness and kurtosis. While skewness at least sounds like something we might understand, kurtosis simply sounds like jargon.