Many different approaches to process improvement are on offer today. An appreciation of the way each approach works is crucial to selecting an approach that will be effective.

As I started reading The Book of Why: The New Science of Cause and Effect, by Judea Pearl and

Students are told that they need to check their data for normality before doing virtually any data analysis. And today’s software encourages this by automatically providing normal probability plots and lack-of-fit statistics as part of the output.

Acceptance sampling uses the observed properties of a sample drawn from a lot or batch to make a decision about whether to accept or reject that lot or batch.

As municipalities clamor for a slice of President Biden’s $1.2 trillion infrastructure spending bill, one Johns Hopkins scientist is re-examining one of the basic elements of road-building: Determining the width of road lanes.

In 2010, new to the world of statistical process control (SPC), I was intrigued by Don Wheeler’s statement that “No data have meaning apart from their context” (from his book, Und

All too often the topic of fixing dirty data is neglected in the plethora of online media covering artificial intelligence (AI), data science, and analytics. This is wrong for many reasons.

Last month we looked at analyzing observational data. Here we will consider experimental data and discover a weakness in the way they are obtained that can contribute to

Most of the world’s data are obtained as byproducts of operations. These observational data track what happens over time and have a structure that requires a different approach to analysis than that used for experimental data.