Last month we found that capability and performance indexes have no inherent preference for one probability model over another.

Many people have been taught that capability indexes only apply to “normally distributed data.” This article will consider the various components of this idea to shed some light on what has, all too often, been based on superstition.

Walter Shewhart made a distinction between common causes and assignable causes based on the effects they have upon the process outcomes.

Quality-related data collection is useful, but statistics can also deliver misleading and even dysfunctional results when incomplete.

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