Does your use of probabilities confuse your audience? Sometimes even using numbers can be misleading.

There are four major questions in statistics. These can be listed under the headings of description, probability, inference, and homogeneity. An appreciation of the relationships between these four areas is essential for successful data analysis.

The cumulative sum (or Cusum) technique is occasionally offered as an alternative to process behavior charts, even though they have completely different objectives. Process behavior charts characterize whether a process has been operated predictably.

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.