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.

The computation for skewness does not fully describe everything that happens as a distribution becomes more skewed. Here we shall use some examples to visualize just what skewness does—and does not—involve.

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.