We are all aware that variation is everywhere. It is inevitable in the output of any process – manufacturing, service, or administrative.

We also know it is impossible to keep all factors, in a constant state, all the time. This is one of the key challenges for a quality improvement team. More specifically, to reduce variability.

A Histogram is a graphic summary of variation in a set of data. The pictorial nature of the histogram enables us to see patterns that are difficult to see in a simple table of numbers. Histograms enable quality improvement teams to diagnose problems with a x-ray vision.

At a macro level, there are three important characteristics of a histogram:

  • The centre
  • The width
  • The shape.

These three characteristics also point quality improvement teams to the COPQ resident in a process.

Histograms and Limits of Acceptability

Quality Tool - Histograms - Suresh Lulla

Potential Pitfalls

There are three important pitfalls that a quality improvement team should be aware of when interpreting histograms:

  1. Before stating your conclusions from the analysis of the histogram, make sure the data is representative of typical and current conditions in the process.
  2. Do not draw conclusions based on a small sample. As a rule of thumb, use a sample of 40 for each histogram you wish to construct.
  3. Remember that your interpretation of the histogram is only a hypothesis that requires additional analysis and direct observations of the process in question.


A histogram is usually a bell shaped curve. What we end up seeing is a result of sorting the good from the bad. Examples: truncated histograms; bi-polar histograms; etc.

Look carefully for the phantom bell shaped histogram. Establish the Cost Of Poor Quality (COPQ) that you are unknowingly burdened with. Refer Quality Capsule #4


In my next edu-blog, on Wednesday 28 October, I will introduce Scatter Diagrams. This is an effective quality tool for bi-variate analysis. It quantifies a cause-effect relationship..


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