COMMON CAUSE	data within a normal distribution -- inherent process randomness
SPECIAL CAUSE	data within a shifting distribution -- "correctable" phenomena

Special causes are generally things that you can correct for, and are always things that you can catch in the data. Examples would be part wear, environmental disturbances, loose fasteners, untrained workers, impurities in materials or media, changes in shift or suppliers, etc. etc.

You catch special causes in the data by means of control charting. With a control chart, you're able to follow what the data is doing in time. If a visual inspection of the control chart causes you to be suspicious, there's a procedure to follow that tells you whether or not you have a special cause. If you have one, the behaviour of the special cause may help you to diagnose what it is, or you can (with the help of everyone involved in the process) design an experiment to help you to isolate it. If you don't have one, then your process is said to be under statistical control.

How to Find Special Causes

1. Plot the data.
2. Designate on the plot the "target value" (generally the mean).
3. Compute the common-cause standard deviation (the variation is the square of the standard deviation).

   This involves the following steps:

   1. Find the difference, or moving range, between each consecutive pair of data points you've plotted.
   2. Take the average of these moving ranges.
   3. Throw out any moving range that's more than 3.27 times the average you found in (3b).
   4. Repeat steps (3b) and (3c) with the moving ranges that are left, until you only have moving ranges which are not greater than 3.27 times their average.
   5. Once (3d) is done, your common-cause standard deviation (which we'll call S_cc) is the resulting average moving range divided by 1.128.

Your process is under statistical control if your common-cause standard deviation is close to zero.

4. Plot "control lines" horizontally for mean plus or minus 1, 2, and 3 times S_cc. (Don't forget to label 'em.)
5. The mean has shifted significantly, and your process has gone out of statistical control, if any of these rules of thumb are violated:
   1. one point outside 3S_cc on either side of the mean
   2. two-out-of-three consecutive points outside 2S_cc on the same side of the mean
   3. four-out-of-five consecutive points outside S_cc on the same side of the mean
   4. eight consecutive points on the same side of the mean

On your chart, make sure to mark the following, because you may need it to diagnose special causes:

• mean and standard deviation of all data
• S_cc
• the number of data points
• the source of the data
• the contact info
• the date, time, and duration of charting

References

Here are some SPC software packages:

• OMatrix shareware
• MicroConsultants shareware

Juran, J. M., ed. Juran's Quality Handbook NYC: McGraw-Hill, 1999. ISBN 0-07034-003-X
Keller, D. Introduction to SPC. Training course manual. Cleveland: Real World Quality Systems, 1993.

Take a look at the following chart of measured data. Does it suggest a "special cause?" Why or why not?

The true story behind the chart may be found HERE.