Common Cause of Variance vs Special Cause of Variance

Common Cause of Variance vs Special Cause of Variance
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Avantika Monnappa

Published on July 28, 2015


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What is Variance?

 Every piece of data which is measured will show some degree of variation. No matter how much we try, we would never attain identical results for two different situations: each result will be different from the other. Variation may be defined as ‘the numerical value used to indicate how widely individuals in a group vary’.

In other words, variance gives us an idea of how the data is distributed about the expected value or the mean. If you attain a variance of zero, it indicates that your results are identical. This condition is usually uncommon. A high variance shows that the data points are spread out from each other and the mean, while a smaller variation indicates that the data points are closer to the mean. Variance is always nonnegative. 

Types of Variance

Change is inevitable, even in statistics. Thus, it is important to know what kind of variation affects your process because the course of action you take will depend on the type of variance. There are two types of Variance: Common Cause of Variance and Special Cause of Variance. 

Common Causes of Variance

Common Cause of Variance, also referred to as ‘Natural Problems', ‘Noise' and ‘Random Cause' was a term coined by Harry Alpert in 1947. Common causes of variance are the usual quantifiable and historical variations in a system that are natural. Though a problem, they are an inherent part of a process. This kind of variance will eventually creep in, and there is nothing you can do about it. Specific actions cannot be taken to prevent this failure from occurring. It is ongoing, consistent, and predictable.
 
Characteristics of common causes of Variance are:

  • Variation predictable probabilistically
  • Phenomena that are active within the system
  • Variation within a historical experience base which is not regular
  • Lack of significance in individual high and low values.

 
This variation usually lies within three standard deviations from the mean where 99.73% of values are expected to be found. On a control chart, they are indicated by a few random points that are within the control limit. These kinds of variations will require management action since there can be no immediate process to rectify it. You will have to make a fundamental change to reduce the number of common causes of variation. If there’s only common causes of variation on your chart, your process is said to be ‘statistically stable'.

When this term is applied to your chart, the chart itself becomes fairly stable. Your project will have no major changes, and you will be able to continue process execution hassle free. 

 Examples of Common Causes of Variance

Take, for example, an employee who takes a little longer than usual to complete a certain task. He is given two days to do a task and instead he takes two and a half days; this is considered a common cause of variation. His completion time would not have deviated a lot from the mean, since you would have had to consider the fact that he could submit it a little late.
 
Here’s another example: you estimate 20 minutes to get ready and ten minutes to get to work. Instead, you take five minutes extra getting ready because you had to pack lunch and 15 additional minutes to get to work because of traffic. These would be Common Causes of Variance.
 
Other examples that relate to projects are inappropriate procedures, as in the lack of clearly defined standard procedures, poor working conditions, measurement errors, normal wear and tear, computer response times, etc. 

Special Causes of Variance

Special Cause of Variance, on the other hand, refers to unexpected glitches that affect a process. The term Special Cause of Variance was coined by W Edwards Deming and is also known as an ‘Assignable Cause'. These are variations that were not observed previously and are unusual, non-quantifiable variations.

These causes are sporadic, and they are a result of a specific change that is brought about in a process resulting in a chaotic problem. They usually relate to some defect in the system or method.  However, this failure can be corrected by making changes in a certain method, component or process.
 
Characteristics of Special Causes of Variation are:

  • New and unanticipated or previously neglected episode within the system
  • This kind of variation is usually unpredictable and even problematic.
  • The variation has never happened before and is thus outside the historical experience base.

 
On a control chart, the points lie beyond the preferred control limit or even as random points within the control limit. Once identified on a chart, this type of problem needs to be found and addressed immediately so as to prevent recurrence of it in the project. It is not usually part of your normal process and occurs out of the blue. 

Examples of Special Causes of Variance 

An example to better explain Special causes: you are driving to work, and you estimate arrival in 10 minutes every day, but, on a particular day you reach 20 minutes later, since you encountered an accident zone and were held up.

Examples relating to project management are if the operator falls asleep during the execution of your project, or a machine malfunctions, a computer crashes, there is a power cut, etc.
 
One way to evaluate a project's health is to track the difference between the original project plan and what is actually happening. Use of control charts helps to differentiate between the Common Causes and the Special Causes of Variation making the process of making changes and amends easier.

Preparing for PMP® Certification? Take this test to know where you stand!

 
Common Causes of Variation and Special Causes of Variation are two sectors that are tested on the PMP® and CAPM exams.

In summary

 

  Common Causes of Variance Special Causes of Variance
Also Known As Natural Problems, Noise, Random Cause Assignable Cause
Nature common, predictable uncommon, unpredictable, sporadic
Term Coined By Harry Alpert W Edwards Deming
Characteristics Variation predictable probabilistically
Phenomena that is active within the system
Variation within a historical experience base which is not regular
Lack of significance in individual high and low values.
 
New and unanticipated or previously neglected episode within the system
Usually unpredictable and even problematic
The variation has never happened before and is thus outside the historical experience base
Examples Inappropriate procedures, Poor maintenance of machines, Poor working conditions, Measurement error, Traffic, Weather Poor adjustment of equipment
 
Operator falls asleep, Accidents, Computer crash, Broken part, Machine malfunction
 

 

PMP is a registered trademark of the Project Management Institute, Inc. 

About the Author

A project management and digital marketing knowledge manager at Simplilearn, Avantika’s area of interest is project design and analysis for digital marketing, data science, and analytics companies. With a degree in journalism, she also covers the latest trends in the industry, and is a passionate writer.


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