Normal Distribution is Actually Rare

When we often use statistical analysis tools and techniques, the underlying assumption is that process/ sub-process displays a “normal” behavior. Even if the limited data that we have shows non-normal behavior, we assume that the reason is the lack of data, and we approximate the distribution to normal.

This assumption and subsequent analysis, conclusions and decisions are therefore inaccurate, especially if we are combining “assumed” normal behavior across multiple processes, viz Process Performance Modeling.

“Normal” behavior is very rare in real life. For example, you travel from your home to office, let us say usually in 1 hour. The least time you have ever done the trip is in 30 mins. If the distribution was normal, the worst time should have been 1 hour 30 mins (symmetrical on both sides). You will find that on some days that you were delayed, the time could have been 2 or even 3 hours!

Another way of saying that real life does not behave in a “normal” way, is “there is a limit on how well you can do, but no limit on how badly you can screw up!”

There is more on this in the books “Fooled by Randomness” and “Black Swan” by Nassim Taleb — must-reads for anyone involved in high maturity CMMI® implementation.

Also see:


I am Rajesh Naik. I am an author, management consultant and trainer, helping IT and other tech companies improve their processes and performance. I also specialize in CMMI® (DEV and SVC), People CMM® and Balanced Scorecard. I am a CMMI Institute certified/ authorized Instructor and Lead Appraiser for CMMI® and People CMM®. I am available on LinkedIn and I will be glad to accept your invite. For more information please click here.

2 thoughts on “Normal Distribution is Actually Rare”

  1. Rajesh,
    Thanks for the book suggestions. Will read them at the earliest opportunity.
    I agree with you that the normal distributions are rather rare especially when human beings are involved.
    I have come across cycle time data of hundreds of service requests handled by support team and always found that the variation was skewed towards the SLA limit. This I guess is because team always do their best to meet the SLA criteria and are capable of achieving it almost every time. But on a few occasions when they miss it it gets reflected as a long-tail and the distribution gets skewed.
    But when such occasions are treated as outliers and not considered then we get a normal distribution.

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