Oh yes. Yes yes. Learn this and understand how it applies to your systems, your processes, and especially (surprise) your people.<p>This is one quarter of how W. Edwards Deming promoted organizational quality control—understanding how variation works, period. (The other three being understanding psychology, understanding systems, and understanding the theory of knowledge or scientific method).<p>This applies directly to understanding whether observed variation has a common cause (is a natural pattern of the system), or is special cause (something unexpected): <a href="https://en.wikipedia.org/wiki/Common_cause_and_special_cause_(statistics)" rel="nofollow">https://en.wikipedia.org/wiki/Common_cause_and_special_cause...</a> and this impacts how you handle the variation.<p>For those criticizing validity, I'll say this is a way to mentally model how to understand variation, and is not meant to be 100% accurate. You're trading intuitive modeling for perfect math. But it will allow you to get close in a back-of-the-napkin quick way so you can identify patterns to study in more depth. Also, think of this in the context of many types of systems, not just a tight electrical signal pattern (which are easy systems to understand). Systems of people doing software development, machines in manufacturing processes, complex network error patterns, etc etc.<p>People don't often have a good idea of what's important and what's noise, especially when you don't even have a control chart but are just using intuition and a few data points. We see outliers and variations all the time in processes, especially in human processes like those we encounter in most software companies. Estimation and delays, developer performance, load failures; all kinds of complex systems that exhibit variation that people are usually "winging it" to understand.<p>Instead of understanding the variation and the data, people often handle every large variation in the same way, trying to "fix" it or peg it on some obvious correlation they think they observe. This says: hold on, understand what you're looking at intuitively first. Then gather more data. Don't act without understanding. Deming was fond of saying, "don't just do something, stand there!" Lots to be learned from that, and much to be gained from the simple intuitive understanding of patterns in variation.