Data Driven Decisions – It’s become a buzz phrase in education and business circles. What does it really mean? To put it simply, making informed decisions based on numerical data, rather than based on our own perceptions, gut, or hunches. In today’s education world, we have access to mountains of data, all at our fingertips. We can access so much information, it can be overwhelming. When we analyze data, we must consider some important questions. What does the data mean? What does it mean when a data point improves or regresses? If we see improvement, should we keep doing what we are doing? Do we throw a party and reward someone for better results? If we see a downturn in data, do we search for reasons why? Do we attempt to find an individual to blame for a bad result? What factors influence a data point? Is there just one factor or cause? Are there multiple factors or causes for the data point? How can we know if there is one factor or multiple factors causing the results of the data we measure?

Variation is Life

To help us understand what to do with data and how data can help us make decisions, we must first understand variation. Variation is part of life. Variation is everywhere, and it’s impossible to eliminate it. Numerical data will improve and regress over time. One data point is not sufficient for developing enough knowledge to make a decision. We need to see a range of data points over time.

Two Types of Variation

Next, we must learn what is causing the variation. W. Edwards Deming described two sources of variation: those caused by the system and those caused by an identifiable factor or factors. Variation of results coming from systemic factors are considered common cause variation. The patterns of interaction and relationships within the system cause the this type of variation of results. There is no one source of the cause, because it comes from the system. Let’s consider one person’s commute to work within the transportation system. There will be variation in the amount of time it takes a person to leave their home and arrive to their workplace. Let’s say the time ranges from 15-20 minutes (nice little commute right?). A time of 16 minutes one day for a commute would be expected and considered a result coming from common cause of the transportation system. Another day, the commute may take 18 minutes. It would be expected that some days the commute may take a little less time, and some days a little more time. We would not consider these differences out of the ordinary, and all would be results of the transportation system. It’s important to keep in mind that much of the ups and downs of data are derived from the system itself. In fact, in a stable system, Deming believed about 94% of the results are caused by the system.

Variation due to a specific cause not from systemic factors is called special cause variation. The cause of a result has a specific source outside of systemic interactions. Let’s consider the example of commuting from home to work again. If one day the commute took over 45 minutes to complete, we would probably expect that a special situation caused the extended time, like a car wreck caused by a driver running a red light. The transportation system would not have caused the wreck, but the driver running the red light. In a stable system, special cause of variation occurs far less than common causes. Deming believed 6% of results were due to special cause, far less than common causes.

Treating Common Cause and Special Cause Variation Differently

Now that we have an understanding of common cause and special cause driving our results, how can this help us make better decisions? In my opinion, knowing how to deal with both is the one concept that will help you improve your decision making more than anything else. If we determine the cause of results, whether desirable or undesirable, are due to common causes in the system, then we must look at the system, and not one individual person or component in the system. It’s the patterns of interactions within the system that are causing the results, not one person or component. If the results are undesirable, blame should be spread across the system, and only working on the system will improve the results. Improvement efforts on the system should focus on the whole system, and not the individual parts. The relationships and interactions within the system must be optimized and improved. It’s important to remember that Deming believed 94% of the results we see are caused by the system. When in doubt, look at the system or process first.

In contrast to common cause variation, special cause variation has a specific cause. If the special cause is resulting in an undesirable result, then steps should be taken to deal with that specific cause. This is usually our default mode for problem solving. We are conditioned to think of finding the one root cause of a problem, and taking steps to solve the problem. We even teach this to students in school. I taught the concept of cause-effect in my World History class. It’s easy to misunderstand cause-effect relationships as meaning one factor causing the effect or result.

Mistake – Treating Common Cause Like Special Cause

This mental model of single cause leading to an effect can potentially hinder us from seeing how systems cause most of the results we see. Deming warned of confusing special cause and common cause variation. Ideally, we should avoid treating common cause variation like special cause, and treating special cause variation like common cause. If results are caused by the system, it is fruitless to try to find a single cause. Yet, there are countless examples in education where we try to find the single cause or the person to blame for poor results, when the results are caused by the system. We also recognize and reward individuals for outstanding accomplishments, when the system is probably more responsible for results than we realize, especially when recognizing a Most Valuable Player of a team or sport (see my blog post The New Debate in Sports: The GOAT – Jordan or Brady). Remember, to improve results coming from common cause, it is better to improve the system or process, than attempting to find a single cause.

Mistake – Treating Special Cause Like Common Cause

If we treat special cause like common cause, we will continue seeing results caused by the special cause. Improving or changing the system or process will not remove the special cause. Going back to our transportation system, let’s say lightning struck a tree, and the tree fell on one of the roads on the commute to work. We could optimize traffic signals and resurface roads to improve driving conditions. However, if we don’t remove the tree, the commute will continue to take longer than needed. This would be an example of treating a special cause like a common cause.

Understanding common causes and special causes of variation in data helps us make better decisions. It helps us know when to search for and deal with a specific cause to a problem, and when the system should be improved or transformed. It also reveals to us how much more powerful systems are than individuals within the system. Knowing that a stable system is far more responsible for results than special causes teaches us that we should continually improve our systems and processes.

I continue to learn about statistics, statistical variation, and how both influence our decisions. I would appreciate any knowledge you can share with me and other readers. How do you or your school use statistics to make decisions? I would like to hear examples of how understanding common and special cause affects how decisions are made in education. Please share in the comments below. Also, if you like this blog, and would like to receive email notification of future blog posts, please subscribe.

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