In this episode, David and Andrew talk about Common Cause Variation vs Special Cause Variation, and the problem of confusing the two. Using the example of transgender students, David describes how a system's capability should be expanded rather than using that special cause situation as a weapon to destroy the entire system.

TRANSCRIPT

Stotz: My name is Andrew Stotz, and I'll be your host as we continue our journey into the teachings of Dr. W Edwards Deming. Today, I am continuing my discussion with David P. Langford, who has devoted his life to applying Dr. Deming's philosophy to education, and he offers us his practical advice for implementation. Today's topic is weaponizing special causes. David take it away.

Langford: It sounds very dangerous, and it is. So I wanted to get into a little bit about special and common causes about what Deming talked about. So once again, we'll go back to Deming's system of profound knowledge. And as part of that he talked a lot about understanding variation in systems and without getting too statistical about things, he basically got people to identify two different types of variation. So there's common cause variation, which typically makes up anywhere from oh 94% to 98, 99% of what goes on in any system or any process. And then there's special cause variation, which is generally less than 2% or less than 1% of what goes on. So that sounds pretty innocuous until you actually start thinking about how that works out in society and systems and classrooms, especially in education in schools and Deming said over and over and over that there are basically two problems with special and common cause. So people treat special cause as if it's common, okay. Which is, and he called these deadly diseases, or they treat common cause variation as if it's special.

Langford: So sometimes it's difficult to sort of understand what is, what does that mean? So let's take an example like in a, in a school common cause variation would be say the, the performance of a whole school or a state or a nation or whatever it might be. You can chart that out over a long period of time and start to see a certain level of predictability of performance on anything you wanna look at, whether you're talking about behavior or you're talking about test scores or grades or kids showing up for school every day. Doesn't really matter what system you wanna look at. When you start looking at it from a systems perspective, you wanna look at as much data as you can.So at least six or seven data points, but preferably 12 and some, a lot of statisticians will say up to 20 data points.

Langford: So if you're just looking at, say test scores over a long period of time, well, you'd wanna actually look at average test scores over a 20 year period. So that, that could take a really long time to see data systems like that emerge, especially in education where we have what I call slow data that emerges. You’re familiar with like manufacturing environments and business, where you have a lot of fast data. So you may be making something and you're collecting a hundred thousand data points in a single day or a month or a week where in education, it, it really doesn't really work like that.

Stotz: I have an example that may be helpful for those people that aren't familiar with the topic. And that is in my coffee factory, we fill bags of coffee with, let's say a hundred grams of coffee. If we fill it with 101, well, we're giving away. If we fill it with 99, we're not delivering what we say. And what first lesson that we learned is that nobody's perfect. No, there is no way to consistently hit 100 is always gonna be some variation. Now that variation may be 100.0 1, 4 7, but ultimately variation around that is bad. And what you find is that maybe when the system is not that strong, you could be putting in 95 grams on sometimes and you could be putting in 105 on sometimes and something in between those. But those are all kind of common causes.

Stotz: There's just there's variation about that average, let's say. And then the only way to improve that would to be say, ‘oh, well, we need to have a more precise piece of equipment.’ It’s not that the workers weren't working hard enough or something, but we just didn't have, so we replace a piece of equipment that's measuring and all of a sudden we weigh more consistently the old clunky one didn't work that well, and now we're getting more and more narrow. And then one day the electricity goes off or we have a, a problem with the electricity and all of a sudden it's throwing the whole system off. Well, that would be some special cause as opposed to this common variation around the 100 that we're aiming for, would I be describing it? Right or how would you add to that?

Langford: Yeah, that, that that's exactly right. And so if we charted out filling your coffee bean bags over a long period of time, we'd probably find out that you'r, you probably...

свернуть