Paul Krugman revisited the so-called “Great Resignation” and concluded it isn’t really a thing. You heard it here first. Thanks for the validation, Dr. Krugman, and for demonstrating the difference between calculating statistics correctly and interpreting them properly.

Sneering at statistics is a popular pastime, with lots of clever quotes from clever people to justify the derision, and high salaries paid to the data scientists who are good at it.

Statistics is hard to get right, even without the math. Failing to appreciate the stochastic nature of the world around us … that is, the randomness that’s intrinsic to so much of our experience … is a major reason so many of us so often draw the wrong conclusions.

Three examples, drawn from Daniel Kahneman’s brilliant Thinking, Fast and Slow, help illustrate just how easy misinterpreting statistical analysis is. Call them the bulls-eye effect, regression to the mean, and the small town fallacy.

Bulls-eye effect

The bulls-eye effect is familiar to anyone who has ever sighted in a rifle. Here’s how it doesn’t work: (1) Put the rifle in a clamp; (2) pull the trigger; (3) realign the site so its crosshairs are pointed at the hole in the target.

Do this and pull the trigger again. This bullet won’t pass through the hole created by the previous one, and the next shot you fire probably won’t pass through either of the previous bullet holes.

The trajectory of a fired bullet is stochastic, affected by random effects. Slight variations in bullet weight, propellant quantity and composition, wind, barometric pressure, humidity, and so on all influence the path each bullet follows on its way to the target.

To properly sight in a rifle you need to fire more than one round – statisticians would recommend 30, but 10 is probably enough. Then, as Figure 1 illustrates, adjust the sight so it points to the middle of the cluster of bullet holes you produced. And, accept that whenever you use the rifle you won’t be able to perfectly predict where the next bullet will fly.

Figure 1 – The bulls-eye effect: Sighting in a rifle

No matter which business process or practice you’re dealing with, perfection will elude you because to a greater or lesser degree, all processes and practices are subject to stochastic effects.

Regression to the mean

A team member does poorly at an assignment. You chew them out, and they do better at the next one. Another team member handles their assignment well. You provide positive reinforcement … you compliment them. Nonetheless they do worse their next time out.

You draw the obvious conclusion: Negative reinforcement works, positive reinforcement doesn’t. And … you drew the wrong conclusion. As shown in Figure 2, if today’s performance is below average and performance is stochastic, tomorrow’s performance will most likely return to average – an improvement due solely to randomness, not negative reinforcement. This return to average is what statisticians call regression to the mean.

Figure 2 – Regression to the mean: Performance improvement

Small town fallacy

Imagine COVID-20 breaks out, and a preliminary analysis shows the top ten per capita hot spots are all small towns. What, you’d likely ask yourself, makes small towns more vulnerable?

The correct answer: They aren’t more vulnerable. At least for the purposes of this article’s hypothetical, the root cause is that small towns are both smaller and more numerous than big cities.

A thought experiment shows how this works.

Use a random number generator to model a population of 10 million cases: Randomly assign a 0 (COVID-free) to 5 million cases and a 1 (COVID-positive) to the other 5 million.

Establish 10 clusters and randomly assign 500,000 cases to each. These are the large metropolitan areas. Calculate the mean per capita rate of infection. All ten will be close to 50%.

Divide the remaining 5 million cases into randomly-selected clusters of 1,000, corresponding to 5,000 small towns. Compute the mean per capita rate of infection for each of them.

The outcome: The rate among the small-town (1,000-case) clusters will, because of their much smaller sample sizes, vary more widely than the metro areas. Some will have much higher rates, and just as many will have much lower rates.

So even if differences in COVID-20 per capita incidence are due solely to stochastic effects, major metropolitan areas will exhibit rates close to the overall 10 million case mean. Smaller municipalities will occupy the most extreme positions on the incidence scale – some will exhibit the highest rates of infection, others the lowest.

Bob’s last word: Stochastic thinking is hard, requiring constant vigilance. You’ll make a good start by familiarizing yourself with common statistical errors. This, from Wikipedia, is a good place to start.

Bob’s sales pitch: has launched my bi-weekly feature, the “CIO Survival Guide.” It’s up right now for your interest and edification: The CIO’s missing priority .

“Who are you going to believe?” Groucho Marx famously asked, “Me or your own eyes?”

The Groucho Question came to mind as I read an intriguing, if conspiracy-oriented opinion piece by James Schmitz Jr., a senior economist at the Federal Reserve Bank of Minneapolis. Schmitz’s contention is that home-building is trapped in 19th century technologies and methods that have prevented productivity increases in that sector for a matter of decades. (“Homebuilding must modernize,” StarTribune, 5/2/2021).

His solution: “Rather than manufacturing homes in factories, they are constructed outside, with a centuries-old technology sometimes called the “stick-built” method.”

He’s the “Me” part of the Groucho-ism. As one who consults on and writes about matters of organizational effectiveness, I found his contention appealing.

As the owners of a downtown condominium who, by HOA mandate had been required to have our balcony door replaced … twice; the first replacement having been problematic … my wife and I recently had the opportunity to see Schmitz’s theory applied in actual reality (and isn’t it strange to live in an era where I have to specify which reality I’m writing about).

We were Groucho’s “… or your own eyes,” as we watched two clearly expert individuals remove the offending door panels and replace them with (presumably) more serviceable ones. To get the job done they had to deal with perhaps six situations that required significant expertise and ingenuity.

It occurred to me that while the door panels had been manufactured in a factory – they were the result of a well-defined process – their installation was clearly and unavoidably a practice.

Why this matters to you: Here in KJR reality we’ve frequently discussed the difference between processes and practices; processes being how work gets done in factories, with their repeatable, predictable steps; practices being how work gets done in hospitals and law offices, where no two cases are ever exactly alike and work must be adapted to each specific situation.

We consultants frequently advise clients to turn case-by-case kinds of work into metaphorical factories so they can perfect their techniques and get results that are more repeatable and predictable. I’ve been guilty of this myself, although I hope with enough qualifiers that I haven’t overpromised the ease and simplicity of the result.

The differences between process and practice aren’t “mere” semantics, not that there’s anything truly mere about semantics in the first place. It’s also fair to say that to the extent it’s possible to redefine how work gets done so it’s less of a craft or practice and more of a manufacturing-like process, defect rates and incremental costs will, in most cases, decrease.

It’s just easier said than done. To turn any craft or process into a factory-like process, a key aspect of the transformation is to view each step in the work as responsible for reducing the variability or, looking through the other end of the telescope, increasing the predictability of what those responsible for the next step in the work will face.

If you’re building a house that means getting started by (based on my extraordinarily limited understanding of the trade) clearing and grading the lot, laying the foundation, and fixing variations in ground compressibility to avoid surprises when pouring the slab and laying cinder block.

When building an application it means (among other things) clearly defining the architecture and coding standards, writing consistent user stories, and determining interdependencies to avoid surprises when, depending on the application architecture, coding each module, service, microservice, or what-have you.

Bob’s last word: When we envision a factory we envision a conveyor belt that carries identical items to each step in the work.

When we envision most of what IT does for a living … or when we envision building a house … we don’t envision anything remotely like that.

Bob’s sales pitch: Looking for help improving the practice of improving business practices and processes? Here’s where to contact me so we can start a conversation.