What does ‘with’ mean?

Many reports on covid hospitalizations report admissions ‘with’ covid, yet people seem to interpret that to mean “due to.”

If you point out that ‘with’ does not mean ‘due to’, you may be accused of minimizing the situation rather than trying to apply basic reading comprehension.

Some of the negative blow back comes from those inexperienced with interpreting numbers. For them, the difference in meaning between ‘with’ and ‘due to’ seems trivial enough that they make a leap on what the report says, “what they really means is ‘due to.'”

What’s more is they do this without realizing it. When they think back to the report they remember the meaning they created for the report rather than what the report actually said.

The movie Inception was about implanting an idea in someone’s mind so that they thought it was their own idea.

What I describe above is reverse-inception, causing someone to believe their own idea is someone else’s. In this case, they think the idea that hospitalizations due to covid is what the reports say, when it is not.

This reminds me of when reporters asked Lance Armstrong if he took performance enhancing drugs and he would respond, “I never failed a drug test.”

Many would interpret Armstrong’s answer as a strong “no” when he didn’t directly say no. He reverse-incepted the answer into our heads. Later, he admitted that answer was his way of feeling like he wasn’t lying, even though he knew how folks would interpret it.

This is not to say that more folks aren’t being hospitalized due to Covid or having serious illness due to Covid. If you think that’s what this says, then you are reverse-incepting an idea onto me.

This is just to point out that if you read the reports on covid hospitalizations, carefully, you will notice they usually do not provide clear enough info on the number of folks hospitalized due to covid to draw sound conclusions.

When I point this out to folks, they ask, what does the report mean then? I’ve used some version of this simplified example to explain it.

Let’s say 100 kids per week are hospitalized for broken arms and all those admitted to the hospital are tested for Covid as standard practice, which they are.

Last week, 10 of those 100 kids tested positive for covid, while 10% in the general population were also testing positive.

This week, Omicron blows through the area and now 25% of people in the general population test positive for covid. Those familiar with data will expect to see this trend in test positivity in hospital admissions, as well.

Sure enough, the local hospital reports that 25 admissions tested positive for covid this week vs 10 last week, therefore the number of kids admitted to the hospital with covid more than doubled!

Here’s what they don’t mention: those 25 were admitted for broken arms, not covid; that the total number admitted for broken arms is on par with the previous week; that the test positivity rate was in-line with the rate in the general population in the area.

They also don’t mention how many people were admitted specifically for covid.

The next stage of reverse-inception is to doubt that the reports would dare be that misleading, because it seems like it would be too easy to be debunked and surely someone would so!

Folks did the same with Lance Armstrong. When they realized that his response didn’t directly deny taking drugs, they would reason that there’s no way he would dare be that misleading because it would be too easy to debunk. And, yet it took years to do so.

Read reports on covid carefully. Pay attention when you hear yourself saying things like “I think what they really mean is…” or “they are making it sound like…”. Those are sure signs that you may be getting reverse-incepted.

Why not build more hospital capacity?

We’ve been hearing that hospital capacity is an issue for 2 years.

I find it strange that building more hospital capacity doesn’t ever seem to be considered as a solution.

“Every flaw in consumers is worse in voters”

Economist and professor Mike Munger said this on this Econtalk podcast, recently.

Good point.

I know folks who think that voting is better than markets. They fail to realize the same folks (you and me) make the choices in both, just with different incentives.

Think about what incentives you face when you buy a food at the grocery store or a restaurant, or go on vacation, pay someone to mow your lawn, remodel your house or buy a streaming service.

Now think about the incentives you face when you vote.

What’s different? What’s the same?

It’s good to keep in mind that whatever you might think the flaws are in markets and consumers is worse in voters and to understand why.

I once had a nice chair: be cautious of statistical studies

I once worked for a company that had nice office chairs.

It wasn’t a huge perk. They didn’t make a big deal of it. They didn’t even mention it.

But I liked it. There were days without much else to look forward to at work than that chair. So it helped.

When I was procrastinating on starting a project, the nice chair was there to sit in and get me started.

When a meeting didn’t quite go my way, I turned the corner and saw the chair and it brightened things a bit.

I worked for other companies, where chairs were good enough. Nothing wrong with them. They were comfortable. They did the job.

But, not once did I look forward to my chairs there. Just like the folks that bought them, I never gave them a second thought.

Does this mean managers should approve nice chairs for their staff to improve motivation and productivity? I doubt it. I’m sure that benefit would be hard to detect in a way managers desire: “Workers with the nice chair are 10% more productive!”

Part of it was the nice chair. Part of it was a little reminder that the owners thought enough about employees to even think about providing nice chairs without expecting anything in return. That last part doesn’t replicate in a ‘data-driven decision to drive results.’

After all, when employees catch wind that the managers made the decision to drive results, they realize there was no soul in the decision, the employee was an afterthought and, oh yeah, there’s the expectation of more productivity.

In that way, the chair might become more of a sore spot than a bright spot in a person’s day, because it becomes a reminder that there is an expectation to do more because of it, even though it’s not exactly clear what doing more is.

Maybe it does mean that managers who genuinely care about their workers in ways that show up like buying them nice chairs without any expectation on results might be more satisfying to work with than managers who ‘do what the data tell them.’

Almost always wrong, example

I’ve worked with mature businesses experiencing declines in units sold as the average price rose.

The obvious answer to most folks: rising prices chased away customers.

The market researchers agreed with the sales data, as their surveys revealed that high price was the number one reason cited by customers for not returning.

Even customers believed it. When I talked to customers, high price was the most common complaint.

I believed it, too.

Though, I opened my mind to other possibilities after we tried a number of ways to lower prices and they failed.

The ‘high price’ hypothesis didn’t die easy. We came up with all sorts of reasons why the price reductions failed: We didn’t tell anybody! The price drop was too small! We’ve already priced customers out and they won’t come back.

We tested those, too, to no avail. While others still clung to their high price explanation, I dug deeper.

I began asking customers lost to high price: What price would win you back?

Most paused as they thought about it and then gave me the real reason why they stopped using the company.

I found two common themes: service beaks and value discovery.

An example of service break is when you go to a high-end restaurant and receive casual chain restaurant level service. It’s not terrible, but didn’t live up to the higher expectation.

Value discovery is when someone tries your product and discovers they do not value it as much as their favorites. How many products do you try only to discover that you do not value it? I like beer. I try many beers that aren’t bad, but I don’t enjoy as much as the beers I like more, so I might buy them once and not again.

On a market research survey I might even say the price is too high and what I really mean is that the I’m not willing to pay for that beer when I can pay the same amount for a beer that I like more.

Those answers inspired me to ask the market research group to see raw data from their surveys.

I found through answers to other questions on the survey that got little attention on the market researchers’ final reports, the lost customers revealed the same two reasons.

The market research group had a strong bias against the company’s high prices and when they saw the answer that fit those biases, they stopped looking.

My analysis of the data inspired approaches to keeping customers, without discounting.

One approach we tried: improving training so that when a customer visited a high-end restaurant, they received the service they expected. That worked better than offering the discount.

Through that experience I learned to be aware and leery of when everybody thinks the answer is obvious. Maybe it isn’t.

Be open to other possible explanations.

Consider how to figure out those other explanations, like when I asked lost customers what price would win them back? And then dug deeper into the market research. The answers were there all along, but nobody wanted to look at them because they were so certain they knew already and discounted any opposing evidence.

After all that, I remember one day receiving a challenge from one of the market researchers, “if price isn’t the reason why units have declined, why have units declined almost in lock step with the price increases?”

What I had also discovered is that the prices had been increased by management in the past to make up for the lost units to achieve the company’s financial goals. So, the undiscovered reasons driving the lost customers were, in essence, causing the prices to rise, not the other way around.