I’ve been thinking a lot about how easy it is to misinterpet data. I am not talking about falling for misinformation here. Rather, there are lots of data points that we come across that are factually true that we just draw unsupported conclusions from. Just because something survives a fact check, doesn’t mean we magically know how to model the facts at hand.
Put another way, just because something is true does not mean you know the truth.
I was reminded of a blog post I wrote a couple of years back before I started this substack and how relevant it is to this latest obsession of mine. So I thought it would be good to repost it here.
On July 12, 2022, Bloomberg.com ran a piece with this headline: “MILLIONS OF AMERICANS REGRET THE GREAT RESIGNATION”.
That certainly sounds pretty bad.
Quits have been at record highs during the Great Resignation. Now, Bloomberg is citing survey data (from Joblist.com) that shows that for millions of people, it wasn’t such a great decision to quit after all.
That was the narrative presented in the article. But if you dig deeper into the piece, you realize that this is a weirdly negative frame on the data. And by “dig deeper,” I mean only as deep as the first sub-head, which is, “About one-quarter of job-leavers rue the decision.
”Wait? What? <record scratch>
If one-quarter of job-leavers rue the decision, that means three-quarters of job-leavers don’t rue the decision.
Yes, the headline is technically true. Millions of people do regret quitting. But the actual data conveys a different message about the millions more who don’t regret it.
When I tweeted the headline and the sub-head, one commenter sided with the headline, replying, “Do you know how large 25% of that # is?” Another commenter put things in the proper perspective with this excellent retort: “Do you know how much larger the other 75% is? (Spoiler: 3x)
”Frame matters. If an 8-ounce glass has 4 ounces of water in it, you could say, “The glass is half full,” or, “The glass is half empty.” When you choose the negative frame, pushing out a headline that the glass is half empty, people will naturally perceive the data as telling a negative story. Of course, the Bloomberg headline is even worse, framing an 8-ounce glass with 6 ounces of water in it as one-quarter empty.
That is some pretty strong negative framing about quitting.This is a good example of why it’s important to digger deeper into the data and look past the way it’s framed.
Millions is a big number. But like all things, it is relative and you should be asking millions compared to what? The what, in this case, is the denominator: All the people who have quit, which, as the article mentions, is about 20 million for the first 5 months of 2022. That means that while 5 million people regret quitting, 15 million don’t regret it.
Making matters worse, it’s likely the 3-to-1 ratio in favor of quitting actually understates the lack of regret of people who quit their jobs during the Great Resignation. That’s because the Bloomberg article was based on a user survey from Joblist. Joblist is a site people use to look for jobs. That means the survey participants were from the specific subset of people you would imagine would be particularly unhappy: Those who haven’t found a job yet (or who found a job they were unhappy with and, so, are still looking).
No doubt, the proportion of quitters with regrets would be much smaller if you added into the denominator all the people this survey would have missed: Those who found satisfactory jobs, as well as those who quit and aren’t looking for another job.
That makes quitting the big winner here.
I suspect one of the reasons this article is framed so negatively is because we have a cognitive aversion to quitting. After all, “Quitters never win. Winners never quit.”
Calling someone a quitter is an insult. And if quitting is a bad thing, if it means you are lazy or unreliable or weak-willed, then the idea that a whole bunch of people have quit their jobs and are happier for it creates a lot of cognitive dissonance.
How can you resolve the dissonance? Rationalize away the data. Or reframe it.
Most people might find it surprising that so many people quit and did not regret it. It’s a good guess that the author of the piece did (thus the negative frame). But it’s not really that surprising at all if you’re familiar with nearly half a century of research from behavioral economists and social scientists of the likes of Daniel Kahneman, Richard Thaler, Richard Zeckhauser, Steven Levitt, Barry Staw, and Leon Festinger.
There are so many cognitive and motivational headwinds that discourage us from quitting that when we finally do it, the reasons for quitting are frequently so strong that we’re very often happier after we finally walk away.