The Dunning-Kruger Effect: Are You Under its Spell?

When it comes to brain biases, one that gets an awful lot of play is the Dunning-Kruger Effect. It’s a phenomenon demonstrated by many studies, but it all started with, you guessed it, Dunning and Kruger.

The pair surveyed undergrad college students who had just taken a test, asking each student to predict his or her score. Then they compared each student’s guess against the results.

The students who did really well on the test had slightly downgraded predictions. The students who did poorly, on the other hand, had overestimated their scores by an average of 30%.

In Daniel R. Hawes’ article “When Ignorance Begets Confidence: The Dunning-Kruger Effect”, he shares the same general conclusion from another study:

“Participants who took tests in their ability to think logically, to write grammatically, and to spot funny jokes tended to overestimate their percentile ranking relative to their peers by some 40 to 50 points, thinking they were outperforming a majority of their peers when, in fact, they are the ones being outperformed.”

Put more frankly, people with limited knowledge have the potential to make total fools of themselves, without ever realizing how ridiculous they seem to everyone else. The problem of course, is you don’t know what you don’t know, so unless someone breaks through the wall of that person’s cognitive dissonance, it can perpetuate itself forever.

So here is an interesting question, and one that might be slightly uncomfortable. What if you are the poster child for the Dunning-Kruger Effect? What if you’re the one they’re chatting about around the water cooler? To that end, I share the following four warning signs for D-KE.

•   Any conversation that begins with your coworkers saying, in voices choked with withheld laughter, “Tell us that story again.” As in, “Tell us again how you explained to the exterminator that insects developed wings not so much through an evolutionary process but because they just didn’t know they couldn’t or shouldn’t be able to fly.”

•   When you approach the water cooler and the eye rolls move from person to person like the wave at a football game, and then they all break into a smile.

•   You believe that simply observing someone do something qualifies you as an expert, as in, “I’ve seen Mick Jaeger twelve times, how hard would it be to strut around on stage like that– provided I had access to his wardrobe.”

•   Any time your sentence contains the following words: “It’s not rocket science…” This one is actually a double whammy, because the Dunning-Kruger Effect is in play when you believe you understand rocket science enough to make a generalized statement, and likewise assume you are an expert in what you’re comparing rocket science to.

Important note: the above list is by no means a complete catalog of being under the influence of the Dunning-Kruger Effect,  but it’s a start.

>It’s also important to understand that D-KE is not the same as self-delusion. Self-delusion  happens when you are in some way a willful party to the charade. But as a participant in the Dunning-Kruger Effect, you are completely oblivious to your behavior—which means without outside intervention, the behavior could continue indefinitely.

We all know people like this. They are both fascinating, and infuriating. And although it is unlikely that you or I will have any effect on the way they operate, there is something satisfying about at least having a name for their ilk.

Now if you’ll excuse me, I’m off to watch another video on a note-for-note breakdown of essential Jimi Hendrix guitar licks. Because there are two things I know to be true: practice makes perfect, and if I only had his guitar, I’d give old Jimi a run for his money.

Cause and Correlation, or the Pirate Problem

graph

As you can see from the above graph, global warming is pirate-based.  It’s something I think we all suspected, but were hesitant to advance until the facts could be summarized in a handy graphic.

There is something about information delivered via graph that instantly lends an air of unassailable authority. The person trapped in the cube next to you, or even the guy down at the gas station couldn’t possibly carry the credibility of a simple graph.

It is an axiom of business that any presenter worth his or her salt is going to fill their PowerPoint with charts and graphs. The more the better, and the more oblique and difficult to read the best. Data delivered with a graph says “Here is the evidence, plain and simple. Let the ascending and descending lines tell you the story.”

The problem with the story, as with the graph above, is that we aren’t just suckered into believing correlation implies causality. We start thinking correlation is causality. Governments, businesses and individuals make this mistake on a daily basis. It’s impossible to calculate the frequency or the magnitude of the resulting financial loss, but it’s enormous.

We all know the crowing of the rooster doesn’t cause the sun to rise. But when rates of breast or prostate cancer is associated with soymilk, or some new drug, it can frequently drive us towards some definitive action, even though the connection of data points might in actuality be more rooster/sun than cause/effect.

The correlation/causality problem goes back a long way. The early human brain, confronted by the rustling of the bush, might naturally assume it was a tiger and not the wind. Erring on the side of safety could make the difference between life and death. Assuming correlation as causality was a small price to pay. This evolution based brain bias is still part of our biology today.

Here are four questions worth considering the next time you’re faced with the seductive whisperings of an X-axis.

1. Where do the represented data points come from?  Groups and individuals might be selectively mining the facts based on their own private agenda.

2. What do the data points represent?  Tiny samples can lead to casual conclusions that would be dismissed in a more robust survey population

3. Was this a blind study? A control group gives you some yardstick by which to judge the rest of the information.

4. Could other factors be in play?  This is probably the most abused problem with the correlation/causality mix-up. Maybe there is some relation between the X axis and the Y axis, but they could just as easily be responding to some other, third influence.

In the case of the rooster/sun problem, you’d want to consider both planetary revolution and the circadian rhythms of diurnal animals. (Additionally, if you’ve ever been on a farm, you’ll know that while roosters do crow at daybreak, those feathery little jerks will also sound their alarm in the middle of the night.)

So the next time some newscaster announces that eating peanut butter has “been linked to” autism, think back to our little graph. And remember: despite the insistence of Pastafarians everywhere (a group inspired by a modern-day Russell’s teapot analogy), most meteorologists agree that pirates have next to no effect on the climate.

Fans of buccaneers, privateers, and skallywags can let out a “Yarr!” of relief.