By Chally Kacelnik
You’ve probably heard of the Dunning-Kruger effect. Coined by David Dunning and Justin Kruger in 1999, you’ve likely heard it as describing how people without much knowledge in a particular area tend to overestimate their knowledge, lacking a sense of how much knowledge they’re lacking. It’s probably true of all of us at some time or another, but gets used to mark and sneer at people for being “too stupid to know how stupid they are”.
Is this nice? No. Is the Dunning-Kruger effect a real thing? Also no – at least not in the way we’ve learned it.
I’ve been thinking about “The Dunning-Kruger Effect is Probably Not Real” by Jonathan Jarry at McGill for a while now. Like Jarry, I also wanted it to be real (I’ve had a lot of smug thoughts along the lines above, but hadn’t spent a lot of time considering whether other people could be having the same thoughts about me!). But his findings (including from correspondence with Dr Dunning) show that the effect has been overstated and misunderstood, partly as a result of how we measure data. As Dr Dunning says, “The effect is about us, not them. The lesson of the effect was always about how we should be humble and cautious about ourselves’. This is about a discrepancy that can happen in anyone’s brain.
When people weaponise the effect to express contempt or frustration about others, they are not doing anything useful about a lack of knowledge. As someone who has engaged in this kind of self-aggrandising behaviour, I can tell you that the only impact it had was to make me less likely to truly respect others or be self-reflective. So much for looking down your nose at cognitive bias, much less avoiding it.
The truth is that anyone, at whatever level of expertise, is capable of underestimating or overestimating how much they know, or of being misinformed. The solution to that is sharing knowledge and, for leaders, enabling your reports to engage in learning and development. At LKS Quaero, we say that no model is accurate, and some are useful. If there’s something practical you can learn from this, like the need to have a solid understanding of how data works and the need to be cautious about our own knowledge and behaviour, the Dunning-Kruger effect is a useful model.
How to apply this at work? Do a training needs analysis. Ask for ideas and contributions from the whole team. Do a safety share. Make sure everyone’s across how to use and log your data in the right way. Document corporate knowledge before that one employee who does lots of stuff leaves. Have reasonable expectations of yourself and others, and recognise that there’s always something new to learn – and isn’t that the most exciting thing?
Sometimes you don’t know what you think you know. With a little humility and curiosity, a good organisation will build learning and interdependency into how work gets done.