I’m going to answer the broader topic: “how do I know what mastery I have in a subject area?”
Are you able to use parts of a problem in that area to fill in the gaps from experience to find a solution?
Are you able to identify why an attempted solution to a problem in that area won’t work, and why?
Do people refer to you with difficult questions about that area?
Are there areas in that subject you feel don’t make complete sense, that when you ask subject matter experts they deflect (or better, admit they don’t understand it either)?
Can you identify problems in that subject area that have not been solved, and can, if not take the time to solve them, identify how you would go about solving them?
Each step here shows an increasing level of mastery in a topic.
It’s not a single curve, but a repeating wave. Every “aha!” moment is a peak and every “fuck this” is a dip. Source: learning music production for years.
If you are wondering where you are on the curve, you likely aren’t too bad. Simply recognizing that you may not know everything is a bit step along the curve
This disptoves any statistical anonmaly that suggests the majority of people fall into the “dunninng-kruger effect”; it doesn’t disprove the existence of ignorant people who overestimate their understanding or knowledgeable people who understimate their understanding.
Thus OP’s question becomes: how do you know if you’re one of those people?
You know what you know, and you don’t know what you don’t know. If you don’t know what you don’t know, it would follow that you wouldn’t understand how much you don’t know either.
IMO its a philosophy battle, just for the sake of battle. Assuming ignorance, and striving to learn more, learn from your mistakes, and self assess reign supreme - imo.
On the surface, if I understand what you’re asking correctly, no. From what I’m understanding of these articles, the dunning Krueger effect never did what it set out to accomplish, but something along the lines of people who don’t know much have a much larger amount of things that they themselves aren’t even aware of not knowing… if that makes sense? I can try to reword later tonight after I finish with work
I believe that I may have originally gotten wind of this from a less wrong post IIRC. Pretty interesting stuff. Imagine if we trained an AI on doing science and peer review, and set it loose on the suite of research findings and had it report back all the BS…
You need to actively and continuously seek out negative but constructive feedback. It’s the only way to keep an objective perspective on your capabilities.
Organizations that are actually serious about quality have processes for this. Organizations that are not pretend to have processes for this ;)
It’s mostly about knowing your limits of knowledge.
If you don’t know about your limits, you’re probably a newbie of the subject. You don’t grasp how much more there’s to learn. You think you’ve learned almost everything.
If you know about your limits, you probably know a lot about the subject. You have learned a lot, but you understand there’s still much more to learn.
The basic effect Dunning-Kruger is about is real and apparent everywhere. The specific formulation as stated from that pair may have some errors but throwing away the idea due to poor science isn’t smart.
To establish the Dunning-Kruger effect is an artifact of research design, not human thinking, my colleagues and I showed it can be produced using randomly generated data.
First, we created 1,154 fictional people and randomly assigned them both a test score and a self-assessment ranking compared with their peers.
So, the experiment with completely fake data disproves Dunning-Kruger? How is this science?
I still personally believe that the basic effect described by Dunning-Kruger does in fact exist on some level. If it’s not due to cognition, that seems to imply that essentially everyone at every intelligence level accurately estimates their own intelligence, that would be weird.
Dunning-Kruger became popular because it gave a name to an apparent phenomena.
That article (or rather, the article linked in that article) doesn’t contradict your intuition, just a specific interpretation of that intuition. The randomly generated data puts everyone around 50%, which is indeed what you would expect from randomly uniformly generated data. So the similarity that the generated data presents is supposed to imply the conclusion that “everyone thinks they’re about average, so their judgement is no better than randomly guessing (assuming that the guesses are uniformly distributed)”, which is a subtle difference from “dumb people think they’re smart” - the latter attributes some sort of “flawed reasoning” to one’s self-judgement, while the former specifically asserts that there is absolutely no relevant self-judgement going on.
edit: You would also be correct that this doesn’t disprove the previous explanation, it just offers an alternative explanation for the observed effect. The fact that data matches up with a generated model definitely does not prove that it is not actually caused by something else, which is one of the criticisms of that viewpoint. It is obviously easier to rigorously demonstrate a statistical explanation than a psychological explanation of course, due to the nature of the two different fields.
That is not quite how that works. The effect can apply to separate fields of knowledge or separate skill sets separately. You might actually know what you are talking about when it comes to e.g. plumbing but only think you do when it comes to e.g. IT systems.
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