My experience with new CS grads was that most of them greatly overestimated what they knew, or alternatively, underestimated how much they did not know.

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Jensson 6 hours ago | parent | next [-]

My experience with every person was that most of them overestimate what they know regardless of experience level. You just notice that more in new grads since its easier to tell when people are wrong about simple things than when they are wrong about more difficult things.

Software engineers tend to repeat the mantra "you cannot make accurate time estimates". That is true regardless of experience level, and everyone seems to be off by about the same amount. So there we have evidence that people overestimate their skills at every level, and its not that different.

	▲	teekert 2 hours ago \| parent \| next [-]
		It’s my experience that I should over-estimate what I know otherwise I miss out on assignments that take me about 1-2 focused days of studying to get to sufficient level. Ie I was once perfect for a project except for point 7 out of 10 which was experience with Keycloak (if you’re higher via an HR dept it’s even worse, they just tick boxes, who cares if you are smart and have broad knowledge). So I’m now proudly Dunning-Krugering around, and use LLMs for super-charged learning. If I underestimated something I take the cost/time myself. So far it hasn’t happened to a significant degree.
	▲	shapefrog 5 hours ago \| parent \| prev \| next [-]
		gell mann effect leads me to pay closer attention and then its game over
	▲	5 hours ago \| parent \| prev [-]
		[deleted]

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al_borland 6 hours ago | parent | prev [-]

This then boils down to people being generally bad at estimating their own level of ability.

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xboxnolifes 6 hours ago | parent [-]

Which is exactly what the Dunner-Kruger effect is. Knowledgeable people statistically underestimating their knowledge and non-knowledgeable people overestimating their knowledge.

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burpingtree 3 hours ago | parent [-]

It that’s the whole point that it’s just a mirage and is the same with random data. The people on the low end can’t underestimate their results as much and the people on the upper end can’t overestimate their results as much. That will come out of any correlation that is not perfectly correlated, which is why the article talks about it being replicable with random data. An interesting graph that would demonstrate a novel effect would be something nonlinear.

	▲	Dylan16807 28 minutes ago \| parent [-]
		There are specific distributions that can be explained by random noise, and other distributions that cannot be explained by random noise. But even if it is entirely caused by normal distribution variance, that doesn't make it a mirage, it gives you a reason.