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Comment by dcolkitt | Hacker News Reader

Can't agree with this enough. The most consistent finding in industrial psychology is that general cognitive ability is by far the best predictor of job performance in virtually every role ever tested.

Even jobs that don't seem cognitively demanding, like janitors or infantry. Higher IQ candidates almost universally do better, even if they start with much less experience.

The takeaway as it applies to modern software hiring, is that skillset is way overemphasized. It's quite common to see job ads that heavily focus on the company's specific tech stack. ("Must have experience with Rails, React, Travis, and AWS")

It's much better to cast a much wider net, and try to find the smartest people anywhere. High IQ people can easily re-tool their specific skillset. What's interesting is this is much closer to how the most successful tech firms, like Google, tend to hire.

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The research says structured interviews and work-sample tests have similar predictive power and all together they have yet more. The takeaway shouldn’t be IQ über alles.

The research shows that structured interviews do have better predictive performance than unstructured interviews. But that effect is entirely mediated by their higher correlation with IQ.

In other words, structured interviews are better because they're less noisy measures of intelligence. The takeaway very much is IQ uber alles.

[1] https://digitalcommons.unomaha.edu/cgi/viewcontent.cgi?refer...

I am not familiar with the research, and don't have time to review it right now. However, it seems like common sense to me that there are some factors beyond intelligence that matter such as motivation, interpersonal skills and character traits.

I think you're right but those are really hard to measure in an interview. Motivation especially.

Your grasp of the research is out of date.

From the 1998 meta-analysis

> The most well-known conclusion from this research is that for hiring employees without previous experience in the job the most valid predictor of future performance and learning is general mental ability ([G M A ], i.e., intelligence or general cognitive ability; Hunter & Hunter, 1984; Ree & Earles, 1992).

> Work sample measures are slightly more valid but are much more costly and can be used only with applicants who already know the job or have been trained for the occupation or job.

http://www.flume.com.br/pdf/Scmidt_The_validity_and_utility....

From the 2016 meta-analysis by the same author.

> Overall, the two combinations with the highest multivariate validity and utility for predicting job performance were GMA plus an integrity test (mean validity of .78) and GMA plus a structured interview (mean validity of .76)

Work sample tests do not work as well as the old research suggests

Table with effects linked below

https://m.imgur.com/jULz7RE

More recent paper

https://www.google.com/url?sa=i&source=web&cd=&ved=2ahUKEwi1...

I can't agree with this. High IQ can't be everything. Wouldn't this value experience and knowledge (like everything you learned in university) at zero? Imo there are a lot of great/amazing and productive Software Engineers that wouldn't do well in a whiteboard algorithm interview. The people that tend to do well, are the ones that have a lot of practice with the kind of problems.

I read the parent as talking about narrow skill sets that a smart and motivated individual can pick up pretty quickly. If you're looking for a senior developer you probably don't want to hire someone who has never programmed even if they're widely acknowledged as really smart and an expert in ball bearing design.

And obviously applies in lots of areas. If I'm looking for someone to head up a digital marketing or marketing research initiative, someone whose only work experience is software development is probably not the best choice no matter how smart and motivated they are.

OTOH, as you get to the level where people are more managers than practitioners, their specific skill sets presumably start to make less of a difference.

Added: And, as peer noted, people do shift careers significantly all the time. I never really directly used my undergrad (or grad) engineering degrees all that much.

> Wouldn't this value experience and knowledge (like everything you learned in university) at zero?

Having switched professions twice, I now use practically nothing of what I learned in university from about second year forward. Math and to some extent physics are still relevant, but that's pretty much it.

It might be painful to admit, but the practical value of that rather specialized knowledge that took me several years of hard work to obtain is pretty much zero now.

> It might be painful to admit, but the practical value of that rather specialized knowledge that took me several years of hard work to obtain is pretty much zero now.

It's sad that your professors didn't tell you beforehand that you were going to learn how to learn during that time, not just study a particular technology stack.

I thought that was fairly common knowledge about a great deal of university education.

This bears the question- are classes based around studying a particular technology track the most effective way to teach somebody how to learn?

The short answer is probably no. And the fact is that top universities mostly aren't so focused on teaching whatever language or framework is the flavor of the day.

That said, you need tools of some sort if you're going to actually build things as opposed to just learning, say, algorithms in pseudo-code. And it probably makes sense to use some fairly standard language to do so. There's not much point in making things deliberately obscure by making students use some language that the professor designed for his PhD thesis.

Studying math is the best way to improve your logical thinking abilities.

Learning how to learn is a specialized set of skills that weren't taught at the university level when I was in a school.(2010)

I bet it wasn't a total waste. The real point of school, particularly university, is to learn how to learn rather than learn a bunch of facts or tools. Attending university probably made it a lot easier for you to switch professions twice.

I learned how to learn way before graduating.

The waste was close to total, but this is only obvious in hindsight. Given the information I had at the time my choices were rational.

> Wouldn't this value experience and knowledge (like everything you learned in university) at zero?

I haven't read the research myself but if they are only looking at interviews, this could be explained by the fact people with absolutely no prior knowledge would be filtered before reaching the interview.

So, what we know from industrial psychology isn't that experience doesn't add value. It's that high IQ people learn faster. This makes sense when you remember that intelligence is broadly defined as the ability to learn.

While Alice may have fewer years of experience than Bob, she might have effectively more experience if she absorbed understanding at a faster rate. An unexperienced, high-intelligence person usually starts at an initial disadvantage, but manages to "get up to speed" quickly.

This also underscores the particular importance of intelligence in software. The field is constantly awash in new technologies, where nobody has had time to accumulate extensive chronological experience. So, it's really important to find people that can absorb new concepts quickly.

[1] https://www.sciencedirect.com/science/article/pii/S019130851...

My interpretation is that among the pool of people who meet the minimum requirements and are interested in that position/career, IQ is the best predictor. If you're just picking random high-IQ people off the street, I don't think they'll do very well in a software engineering job.

What is the correlation between IQ tests and algorithm interviews? Do people with better IQ scores do better on algorithm interviews?

With or without months of preparation on some of the exact problems interviewers use “grinding LeetCode”?

I wouldn't expect that. Algorithm interviews as they're practiced tend to favor memory abilities and not cognitive. It's hard to come up with standard algorithms on the spot if you haven't been exposed to them.

I do know that cognitive abilities and long term memory abilities aren't correlated. Cognitive abilities and short term / working memory are positively correlated though.

The trick here would be separating high IQ people who have prior algorithmic experience from those who do not, because that's a much bigger influence on performance.

I work at Google, based on personal experience I would say it does a good job of setting a minimum bar in terms intelligence and coding ability. However, it might reject some people unfairly.

A little bit of preparation helps a lot, but beyond a certain point preparation doesn't help anymore. All interview questions require solving a new problem in the interview itself.

Depending on the interviewer and question, the difference between a hire and no-hire recommendation can be quite marginal. There is a huge luck factor involved.

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