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Comment by oneplane | Hacker News Reader

It's very weird how some of those tech interviews are done. I've luckily never had to do one of those, but from the YouTube videos of training interviews and example interviews, almost everyone seems to be asked to implement a slight variation on something to be found in a Data Structures & Problem Solving book by Weiss. Great if that is what you'll be doing all day, but regurgitating some way to get the smallest number in a list or making a directed graph for a word morphing joke hardly represents anything other than a persons skill to repeat what someone else did and wrote down decades ago.

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I had an interview question a few weeks ago (writing a JSON parser from scratch) that required me to remember that in python that a for loop allows you to iterate through not just a list but also each of the characters in a string. I forgot about that and spent several minutes wasting time trying to think of a regex-based solution before I remembered that was an option.

Why should an interview question reward you/require you to remember this? If I actually had to solve a real-world problem similar to this, I would google it, see a solution that iterated through the characters with a for loop and go “oh yeah, I forgot you could do that. Duh.” Why am I not allowed to google dumb things? On most days, my brain is focused on high-level abstractions that tie together two or more tools via disparate APIs, why the hell should I remember or think about for loops?

So everyone knows that the interview isn't perfectly realistic--it's a simulation.

Consider that when athletes are scouted, they are usually not asked to play in a full game of their sport. But instead certain stats like 'how fast can he run 100m' and 'how high can he jump' are used.

Sure, in a real game you will never run 100m in a straight line on asphalt--but the speed at which one runs 100m in a straight line is a good proxy for how fast one can maneuver during real play.

That's not a very good example. Athletes are being watched for 4-8 years before they are ever drafted. Colleges are watching all the high school games, and Pros are watching all the college games.

Perhaps--but it's the analogy I came up with while writing this comment. If you can replace it with a better one, go ahead.

Sometimes analogies are not really meant to be perfect, but just simpler methods of communicating information.

For almost any sport this is a spectacularly bad metric. Just because it can be measured doesn’t make it meaningful. FWIW, the “fitness” required for sportspersons is a very sport-dependent diffuse constellation of factors.

> Consider that when athletes are scouted, they are usually not asked to play in a full game of their sport. But instead certain stats like 'how fast can he run 100m' and 'how high can he jump' are used.

Teams rarely sign players based solely on measures like this, and those that do frequently regret it.

Additionally, the nature of contracts in professional sports are very different from regular employment. Players are cut or traded frequently, often find themselves on very team-friendly deals when unproven, and are granted employment in time-limited blocks.

I know. It's just an analogy. You can replace it with a better one if you want.

The general point I am making is: these are not trying to measure actual job tasks, but instead measuring something that (they hope) is closely related to, or correlated with one's ability to perform the job. The idea is that if one can do these sorts of problems, one can probably do the actual job at hand.

> (they hope) is closely related to, or correlated with one's ability to perform the job. The idea is that if one can do these sorts of problems, one can probably do the actual job at hand

This is a claim about the intentions behind these practices.

Intentions aside, the whole point of this perennially recurring discourse is about effectiveness — that certain overused methods of filtering job candidates are ineffective on their own terms — and negative side effects — that they're biased against people with non-traditional / from underrepresented backgrounds.

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