[SandersAK]

It's hard to edify and spend that time when you routinely flag and kill my comments.

You claim that HN is "better than this" but Naval's post is totally devoid of insight. It is hackneyed and the commentary responding to it is a reflection of that. If you want better commentary on the front page, then do a better job of making the front page articles worth talking about.

As to me claiming to know how Naval got rich - that's easy - he's a person who cashed in on a golden era of startups when it was easy to sell off bad properties for big money. Instead of realizing that he was extremely lucky to be at the right place and right time, with the privilege of having enough personal wealth to take those risks, he instead spends the rest of his career trying to justify his wealth and position. That's why all his advice and "insight" are the sort of trite self-help aphorisms that anyone can say and make it sound true.

You always ask others to be better on HN, but maybe take a step back and ask yourself if maybe the state of HN is a reflection not of the community but of the guidelines and leadership that drives it today.

as for me? I gotta get back to my day job ;)

[dang]

The last comment of yours that was flagged and killed was six months ago: https://news.ycombinator.com/item?id=20348091. It was done by users, and rightly so.

I have no opinion about Naval one way or the other, but your argument there is flawed. There were tons of people trying to do the same thing at the time. Plenty had privilege and money. Why didn't they all succeed too? To just say "luck" is a non-answer; that's assuming your conclusion. A real argument would need to show how it wasn't anything else.

[alexpetralia]

It might be useful to think of "luck" as an error term on a regression model, where the covariates are well-known factors (e.g. wealth, ambition, connections) and the error term is everything else.

Given that plenty had privilege and money (i.e. those are the covariates), and many didn't succeed, we'd expect those weren't significant variables.

Many likely had drive, connections, know-how as well. Many of them probably failed too. Again, insignificant covariates.

So what does that leave? The error term: luck.

In other words, during a tech boom like at the turn of the century, there are so many winners (often outsized, which distort many basic statistical assumptions, such as a normal distribution) that is almost futile to try and identify significant covariates.

To speculate here, often with the pretense of certainty, is more often reflective of post-hoc reasoning than actual science.

That, in my opinion, is why "luck" is an adequate answer. We can be fairly sure that certain covariates contribute to success over the long-term in life (i.e. we have a large sample size of being alive, and we can extrapolate from many other people who have lived). It is far harder to do this with nonce hype cycles (infrequent, low-sample size).

Chalking up more success to the error term, "luck", seems perfectly appropriate during such unusual times.

[dang]

If I understand you correctly, then "luck" is another word for "we don't know". That's reasonable, but then we shouldn't make strong claims to knowing.

There's a cruder version of this argument, according to which all success is luck. I hear that, or things that sound like it, a lot, but it's too simplistic and usually too self-serving to be plausible. Though many successful people will be the first to tell you they were lucky. (I always remember https://news.ycombinator.com/item?id=1621845)

[alexpetralia]

I think that's a fair opinion.

The only tweak I'd make is really around sample sizes and statistical significance.

All success _may_ be due to luck (the error term), but we can be fairly confident (say p < .001) it isn't. Said otherwise, we can be reasonably sure that some covariates (e.g. hard work, wealth, education) are significant, given a large enough sample size and we define what qualifies as success. One of course could quibble about these - do we really have enough statistical significance for each trait? - but the fact is that every person everyday operates with some intuitive understanding that these things matter. In other words, not all outcomes are due to luck.

This is much harder to do with small sample sizes or unusual occurrences. Statistics is based on frequencies, and if we have low frequencies (such as a tech boom), we should be much less confident in the significance of each variable.

This is, presumably, why people intuitively chalk up much success during these times as survivorship bias. It's not that it certainly, absolutely is; rather, it's that we're far less confident on which variables are significant and which aren't. The error term remains.

Again, attributing it to the error term, implies mostly that we shouldn't have too much confidence in our speculations on which traits are significant during such one-off events.