Quoting benkuhn.net:

Light-tailed distributions most often occur because the outcome is the result of many independent contributions, while heavy-tailed distributions often arise from the result of processes that are multiplicative or self-reinforcing.

in a light-tailed distribution, outliers don’t matter much. The 1% of tallest people are still close enough to the average person that you can safely ignore them most of the time. By contrast, in a heavy-tailed distribution, outliers matter a lot: even though 90% of people live on less than $15,000 a year, there are large groups of people making 1,000 times more. Because of this, heavy-tailed distributions are much less intuitive to understand or predict.

The most important thing to remember when sampling from heavy-tailed distributions is that getting lots of samples improves outcomes a ton.

sampling from a heavy-tailed distribution can be extremely demotivating, because it requires doing the same thing, and watching it fail, over and over again: going on lots of bad dates, getting pitched by lots of low-quality startups, etc. An important thing to remember in this case is to trust the process and not take individual failures, or even large numbers of failures, as strong evidence that your overall process is bad.

Often, you’ll have a choice between spending time on optimizing one sample or drawing a second sample—for instance, editing a blog post you’ve already written vs. writing a second post, or polishing a message on a dating app vs. messaging a second person. Some amount of optimization is worth it, but in my experience, most people are way over-indexed on optimization and under-indexed on drawing more samples.

it’s very important for your filters to be as tightly correlated with what you actually care about as possible, so that you don’t rule candidates out for bad reasons.

A subtlety here is that the traits that make a candidate a potential outlier are often very different from the traits that would make them “pretty good,” so improving your filtering process to produce more “pretty good” candidates won’t necessarily increase the rate of finding outliers, and might even decrease it. Because of this, it’s important to filter for “maybe amazing,” not “probably good.”

[T]he best startup ideas seem at first like bad ideas. I’ve written about this before: if a good idea were obviously good, someone else would already have done it. So the most successful founders tend to work on ideas that few beside them realize are good. Which is not that far from a description of insanity, till you reach the point where you see results.

He drew two intersecting circles, one labelled “seems like a bad idea” and the other “is a good idea.” The intersection is the sweet spot for startups. This concept is a simple one and yet seeing it as a Venn diagram is illuminating. It reminds you that there is an intersection—that there are good ideas that seem bad. It also reminds you that the vast majority of ideas that seem bad are bad.

it’s very common for people sampling from heavy-tailed distributions to focus on “ruling out” candidates instead of “ruling in,” which is likely to be a bad approach for similar reasons. In dating, for instance, people often have some sort of checklist they want a potential partner to satisfy, where most of the checkboxes (say, professional background) rule out lots of people but are only weakly correlated with long-term compatibility.

Once, on a day where I felt like I knew something, I declared that I would be okay with dating anyone who wasn’t vegan or an actress. It was clear to me that cheeseburgers were crucial to my happiness, and that I’d have a hard time getting close to a professional emotion simulator. Now I have a wife who is both a vegan and an actress, with whom I’m extremely happy. I can still recall, with shocking clarity, the moment three hours after I met my wife, when I offered her a piece of chicken. “Actually, I’m vegan,” she said. “Well,” I said to myself, “I suppose I am fucked now.” The night air was glimmering, love was all around, and I mentally edited out many chunks of animal protein in the future.

In fact, it’s generally true that it’s easier to filter for downsides than upsides, because downsides are more legible.

I’ve observed many other people who seem like they could achieve an outlier outcome fall into the same trap of “settling”—in job searches, in interviews, in dating, and in any other heavy-tailed situation. On average, I expect most people would benefit from rejecting more early candidates in all of these.

One reason you might be reluctant to do this is the worry that, if your job/candidate/relationship is actually the best you can hope for and you reject them, you’ll never find another equally good one. For this, I think it’s helpful to cultivate an abundance mindset. If you found your current job after two months of searching, then, unless you did something hard-to-replicate during those two months (e.g. call in a bunch of favors that you no longer have the social capital to do again), you should expect to be able to find an equally good opportunity in the future by putting in an equal amount of work.
Of course, that’s just a prior that you should update away from if your current job is an outlier. But most people are much more likely to overestimate the outlierhood of their current job than underestimate it.

it’s helpful to think ahead about what you’d expect a potential outlier to look like

often the best you have to go on is your first-principles reasoning: does it seem like the things you’re filtering on are tightly correlated with actual outlier-hood? Are you discarding samples for silly reasons?

To have a working process for sampling from a heavy-tailed distribution, you need to solve two problems:
A good way of evaluating whether a sample is an outlier A good way of drawing samples

good process for searching for outliers look like?
Take lots of shots on goal. The more samples you have, the more likely you’ll find an outlier. Know what to look for: try to figure out how good of an outcome is possible, so you know when to stop. Find ways to evaluated candidates that are well-correlated with what you care about. Filter for “maybe amazing,” not “probably good.” When possible, try to sample and evaluate candidates quickly, so that you can iterate on your sampling process more quickly. Don’t get discouraged when you do the same thing over and over again and it mostly doesn’t work!