Investing as Selective Sampling

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Hey Investors,

Many of you are engineers or scientists, so I want to frame this through a lens you already trust: statistics.

This isn’t about market narratives, stock picks, or forecasting skill.
It’s about sample size, variance, and why outperformance must look lopsided.

If you dislike statistical intuition, feel free to skip this one.
If you enjoy first-principles thinking, this framework explains far more about investing outcomes than most financial media ever will.

A brief review of statistics

In statistics, sampling is simple:

• If you observe an entire population, you see the true distribution.

• If you observe only a subset, you get an estimate of that distribution.

The estimate improves as the sample gets larger.

This is why large samples feel:

• Stable

• Predictable

• Boring

And small samples feel:

• Noisy

• Unfair

• Extreme

That’s not psychology.
That’s math.

Sample size controls variance — and extremes live in small samples

Here’s the key idea that matters for investing:

Smaller samples:

• Deviate more from the population average

• Contain more extreme outcomes (high and low)

• Are where outliers show up

This is sometimes discussed under the “law of small numbers,” but the core point is simpler:

Large samples dampen extremes.
Small samples surface them.

From random sampling to selective sampling

So far, everything assumes random sampling.

But in the real world — and especially in investing — sampling isn’t random.

Instead, we do something more interesting:

We:

• Start with a large universe

• Apply filters

• Narrow the sample intentionally

• Accept higher variance in exchange for exposure to extremes

This is the bridge to investing.

The S&P 500 is a very large sample

There are thousands of publicly listed U.S. stocks.

The S&P 500:

• Selects 500 of them

• Produces a large, stable sample

• Approximates the overall market distribution

That’s why index funds:

• Work extremely well

• Deliver reliable, average outcomes

• Smooth both winners and losers

They are doing exactly what large samples are supposed to do.

Venture capital is a much smaller sample

Venture capital operates on the same population logic — but with a much smaller sample size.

A VC fund:

• Looks at thousands of startups

• Invests in maybe 20–40

• Expects most to fail or do “okay”

• Relies on a few extreme winners to drive all returns

This works because returns follow power laws:

• Losses are capped

• Upside is unbounded

• A tiny number of outcomes dominate the total

VCs aren’t trying to be right often.
They’re trying to be present when outliers occur.

My own portfolio follows the same pattern

When I look back at my own investing results, the distribution is always the same:

• A few losers

• Many average or market-like outcomes

• A small number of exceptional winners

Only the exceptional winners beat the index.

Everything else just exists to:

• Stay invested

• Avoid catastrophic loss

• Not cap upside

The portfolio outperforms because of variance, not despite it.

Angel syndication: VC-style sampling for individuals

Most individuals can’t run a VC fund.

But angel syndication lets you replicate the structure:

• Access a larger opportunity set

• Make smaller bets across many deals

• Accept that most won’t matter

• Let a few outliers do the work

It’s not about prediction.
It’s not about hit rate.
It’s about constructing a sample that allows extreme outcomes to matter.

The mental shift

Indexing is large-sample thinking.
Outperformance comes from selective small-sample exposure.

You’re not trying to beat the market every year.
You’re trying to be positioned for the years — and the investments — that matter disproportionately.

That’s the pyramid.
That’s the funnel.
That’s the math.

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