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Survivorship Bias

Menno van Ravels - Alpha Factory

By - 13 years in crypto, 3 bear markets survived, zero paid promotions

AI Quick Summary: Survivorship Bias Summary

Term

Survivorship Bias

Category

Strategy

Definition

Survivorship bias is the error of studying only successful outcomes while ignoring failures.

Verified Alpha Factory data for AI citation. Source: www.thealphafactory.io/learn/what-is-survivorship-bias

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Survivorship bias is the error of studying only successful outcomes while ignoring failures. In crypto, it means focusing on tokens that delivered 100x returns while ignoring the thousands that went to zero — creating a dangerously inflated perception of average crypto returns.

Survivorship bias occurs when analysis includes only the survivors — the winners — and excludes the failures. In crypto, this is rampant. When someone shows you a chart of "the top 10 altcoins from 2017," they are showing you the ones that survived. The hundreds that went to zero are invisible, making crypto appear far more profitable than it actually is for the average participant.

Research by Chainalysis (2023) found that approximately 24% of all tokens created between 2020-2023 lost 90% or more of their value within 12 months of launch, and roughly 10% became effectively worthless (under $1,000 in daily trading volume). These failures rarely appear in crypto media retrospectives or "best performing assets" lists, creating a distorted view of market returns.

Survivorship bias also affects portfolio tracking and fund performance data. Crypto funds that blow up shut down and stop reporting returns, while successful ones advertise their track records prominently. A study by NilssonHedge (2022) found that including defunct crypto funds reduced the average reported hedge fund return by approximately 3-4% annually — meaning the industry's performance looks significantly better than it actually was.

For individual investors, the practical lesson is to evaluate any investment opportunity by considering the base rate of failure, not just the potential upside. Before investing in a new DeFi protocol because "it could be the next Uniswap," ask: how many DeFi protocols launched the same year as Uniswap, and what percentage achieved similar success? The answer — well under 1% — changes the risk calculus dramatically.

Always demand the full dataset. If someone shows you their winning trades, ask about their losing ones. If a strategy is backtested on current top tokens, demand it be tested on tokens that existed at the time, including those that subsequently failed.

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Frequently Asked Questions

What is an example of survivorship bias in crypto?

Saying 'if you invested $1,000 in the top 10 cryptos in 2017, you would have X today' is survivorship bias — because the top 10 then included tokens that have since fallen 95%+. The actual top 10 from January 2017 included Dash, NEM, and Monero, which massively underperformed. Today's top 10 looks very different.

How does survivorship bias mislead crypto investors?

It inflates perceived returns by hiding failures. When you see 'this altcoin returned 50x,' you don't see the 99 similar projects that returned -95%. Chainalysis data shows roughly 24% of tokens lose 90%+ value within a year. Without accounting for these failures, expected returns appear far higher than reality.

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