Survivor Bias

In statistics, there is a concept called “survivor bias,” which refers to researchers focusing only on the common characteristics of “survivors” while ignoring information from those “who failed.”

A classic example is that during World War II, the mathematician Abraham Wald was tasked with studying how to reinforce the armor of British bombers. On the aircraft that returned, the bullet holes were mainly concentrated on the wings and the tail, but Wald believed the cockpit and fuel tanks should be reinforced, because bombers that were hit in those areas never made it back.

The same logic also applies to books that talk about entrepreneurs’ secrets to success—blindly copying the advice from those books doesn’t mean you can replicate success; what’s more valuable is to analyze the mistakes made by companies that went bankrupt.

In our circle, it’s the same: everyone keeps focusing on those few, most sensational success stories. For example, someone may have made a few million on SHIB or NFT projects, but very few people analyze what went wrong with those bankrupt exchanges and funds: fraud, high-leverage trading, and failures in risk control.

Learn from other people’s mistakes—sometimes the cost of your own mistakes can be too heavy!