Survivor bias (also known as survivorship bias) is a logical error that occurs when we focus only on the people or things that “survived” a process, ignoring those that didn’t, which can lead to false conclusions.

🔍 Classic Example:

During World War II, the military sought to armor airplanes based on the locations of bullet holes found on returning planes. But statistician Abraham Wald pointed out:

The military was only analyzing planes that came back. The ones that didn’t return likely got hit in areas not shown in the data—like the engines.

So he advised reinforcing the areas without bullet holes, not the ones with them.

💡 Why It Matters:

Survivor bias can:

• Skew data analysis

• Lead to overestimating success rates

• Make failures invisible

📌 Common Real-Life Examples:

1. Business Advice

You hear stories about college dropouts becoming billionaires (like Steve Jobs or Mark Zuckerberg), but forget the thousands who dropped out and didn’t succeed.

2. Fitness Influencers

You see amazing transformation stories, but not the many who trained hard and didn’t get the same results.

3. Investing

We praise the stocks that performed well, but ignore the ones that went bust and quietly vanished.

✅ Lesson:

To make smart decisions, don’t just study the winners.

Ask: Who didn’t make it? Why?

That’s where the real insight often lies. 

Read more about the different kinds of bias here