Representativeness Bias and Regression to the Mean

🧠 What Is Representativeness Bias?

It’s the tendency to judge the probability of an event based on how representative or "normal" it looks, instead of relying on actual data. For example, if you see the sequence red, black, red, black, you might think “red should come next” because it seems like a balanced pattern.

🔁 How It Applies to Roulette

Players often expect short-term results to reflect long-term probabilities. But roulette can show long, random streaks without following any apparent logic.

This bias leads us to believe that a sequence “can’t continue” for long — which is false. The wheel doesn’t remember past outcomes.

📉 What Is Regression to the Mean?

This is the phenomenon where, after extreme results, future outcomes tend to move closer to the average.

For example, if black comes up 10 times in a row, many players believe red must be "due." But roulette doesn’t guarantee balance — each spin is independent.

⚠️ Common Misinterpretations

“One dozen has hit too much, the other is due.” ❌
“Odd numbers are falling behind.” ❌
“It always returns to average, so bet on what’s missing.” ❌

These beliefs mix representativeness bias with a flawed understanding of regression to the mean.

💡 Conclusion

Recognizing these biases helps avoid mental traps. Roulette is random and doesn’t follow human-like patterns. What has happened doesn’t affect what comes next.