What is the Gambler’s Fallacy and Why is it so Dangerous?

Content Team 1 year ago
What is the Gambler’s Fallacy and Why is it so Dangerous?

What is the Gambler’s Fallacy and Why is it so Dangerous?

Have you ever been to a casino and witnessed someone betting big after a long losing streak, believing that their luck is about to turn around? Or maybe you’ve heard of lottery players choosing their numbers based on the frequency of past winning numbers, thinking that those numbers are “due” to appear again soon. These are examples of the gambler’s fallacy, a common cognitive bias that affects many people. In this article, we’ll learn about the gambler’s fallacy, explain how it works, and why it can be a dangerous trap for those who believe in it.

What is the Gambler’s Fallacy?

The gambler’s fallacy is a cognitive bias that occurs when people believe that past events affect the probability of outcomes of future events. Specifically, the fallacy is the belief that if a particular event has not occurred for a while, it’s more likely to occur in the near future. Conversely, if a particular event has occurred frequently, they may believe it’s less likely to occur again soon.

For example, consider a person who is flipping a coin. If the coin lands on heads five times in a row, the gambler’s fallacy would suggest that it’s more likely to land on tails on the next flip to “balance out” the sequence. However, in reality, the probability of a coin landing on heads or tails is always 50%, regardless of the previous flips.

Similarly, in a roulette game, if the ball lands on black several times in a row, a person may start betting on red, thinking it’s “due” to come up next. Again, this is fallacious reasoning, as the probability of the ball landing on black or red remains the same for each spin. As you can imagine, the gambler’s fallacy can lead people to make irrational decisions and can have serious consequences, particularly when it comes to gambling for real money.

Examples of Gambler’s Fallacy

The gambler’s fallacy can manifest in many different contexts, from sports betting to the stock market. Here are a few examples of the gambler’s fallacy in action:

  • The Monte Carlo Fallacy: In 1913, a roulette wheel in the Monte Carlo casino in Monaco landed on black 26 times in a row. As a result, many people started betting on red, thinking that black was “due” to come up less often. This event has become a classic example of the gambler’s fallacy, as the probability of the ball landing on black or red was always the same for each spin.
  • The Hot Hand Fallacy: The hot hand fallacy is the belief that a player who has had a string of success is likely to continue being successful. However, research has shown that this belief is often unfounded. In other words, it’s based on human psychology rather than facts and does not affect the game’s outcome. On the other hand, there are studies that support the theory of the hot hand in some sports.
  • The Lottery Fallacy: Here’s another gambler’s fallacy example. In lottery games, some people choose their numbers depending on the frequency of past winning numbers. For example, if the number 7 has not been drawn for a while, people may believe that it’s “due” to come up soon. However, this is fallacious reasoning, as each lottery drawing is random and independent, and the past winning numbers don’t affect the future ones.

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Tips for Avoiding gambler’s Fallacy

Avoiding the gambler’s fallacy might be difficult, but there are a few tips and techniques you can use to make a better decision:

Trust the facts: Rather than relying on your gut feeling, try to learn all the facts. Take a step back and evaluate the data while betting on sports or investing in the stock market. This can help you avoid making rash decisions based on incorrect assumptions.

Understand probability: Probability might be a difficult topic, but understanding the basics can help you avoid the gambler’s fallacy. Remember that the likelihood of an event occurring is independent of past events because each event is unique.

Develop a strategy: It’s critical to have a clear plan in place when betting. Ensure you understand the odds, rules, and terms, as this can increase your chance of winning. Also, don’t forget to set a budget and stick to it. This can help you make good choices using objective analysis.

Take a break: If you notice that you’re getting overly emotional, taking a break might help you gain perspective. This can help you avoid irrational decisions and emotional responses rather than objective evaluation.


The gambler’s fallacy is a common mistake that can lead to poor decisions and significant financial losses in gambling. By using probability theory and objective analysis, you can avoid falling into a trap.

Remember that each event is unique, and there is no pattern or predictable outcome when it comes to these things. Make informed decisions based on facts and avoid relying on superstitions or random assumptions. With a clear mind and determination, you can become a successful gambler and avoid the pitfalls of the gambler’s fallacy.


Why does the gambler’s fallacy occur?

The gambler’s fallacy occurs because people look for patterns in the world around us, even when they don’t actually exist. They assume past events can somehow influence future outcomes, because of cultural and social factors, such as superstition. In reality, each event is independent and has its own unique probability.

Who invented the gambler’s fallacy?

The earliest mention of the gambler’s fallacy dates back to the 17th century when Italian mathematician Gerolamo Cardano wrote about the concept in his Book on Games of Chance. The term “gambler’s fallacy” was coined in the 1950s by psychologist Amos Tversky and mathematician Daniel Kahneman in their groundbreaking work on decision-making and cognitive biases.

What is the gambler’s fallacy in statistics?

According to science and psychology, the gambler’s fallacy is a cognitive bias that occurs when people believe that past events can influence the probability of future events, even when the two events are independent of each other.

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