Why Humans Are Bad at Probability (Cognitive Bias)

Crash Course: Why Humans Are Bad at Probability (Cognitive Bias)

Why Humans Are Bad at Probability (Cognitive Bias)

Introduction Did you know that humans are terrible at predicting the odds of everyday events? Like, really bad. We're so bad that we'd rather believe in a 1-in-a-million chance of winning the lottery than face the fact that we're probably going to get stuck in traffic on the way to work.

The Core Idea Cognitive biases are systematic errors in thinking that affect the way we perceive and understand probability. These biases are so ingrained in our brains that we often don't even realize we're making mistakes. In this crash course, we'll explore why humans are bad at probability and what it means for our daily lives.

Key Facts & Figures

• The Gambler's Fallacy: In 1913, mathematician Karl Pearson discovered that people tend to believe that a random event is more likely to happen because it hasn't happened recently. ⚠️
• The Monty Hall Problem: In 1966, game show host Monty Hall offered contestants a chance to win a car by choosing one of three doors. The probability of winning was 1/3, but contestants often chose the wrong door because they thought the odds had changed. ?
• The Availability Heuristic: In 1973, psychologists Amos Tversky and Daniel Kahneman found that people overestimate the importance of vivid, memorable events (like plane crashes) and underestimate the importance of more common events (like car accidents). ?
• The Base Rate Fallacy: In 1983, psychologist Baruch Fischhoff discovered that people often ignore the base rate of a probability problem and focus on irrelevant information. For example, if a doctor says you have a 90% chance of having a certain disease, you might think you're almost certain to have it – but what if the base rate of the disease is only 1%? ?
• The Hindsight Bias: In 1977, psychologists Baruch Fischhoff and Lawrence Beyth found that people tend to believe, after an event has occurred, that they would have predicted it. This is known as the "I knew it all along" effect. ?
• The Law of Large Numbers: In 1713, mathematician Abraham de Moivre discovered that as the number of trials in a probability experiment increases, the average outcome will converge to the expected value. But humans often ignore this law and make predictions based on small samples. ?
• The Representative Bias: In 1954, psychologist Edward Thorndike found that people tend to judge the probability of an event based on how closely it resembles a typical case. For example, if you meet someone who is a good-looking, successful businessperson, you might think that most businesspeople are like that – even if the base rate is much lower. ?
• The Anchoring Bias: In 1974, psychologists Amos Tversky and Daniel Kahneman found that people tend to rely too heavily on the first piece of information they receive (the "anchor") when making decisions. For example, if you're told that a house is worth $200,000, you might think it's a good deal even if it's actually worth $150,000. ?
• The Sunk Cost Fallacy: In 1984, psychologist Daniel Kahneman and Amos Tversky found that people tend to continue investing in a decision because of the resources they've already committed, even if it no longer makes sense to do so. For example, if you buy a ticket to a concert and it rains, you might still go because you don't want to waste the ticket – even if it's not worth the hassle. ?️
• The Availability Cascade: In 1995, psychologist Cass Sunstein and Richard Thaler found that people tend to follow the crowd and believe in a probability event because others believe in it, even if there's no evidence to support it. For example, if everyone around you thinks that a certain stock is going to go up, you might start to believe it too – even if the fundamentals don't support it. ?
• The Illusion of Control: In 1975, psychologist Ellen Langer found that people tend to believe they have more control over events than they actually do. For example, if you flip a coin and it lands on heads, you might think you influenced the outcome – even if it was just chance. ?
• The Self-Serving Bias: In 1974, psychologist Albert Bandura found that people tend to attribute their successes to their own abilities and their failures to external circumstances. For example, if you win a game, you might think it's because you're a great player – but if you lose, you might blame the other team or the referee. ?

Thought Bubble Imagine you're at a casino, and you're playing a game of roulette. You bet on red, and the wheel spins. The ball lands on... black! You're disappointed, but you think to yourself, "Well, I was due for a win. The wheel was just being unfair." But what if I told you that the probability of the ball landing on red or black is actually 48.65% each? That's right – the wheel is fair, and the outcome is just chance. But our brains are wired to think that we can influence the outcome, even when we can't. This is the illusion of control, and it's a powerful cognitive bias that affects us all.

Why This Matters * Financial decisions: Cognitive biases can lead to poor investment decisions, which can cost you money in the long run. * Healthcare: Biases can lead to misdiagnosis or overdiagnosis, which can harm your health. * Politics: Biases can lead to poor decision-making, which can affect the outcome of elections and policy decisions. * Education: Biases can lead to poor teaching methods, which can affect student outcomes. * Personal relationships: Biases can lead to misunderstandings and conflict with others. * Risk management: Biases can lead to poor risk assessment, which can put you and others at risk.

Crash Course Recap

• Humans are bad at probability because of cognitive biases.
• The Gambler's Fallacy is a common bias that affects our perception of probability.
• The Availability Heuristic leads us to overestimate the importance of vivid events.
• The Base Rate Fallacy ignores the base rate of a probability problem.
• The Hindsight Bias makes us think we would have predicted an event after it occurs.
• The Law of Large Numbers says that as the number of trials increases, the average outcome will converge to the expected value.
• The Representative Bias judges the probability of an event based on how closely it resembles a typical case.
• The Anchoring Bias relies too heavily on the first piece of information received.
• The Sunk Cost Fallacy continues investing in a decision because of resources already committed.
• The Availability Cascade follows the crowd and believes in a probability event because others believe in it.
• The Illusion of Control believes we have more control over events than we actually do.
• The Self-Serving Bias attributes successes to our own abilities and failures to external circumstances.

Quiz Yourself

1. What is the name of the bias that leads us to believe that a random event is more likely to happen because it hasn't happened recently? a) The Gambler's Fallacy b) The Availability Heuristic c) The Base Rate Fallacy d) The Hindsight Bias

Answer: a) The Gambler's Fallacy

1. What is the name of the game show host who offered contestants a chance to win a car by choosing one of three doors? a) Monty Hall b) Bob Barker c) Drew Carey d) Alex Trebek

Answer: a) Monty Hall

1. What is the name of the bias that leads us to overestimate the importance of vivid, memorable events? a) The Availability Heuristic b) The Representative Bias c) The Anchoring Bias d) The Sunk Cost Fallacy

Answer: a) The Availability Heuristic

1. What is the name of the bias that ignores the base rate of a probability problem? a) The Base Rate Fallacy b) The Hindsight Bias c) The Law of Large Numbers d) The Representative Bias

Answer: a) The Base Rate Fallacy

1. What is the name of the bias that makes us think we would have predicted an event after it occurs? a) The Hindsight Bias b) The Illusion of Control c) The Self-Serving Bias d) The Availability Cascade

Answer: a) The Hindsight Bias