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Why People Are So Confident When They're Wrong

Veritasium uses rogue trader Nick Leeson, whose hidden bets sank Barings Bank for $2.8 billion, as the spine of an argument that overconfidence is the most dangerous human bias. It walks through the calibration research showing people who feel 90% sure are right only 75% of the time, debunks the famous Mount Stupid graph that everyone mistakes for the real Dunning-Kruger curve, and shows overconfidence is partly a side effect of finite working memory. The twist is that overconfidence survived evolution because it wins status and triggers reward activity in listeners' brains, even when it cannot deliver. The fix is not more knowledge but feedback, calibration, and the humility to listen hardest to people who disagree with you.

Published Nov 12, 2025 24:01 video 23 min read Added Jun 16, 2026 Open on YouTube →

At a glance

A junior trader at Barings Bank fat fingers an order in July 1992, loses about $40,000, and her boss hides the mistake in a forgotten error account numbered 88888. Nobody at head office notices. That silence is the spark. Over the next two and a half years, Nick Leeson doubles down again and again, certain each time that the next trade will win it all back, until a magnitude 6.9 earthquake under Kobe finishes him off and brings down one of the oldest banks in the world. Total damage in today's money: $2.8 billion. He was 28.

Veritasium uses Leeson as the spine of an argument that overconfidence is the most dangerous bias humans have, the one quietly implicated in the Titanic, Chernobyl, and the loss of Challenger. The video then asks the real question: not who is overconfident, but why all of us are. It walks through the classic calibration research, debunks the famous "Mount Stupid" graph that everyone mistakes for the real Dunning-Kruger curve, shows that overconfidence is partly a side effect of finite working memory, and then delivers the twist that overconfidence survived evolution because it works socially. The fix is not more knowledge. It is feedback, calibration, and the humility to listen hardest to the people who disagree with you.

Below is the whole video rebuilt in order: every study, every number, every name, and the Leeson story braided through it the same way Derek tells it.

Overconfidence: the most dangerous bias

The cold open is a confession from inside a disaster. Friday, 17 July 1992, on the floor of Singapore's international stock exchange, a junior trader at Barings means to buy 20 futures contracts for a client and sells them instead. The error costs the bank nearly $40,000. To save her job, her boss Nick Leeson buries the loss in an obscure error account, account 88888, the kind banks keep to reconcile small discrepancies. Central office should have flagged it within the day. Nobody did. And that is where the rot starts, because the silence does not read to Leeson as luck. It reads as permission.

In his own words, played over the footage: "I'm operating in the belief that I can get it back. You know, maybe it goes back to the confidence thing. I don't know. I mean, I'm confident I can get it back at that stage."

The video then hands the thesis to its expert voice, the social psychologist Don Moore, who studies confidence at Berkeley: it is easy to make the case for overconfidence as the most dangerous of the human biases. It gets us to take risks, make commitments, enter contests, and try things that ultimately fail, sometimes in costly, embarrassing, and dangerous ways.

This is not a quirk of a few reckless gamblers. Overconfidence has been implicated in almost every large disaster, from the sinking of the Titanic to the Chernobyl nuclear accident to the loss of the space shuttle Challenger. And it lives in ordinary people too. The headline statistic: roughly 93% of us think we are better drivers than the median, which is statistically impossible. The number traces back to Ola Svenson's 1981 study, in which most American and Swedish drivers rated themselves safer and more skillful than the median driver.

How confident are you that you are right?

To show calibration breaking in real time, Veritasium runs a street quiz. The questions are deliberately the kind that feel obvious and are not:

True or false: Australia is wider than the Moon. (It is. Australia is about 4,000 km across; the Moon's diameter is about 3,475 km.)
Are there more stars in the Milky Way or more trees on Earth?
One teaspoon (4.5 grams) of pure olive oil contains more chemical potential energy than 4.5 grams of TNT. (True. TNT is optimized to release energy fast, not to store a lot of it.)
The energy density of butter is ten times that of lithium batteries. (Also true, for the same reason.)
Plants are more efficient than the average solar panel at absorbing the Sun's energy. (False. Photosynthesis converts only a small percent; commercial panels beat it handily.)

Derek is upfront that these are not the exact questions from the original research (more on those below), and that the specifics do not matter. What matters is the second question asked after each answer: how confident are you that you are right? The replies come back fast and absolute. "100% confident." "100, yes." "I've also gotta go 90%." Then the reveal lands and the smug confidence collapses, most memorably on the stars versus trees question: there are about 200 billion stars in the Milky Way, but about 3 trillion trees on Earth. The crowd groans. "False!"

That gap between certainty and accuracy is the entire subject, and it was first measured cleanly decades ago. The foundational work is Lichtenstein, Fischhoff, and Phillips' 1982 review of calibration research, which surfaced a stubborn pattern. The video states the classic finding plainly: when people say they are 90% certain, they are right only about 75% of the time.

Veritasium ran its own version online, asking the community science questions plus a confidence rating. The result was even sharper than the lab. The people who described themselves as 91 to 100% sure were correct only 51% of the time, barely better than a coin flip while feeling near certain. This pattern has been replicated again and again, across general knowledge, motor skills, and expert domains.

Figure 1. Calibration is how well stated confidence matches real accuracy. A perfectly calibrated person sits on the diagonal. Real people bow far below it, and the gap widens exactly where they feel most certain. The 91 to 100% confident group in the Veritasium poll cratered to 51% correct.

It is not just laypeople. Don Moore describes a paper analyzing the Survey of Professional Forecasters, the chief economists at corporations and banks who forecast the economy every quarter. On average they say they are about 53% sure they have correctly called something like inflation, and they turn out to be right about 23% of the time. The label for this match between what you think you know and what you actually know is calibration. Perfect calibration means that when you are 80% confident, you are right 80% of the time. Almost nobody is calibrated.

Leeson digs the first hole

With calibration established, the video returns to the trading floor to show what miscalibration costs at scale. Leeson's plan to recover the team's loss was to bet that the Japanese stock market would rise, so he went long on the Nikkei 225, the top 225 companies in Japan. Since the Japanese asset price bubble peaked two years earlier, the Nikkei had been sliding from 38,000 toward 16,000. Leeson was sure it would bottom out and rebound. It kept falling, down to about 14,000.

The whole time, he was betting the wrong way and using the gambler's classic move: double down. Bet double the losses so the next win clears the whole hole at once. The strategy works the moment a win arrives. The market gave him no win. Losses ballooned from $40,000 to around $3 million.

Then his luck turned. In spring 1993 the market rebounded to 20,000, and by summer the 88888 account was back in credit. Leeson went out, drank, danced on tables, free of the hole. On Monday morning he made another futures error he did not want to admit, dropped it back into 88888, and told himself that since he had escaped once, he could escape again. The losses in the five eights account grew, and each new loss made the next covering trade harder. By the end of 1993 they exceeded $30 million. Leeson is an extreme case, but the tendency to overestimate ourselves is universal. So why?

Why are people overconfident?

The video lines up three candidate explanations and takes each seriously.

Explanation one: ego. The obvious default, in Moore's words, is that we want to feel good about ourselves and pretend to be well informed. We get enormous satisfaction from being able to say "I know," or better, "I told you so," so we pretend to ourselves and others that we know when we do not. This is the motivated, egocentric account of excessive faith in our own judgment. True as far as it goes, but incomplete.

Explanation two: stupidity. Enter Franz Reichelt, a Parisian tailor with no scientific or engineering training who became obsessed with aviation safety. He designed a parachute suit that failed every test, and convinced himself the only problem was insufficient drop height. In February 1912 he took it to the Eiffel Tower and, rather than testing it with a dummy, jumped himself. He died on impact. Tragic, and as Derek puts it bluntly, really dumb. But stupidity is not a general theory of overconfidence either, because smart, expert people are overconfident too.

The real Dunning-Kruger graph

Here the video does something most explainers do not: it debunks the picture everyone associates with the topic. Derek shows people the famous graph where confidence rockets to a peak with almost no knowledge, crashes into a "Valley of Despair," then slowly climbs as expertise grows. People recognize it instantly as "the Dunning-Kruger graph." One sharp interviewee pushes back: "I think that this is not the Dunning-Kruger graph. I think that this is a bit of a communication trick."

She is right. That image is the Mount Stupid curve, the thing that surfaces when you Google image search the effect. It is a meme that resonated so hard it got conflated with the real research. It is not what David Dunning and Justin Kruger actually found.

The real 1999 study tested people on grammar, logic, and humor, then asked them to estimate their own performance. Plotting actual percentile against perceived percentile gives a very different shape: two lines that converge. The people who performed worst showed the largest gap between confidence and reality, the most overconfident. The people who performed best were actually slightly under confident. The honest read is that the worst performers think they are roughly average, and so do the best.

Figure 2. The actual Dunning and Kruger result, not the Mount Stupid meme. Real ability climbs steeply across quartiles while self assessment stays bunched near the middle. The bottom performers think they are average and the top performers slightly underrate themselves.

Then Derek complicates his own slide. To him the effect looks partly like a statistical artifact. During his PhD he asked students physics questions plus their confidence, and found that accuracy spanned the full range from near 0% to 100% while confidence varied over a much narrower band, everyone clustered around the middle. Mechanically, that alone makes poor performers look overconfident and top performers look under confident, which is exactly the Dunning-Kruger shape. So overconfidence is not entirely about how little you know. It is also that most of us express middling, middle of the road confidence regardless of how much we actually know. (This regression to the mean critique mirrors the published reanalysis by Nuhfer and colleagues, who argue much of the classic effect is a statistical mirage.)

How your brain takes shortcuts

There is a third, more physical factor: how much information a brain can actually process at once. To set it up, the video tells the Challenger story as a failure of synthesis, not nerve.

Monday, 27 January 1986, 5:45 PM at the Kennedy Space Center. Engineers at Morton Thiokol, who built the shuttle's solid rocket boosters, call an emergency conference. The overnight forecast is 25 degrees Fahrenheit, far colder than any prior launch. They know the rubber O-rings that seal the booster joints stiffen in cold and seal worse. They have a few hours to assemble the case. Over six hours they present 13 charts covering O-ring temperature, hot gas erosion, joint rotation, and more. But the data is scattered, incomplete, and never synthesized into one clear story. They had never tested below 53 degrees, so managers were juggling historical O-ring data, erosion patterns, joint dynamics, seal resiliency, and pressure differentials all at once. No single chart told the whole story. Overwhelmed by seemingly contradictory data and confident the boosters were safe, Thiokol management overruled their own engineers and approved the launch. At 11:38 the next morning, 73 seconds after liftoff, all seven crew members were killed.

The philosopher's framing comes next: calibrating your certainty means thinking of all the ways you could be wrong, and that is genuinely hard for finite, fallible agents if it means considering everything you do not know. The relevant constraint has a name, short-term (working) memory capacity, the number of novel chunks you can hold at once. Two studies pin it to overconfidence:

Hansson, Juslin, and Winman (2008) asked people to give ranges for facts like the length of a river or the population of a city. People's ranges were consistently too narrow, which is overconfidence by definition, and those with worse short-term memory were more often wrong and more overconfident.
A 2023 study by Conte and colleagues had participants hold sequences of letters in mind while judging their own performance. As memory load rose, confidence estimates got less accurate, even for people with high working memory.

Together these say something freeing: assessing your own accuracy is a mentally taxing task. Overconfidence is not necessarily arrogance. It is a brain working at the limit of what it can track and hold. And when a brain hits that limit, it reaches for shortcuts.

Daniel Kahneman named those shortcuts heuristics, which together produce the systematic errors called cognitive biases. The big one here is attribute substitution: swapping a hard question for an easier related one without noticing. The demonstration is elegant. Ask students how happy they are in life, then how many dates they had last month, and the two barely correlate. There is more to happiness than matches on Tinder. But flip the order, ask about dating first, and the correlation jumps to 0.66. Working out how happy you are is hard and multi-variable, so once your dating life is primed, your brain quietly answers the easy question ("how many dates lately?") in place of the hard one ("how happy am I?"). Misprocessing information that way can be disastrous.

How overconfidence is good for you

If overconfidence is so costly, why did natural selection not breed out the confidently incorrect? Because it pays socially. The video presents the evidence in two parts.

First, status. In a study Derek frames as a scientific version of The Apprentice, researchers in 2012 (Anderson, Brion, Moore, and Kennedy) compared people's self assessments against objective skill, then put them in group tasks and tracked who emerged as a leader and whose ideas moved the group across multiple sessions. The result was clean: overconfident individuals were more likely to lead, assert themselves, and keep influence, even when their actual ability was mid.

Second, the brain reward. Using fMRI, researchers at the University of Sussex measured brain activity in people hearing confident versus unconfident advice. Confident advice produced increased activity in the ventromedial prefrontal cortex, a region tied to processing rewards and expected satisfaction. In other words, human brains are biologically tuned to be swayed by confident people. We literally feel better hearing them.

Setting	What overconfidence does to the individual	What it does to everyone else
Group task / leadership	Wins leadership, asserts ideas, keeps influence even when ability is only average	The group follows, sometimes off a cliff
Hearing confident advice	Speaker is believed	Listener's reward centers (vmPFC) light up, they feel good
Job interview / campaign	Earns trust they cannot cash, talks a good line without the goods	Interviewers and voters place more faith than warranted
Barings trading floor	Leeson posts fake profits, requests up to $5M at a time	Management grants every request, ignores the doubters

Figure 3. The cruel incentive. Overconfidence pays the confident person in status and pays the audience in a dopamine hit, which is exactly why it persists and exactly why it is so dangerous when the confident person is wrong.

Moore draws the troubling corollary. This dynamic is a problematic incentive for anyone competing for something they want. People who express more confidence in job interviews or political campaigns earn the trust of interviewers and voters even when they are overconfident and cannot deliver. They do not know the answer, but they can talk a good line, and they know the audience rewards maximal confidence. So that is what gets expressed.

Leeson knew this in his bones and exploited it. While secretly bleeding money into 88888, he publicly posted huge profits, and everyone bought the illusion. Auditors came and, in his own gloating words, "they don't test any records, so I can't be happier. They didn't test one record." He kept requesting head office funds to double down, up to $5 million at a time, and Barings management, who barely understood futures and believed in their star trader, kept granting them. Leeson himself was stunned: "Every day that I make one of these requests for additional money, I never expect it to come. I expect somebody to say, 'Look, mate, what the hell's going on?'" Nobody did.

The importance of feedback

By autumn 1994 the five eights account was nearly $260 million in the red. To mask it, Leeson began buying futures from himself to inflate his apparent profits. At SIMEX's 10th anniversary that September, Barings collected two awards largely credited to him. In December he was flown to New York as the man seemingly responsible for $44 million of Barings turnover. A few traders did raise the obvious doubt, that those profits could not come from the low risk trading he claimed, but management's faith was unshakeable and the concerns were dismissed. Barings' overconfidence in Leeson and Leeson's overconfidence in himself were converging on one outcome.

The deeper mechanism is feedback quality. In a controlled environment with clear rules and guaranteed outcomes, like a chess match, you get clean, immediate feedback on whether a decision was good or bad. With reliable feedback, professional chess players genuinely learn to make better decisions with accurate confidence. But in a noisy environment, where consequences are inconsistent or badly delayed, the feedback is unreliable and even experts cannot tell whether their confidence is earned.

The video's clean example of unreliable feedback is political punditry, complete with old clips of pundits calling a Romney landslide and giving Hillary an 80% chance. Then the sharpest case: Allan Lichtman, who had correctly called 9 of the previous 10 US presidential elections with his 13 Keys to the White House system. Using the same method in 2024 he predicted a Kamala Harris win, and was wrong. He attributed the miss to disinformation that misled the electorate and made it hard to read basic facts like the real state of the economy. The point is not that Lichtman is foolish; it is that nine correct calls is exactly the kind of inconsistent positive feedback that inflates confidence beyond what the method can support. Leeson got the same poison: some bad trades, but he had also clawed it all back once before, and that single recovery clouded his judgment and amplified his overconfidence.

How one of the oldest banks collapsed

By 1995 Leeson's losses ran into the hundreds of millions and Barings had unwittingly wired him about $1 billion. For a bank with roughly $700 million in capital base, legally permitted to lend only about a quarter of that, the math was already insane, and nobody questioned it. They were blinded by his apparent success. His positions had grown so enormous that he estimates he held about half of the entire Nikkei futures market. All he could do was buy time and pray the market moved his way. For a while the economy was stable and nothing on the horizon threatened it.

Then, on 17 January 1995, the Great Hanshin earthquake, magnitude 6.9, struck 20 km from Kobe, one of Japan's key ports. The devastation hit the market and the Nikkei plunged 1,055 points. Leeson, doubling down one last time, bet heavily that the index would snap back fast. It did not. In today's money his final losses came to $2.8 billion.

On 23 February Leeson ran. Three days later Barings, one of the oldest and most trusted banks in the world, collapsed, overconfidence at least partly to blame. Leeson fled to Malaysia, then Thailand, was arrested in Germany, and was extradited to face justice. He was 28 years old. We do not all bring down banks, but in a complex world with noisy feedback and overwhelmed brains, the same set of simplistic biases takes over, and we routinely end up believing we know more than we do.

How to correct overconfidence

The video closes with a practical toolkit rather than a scold.

Keep score. Moore calibrates by tracking and scoring his own predictions. When a colleague asks how long he will take to send comments, he does not promise Friday; he says "there's a 60% chance I can get you comments by Friday." It earns quizzical looks, but it forces honest probability and builds a record he can check.

Get feedback, not just information. Moore's line: the best medicine for overconfidence is not so much information as feedback, and he gets plenty of it. Information you can rationalize; feedback you cannot.

Be intellectually humble, and listen hardest to your critics. To get more accurate, capitalize on the wisdom of the crowd by listening to others, especially the people who disagree with you. Understanding the best arguments of your critics, and what they know that you do not, is among the most useful things you can do for a decision.

The closing thesis, in Derek's words: the best calibrated people are not those who know the most, but those who know what they do not know. True wisdom is not being certain; it is knowing the limits of your own certainty.

Key takeaways

Overconfidence is plausibly the most dangerous human bias, implicated in disasters from the Titanic to Chernobyl to Challenger, and in everyday miscalibration (about 93% of people rate themselves above the median driver).
Calibration is the match between felt certainty and real accuracy. People who feel 90% sure are right about 75% of the time; the Veritasium poll's "91 to 100% sure" group was right just 51%; professional forecasters who feel 53% sure about inflation are right about 23% of the time.
The famous "Mount Stupid" graph is not the real Dunning-Kruger result. The actual finding is two converging lines: the worst performers think they are average, the best slightly underrate themselves, and part of the effect is a statistical artifact of narrow-band confidence.
Overconfidence is partly a hardware limit. Worse short-term memory predicts more overconfidence, and higher memory load degrades self assessment, so the brain substitutes hard questions with easy ones (dating primed, life-happiness correlation jumps to 0.66).
Overconfidence survived evolution because it works socially: it wins leadership and status (the 2012 Apprentice-style study) and triggers reward activity (vmPFC) in listeners, even when it cannot deliver.
The cure is calibration through feedback, scoring your own predictions in probabilities, intellectual humility, and listening hardest to the people who disagree with you.

Chapters

00:00 The Most Dangerous Cognitive Bias 03:01 How confident are you that you are right? 07:40 Why are people overconfident? 08:53 The Real Dunning-Kruger Effect Graph 10:22 How Your Brain Takes Shortcuts 14:02 How overconfidence is good for you 15:55 Nick Leeson Rogue Trader 17:17 The Importance of Feedback 18:45 How One of England's Oldest Banks Collapsed 20:41 How To Correct Overconfidence

Notable quotes

"I'm operating in the belief that I can get it back. You know, maybe it goes back to the confidence thing. I'm confident I can get it back at that stage." Nick Leeson, on the mindset that doomed Barings (00:24)

"It's easy for me to make the case at overconfidence as the most dangerous of the human biases." Don Moore (00:52)

"When people are 90% certain, they are right only 75% of the time." Derek Muller, on the classic calibration gap (04:35)

"They are, on average, too sure that they know what's going to happen... they say there's something like 53% sure... and they are right, like, 23% of the time." Don Moore, on professional forecasters (05:00)

"This is not the Dunning-Kruger graph. I think that this is a bit of a communication trick." A street interviewee, correctly calling out the Mount Stupid meme (09:00)

"So, overconfidence isn't necessarily about arrogance. It's your brain working at the limits of what it can track and hold." Derek Muller (12:30)

"They don't actually know the answer, but they can talk a good line to impress others... They know the audience will place more faith in them if they express maximal confidence." Don Moore, on interviews and campaigns (14:50)

"I think that the best medicine for overconfidence is not so much information as feedback, and I get plenty of that." Don Moore (20:50)

"The best calibrated people aren't those who know the most. It's those who know what they don't know. True wisdom lies not in being certain, but in knowing the limits of your own certainty." Derek Muller (21:20)

Resources mentioned

Veritasium and host Derek Muller
Don Moore, social psychologist at UC Berkeley Haas, author of Perfectly Confident
Nick Leeson and the collapse of Barings Bank, trading on the Nikkei 225 via SIMEX
The Dunning-Kruger effect, David Dunning and Justin Kruger, and the Nuhfer reanalysis of the statistical artifact critique
Ola Svenson's "Are we all less risky and more skillful than our fellow drivers?" (1981)
Lichtenstein, Fischhoff, and Phillips, calibration of probabilities (1982)
The Survey of Professional Forecasters
Hansson, Juslin, and Winman (2008) on short-term memory and overconfidence
Daniel Kahneman, heuristics, biases, and attribute substitution
Anderson, Brion, Moore, and Kennedy (2012) on overconfidence and social status
University of Sussex fMRI work on confident advice and the ventromedial prefrontal cortex
Allan Lichtman and the 13 Keys to the White House
Franz Reichelt, the flying tailor, and the Eiffel Tower jump
Historic disasters referenced: the Titanic, Chernobyl, the space shuttle Challenger and its O-ring failure, and the Great Hanshin earthquake
Elements of Truth, the Veritasium science trivia board game launched on Kickstarter, where points scale with how confident you bid

Full transcript

[Derek] Friday, the 17th of July, 1992. Amidst the chaos of the trading floor at Singapore's international stock exchange, one of the junior traders makes an expensive mistake. Instead of buying 20 futures contracts for a client, she sold them instead, costing Barings Bank nearly $40,000. To save her job, her boss, Nick Leeson, a young trader keen to make his mark, decides to hide the loss. He puts it in an obscure error account, that's account 88888. It's used by banks to solve small discrepancies and trades. It's a dangerous move and it should have been picked up by the central office immediately, but when nobody from Barings notices, he gains confidence, convinced he can win back the loss and get the team out of trouble. [Leeson] I'm operating in the belief that I can get it back. You know, maybe it goes back to the confidence thing. I don't know. I mean, I'm confident I can get it back at that stage. [Derek] But Leeson's staggering overconfidence would create a debt of gargantuan proportions and lead to one of the biggest collapses in banking history. It's easy for me to make the case at overconfidence as the most dangerous of the human biases. Overconfidence gets us into all sorts of trouble. It leads us to take risks, make commitments, enter contests, try things that will ultimately fail, sometimes in costly, embarrassing, and dangerous ways. Overconfidence has been implicated in almost every big disaster, from the sinking of the Titanic to the Chernobyl nuclear disaster, to the loss of the space shuttle Challenger. But overconfidence isn't reserved for just a few reckless individuals. We can all fall victim to it. For example, 93% of us think we are better drivers than the median, which is, of course, impossible. The scale of the problem was identified in some now-classic research with a set of simple quiz questions. True or false, Australia is wider than the moon. "Oh! I bet they're about the same size." It looks small on a map, but it's huge. Are there more or less stars in the Milky Way than trees on Earth? "More stars." Than trees? One teaspoon of pure olive oil, four and a half grams, contains more chemical potential energy than an equivalent four and a half gram amount of TNT. "I'm gonna say true." This feels kind of like a trick question. The energy density of butter is 10 times that of lithium batteries, right? "I mean, lithium batteries are rubbish. Everyone's going on about them. Just run your car on butter, I say." True or false, plants are more efficient than the average solar panel at absorbing the sun's energy. "Yeah, they must be." Now, these aren't the exact questions from the original research, more on those later, but the specifics don't really matter. The interesting bit is what they asked next, how confident are you that you are right? "100% confident." "100% confident?" "100, yes." "I think it's true. So, I've also gotta go 90%." "100%." "100%?" I don't think we actually had any expectations. This was very early on in the research on confidence assessment, but we didn't really know what we would find. [Derek] The results revealed a surprising disparity between how accurate someone thinks they are and how accurate they actually are. When people are 90% certain, they are right, only 75% of the time. In fact, we ran our own version of this online and found an even more extreme discrepancy. We asked the Veritasium community a bunch of science questions and then asked how confident they were in their answer. Our results showed that those who were the most confident describing themselves as 91 to 100% sure were only correct 51% of the time. Since the original research, these results have been replicated again and again in all areas from general knowledge to motor skills, and it affects everyone, even experts. I have a paper where we analyze the survey of professional forecasters. These are chief economists at various corporations and banks who are invited to forecast the state of the economy on a quarterly basis. We find that they are, on average, too sure that they know what's going to happen, so they, on average, say there's something like 53% sure they have correctly predicted what's gonna happen to inflation, for instance, and they are right, like, 23% of the time. [Derek] How well what you think you know matches with what you actually know is called your calibration. So, if you're perfectly calibrated and, say, 80% confident, you should be right 80% of the time, but most of us are not well calibrated. "100%." There are 200 billion stars in the Milky Way, but there are 3 trillion trees on Earth. "Oh! False!" Most of the time, being overconfident isn't a huge issue, but for Nick Leeson, the stakes soon became very high. Leeson's plan to recover his team's loss was to bet that the Japanese stock market would go up, so he went long on the top 225 companies in Japan, the Nikkei 225. Ever since the Japanese asset price bubble peaked two years previously, the Nikkei had been dropping from 38,000 to 16,000, and he was confident the market would soon bottom out and start rising, but it continued to fall. Over the next few weeks, it dropped to a low of 14,000, and the whole time, Nick was betting the wrong way with ever bigger and riskier bets, he would double down, betting double his losses, so the next win would bring him back above water. This strategy should work when the next win comes along, but the market collapse was unrelenting, and his losses ballooned from $40,000 to around $3 million. In spite of this, Leeson remained confident, and eventually, his luck turned around. In the spring of 1993, the market rebounded up to 20,000, and by the summer, that account was back in credit. Leeson goes out to celebrate that weekend, drinking and dancing on tables, finally free of the hole he had dug himself into. But on Monday morning, he made another trading error on the futures market, a damaging loss that Leeson didn't want to admit, so he put the loss back into account 88888, confident that if he'd got out of the previous one, he could do it again. He began making risky trades to try to recover that initial loss, and as the losses mounted in the five eights account, it became harder and harder to make new trades to cover the last loss. By the end of 1993, his losses exceeded $30 million. Leeson is obviously an extreme example, but this tendency to overestimate our abilities is something we all share. So, the question is why? The obvious explanation that a lot of people default to that is we wanna feel good about ourselves and pretend like we're well-informed, that we enjoy enormous satisfaction from being able to say, "I know," or even better, "I told you so." And so, we pretend to ourselves and others that we know when we don't. That is the motivated egocentric version of an explanation for excessive faith in our own judgment. [Narrator] Reichelt had faith in the idea, if nothing else. [Derek] But what if it's something more uncomfortable, stupidity? Franz Reichelt was a tailor with no formal scientific or engineering training, but he was obsessed with solving the problem of aviation safety. He spent months designing a parachute suit, which repeatedly failed during testing, but he became convinced the issue was that he didn't have enough height for it to work. So, in February, 1912, he took it to the Eiffel Tower, and rather than using a dummy to test it, he jumped himself. Tragic, but also really dumb. Do you recognize this graph? "The, like, Dunning-Kruger graph?" Are you confident that this is the actual Dunning-Kruger graph? "Not at all, in fact, I think that this is not the Dunning-Kruger graph. I think that this is a bit of a communication trick." This is what people call the Mount Stupid Curve, which will pop up if you search for the Dunning-Kruger effect into Google images. "As far as I understand it, it's an effect that says that when you know very little about it, your confidence is high, and as you know more about it, your confidence sinks until you become a master at it." "But I think that the actual result is, like, a little bit more fuzzy than this." It was. This graph doesn't actually show the Dunning-Kruger effect. It's just a meme that resonates with a lot of people, and it became conflated with the effect. In their original research, Dunning and Kruger tested people on tasks like grammar, logic and humor, and then they asked participants to estimate how well they performed. That produced this curve. You can see that those who performed worse had the largest mismatch between their confidence and performance. They were the most overconfident. Those who performed the best were actually slightly underconfident. This suggests that overconfidence may be linked to how much we know. Those who know less think that they know more than they do, but to me, this effect looks a little like a statistical artifact because while I was doing my PhD, I asked students some physics questions and I also asked for their confidence in their answers. And when I looked at the results, accuracy spanned the full range from nearly 0% to 100%, but confidence varied over a much narrower range. All the scores were roughly in the middle. This meant that poor performers were the most overconfident and the highest performers were actually slightly under confident. This is exactly what Dunning and Kruger found, so maybe overconfidence isn't entirely due to how much we know, but also because most of us express at least kind of middle-of-the-road confidence. But there is another factor at play here, which is how much information our brains are capable of processing. Monday, the 27th of January, 1986, it's 5:45 PM at the Kennedy Space Center in Cape Canaveral, and the engineers from Morton Thiokol, who made the Challenger space shuttle's rocket boosters make an emergency conference call. They've just seen the weather forecast, predicting temperatures overnight will plummet to 25 degrees Fahrenheit, far colder than any previous shuttle launch. They know the rubber O-rings that seal joints in the boosters become less flexible in the cold, but have just a few hours to gather data, create charts, and present their case. Over the next six hours, the engineers present 13 charts with data on O-ring temperature, hot gas erosion, joint rotation, and more. But the data is scattered, incomplete, and not synthesized into a clear narrative. They'd never tested below 53 degrees Fahrenheit, so NASA managers were trying simultaneously to track historical O-ring data, erosion patterns, joint dynamics, seal resiliency, pressure differentials, and more. No single chart told the whole story. So, overwhelmed by seemingly contradictory data and confident that their rocket boosters were safe, Thiokol management overruled their own engineers and approved the launch. At 11:38 the next morning, 73 seconds after launch, all seven crew members were killed. Calibrating your certainty requires thinking of all the ways that you could be wrong, and that's hard for finite, fallible agents like us if it means considering everything that we don't know. [Derek] How many chunks of novel information you can hold in your head at one time is your short-term memory capacity. In 2008, Hansson, Juslin, and Windmann investigated how short-term memory capacity was linked to accuracy and overconfidence. Participants were asked to give ranges for factual questions like the length of a river or the population of a city. People's ranges were consistently too narrow, which effectively meant they were being overconfident, and those who had worse short-term memory were more often wrong and more likely to be overconfident. Another study conducted by Conte in 2023 asked participants to keep sequences of letters in their mind while they judged their own performance. As the memory load increased, confidence estimates became less accurate, even for participants with higher working memory capacities. Together, these studies suggest that assessing your accuracy is a mentally taxing task. So, overconfidence isn't necessarily about arrogance. It's your brain working at the limits of what it can track and hold. And because of this, your brain can start using shortcuts. Psychologist Daniel Kahneman describes these mental shortcuts as heuristics, that together, create systemic errors called cognitive biases. One shortcut we use a lot is substituting hard questions with easier related ones. Researchers have tested this by asking students how happy they were in their life and how many dates they'd had in the last month. Unsurprisingly, these two questions did not correlate. There's a lot more to happiness than matches on Tinder, at least for most people. However, if you switch it around and ask about their dates first, suddenly, the correlation jumps to 0.66. Working out how happy you are in your life is a difficult question. You have to consider many things and balance them all out. So, when you're primed with the information about your dating life, you're very likely to substitute that hard question, how happy am I in my life, with, how many dates have I recently had? Overconfidence from misprocessing information like this can be disastrous. So, why are our brains wired this way? I mean, you might expect natural selection to have wiped out the confidently incorrect, but there is evidence that overconfidence can actually be advantageous. Overconfidence can massively improve your status. In a scientific version of "The Apprentice," scientists in 2012 compared participants' own assessments of their skill with objective measures. They placed them in group tasks to see who is chosen as a leader and whose ideas influenced the group, tracking status over multiple sessions and assessing whether the desire for status led participants to exaggerate their abilities. The results were clear, overconfident individuals were more likely to lead, assert themselves, and maintain influence, even when their actual abilities were mid. And the evidence certainly shows people react better to confident individuals. Using an fMRI, researchers at the University of Sussex measured brain activity of people after hearing unconfident versus confident advice, and those who listened to the confident advice had increased activity in the ventromedial prefrontal cortex. That's a brain region associated with processing rewards and expected satisfaction. This means that human brains are biologically tuned to be influenced by confident individuals. We literally feel better when we hear confident people. But recognizing that dynamic highlights a potentially problematic incentive for anyone who's contending for positions that they want. People who express more confidence in employment interviews or political campaigns, for instance, do earn the confidence of interviewers and potential voters, even if they're being overconfident. They can't actually deliver. They don't actually know the answer, but they can talk a good line to impress others, even if they can't ultimately deliver on those grand assertions. They know the audience will place more faith in them, if they express maximal confidence. [Derek] Leeson knew this and he took advantage of it. While he was secretly racking up huge losses in the infamous five eights account, he was publicly posting huge profits and everyone believed the illusion. "They come in and they don't test any records, so I can't be happier. They didn't test one record." As Leeson's hidden losses mounted, he requested huge sums from head office to keep doubling down up to $5 million at a time. Barings management, barely understanding futures and believing in their star trader, they granted his requests and continued to grant future requests. "Every day that I make one of these requests for additional money, I never expect it to come. I expect somebody to say, 'Look, mate, what the hell's going on?'" By Autumn 1994, the account was nearly $260 million in the red. To mask this, Leeson began buying futures from himself, inflating his perceived profits further. At SIMEX's 10th anniversary in September, Barings received two awards, largely credited to Leeson. In December, he was flown to New York, seemingly responsible for $44 million in Barings turnover that year. Some traders raised doubts that these kinds of profits could be made from the low risk trading he was supposed to be doing. But management's faith in Leeson's talent was unshakeable, and they dismissed these concerns. Barings' overconfidence in Leeson and Leeson's overconfidence in himself would be the bank's downfall. The figures are so big, it seems like the ultimate confidence trick, but there's a partial explanation for the delusion on both sides. Leeson's and Barings' overconfidence was amplified by the complexity and unpredictability of the market and the trades he was making. It's sort of an issue of feedback. In a controlled environment where there are clear rules and guaranteed outcomes like in a chess match, there is clear feedback on whether decisions are good or bad. With reliable feedback, professional chess players are able to learn to make better decisions with more accurate confidence. But in a noisy environment where there is rarely consistent or timely consequences for predictions, this feedback is unreliable. Whether our confidence is accurate or misplaced is increasingly hard to judge, even amongst experts. It's especially problematic for political pundits. "This is going to be a landslide. I think Romney's gonna win by quite a bit." "So, right now, we have Hillary's about a 75 or an 80% favorite." [Derek] For example, prior to 2024, political analyst Allan Lichtman had accurately predicted the winner of nine of the 10 previous US presidential elections, using his 13 keys to the White House method. Using the same strategy in 2024, he predicted that "Kamala Harris will be a precedent-breaking president." And look what happened. Is this crazy? He attributed his miscalculation to the spread of disinformation that misled the electorate. This noisy environment made it difficult to discern key issues like the actual state of the economy. Similarly, the feedback Leeson had received was inconsistent. He was making some bad trades, but he had also won it all back before, and this significantly clouded his judgment, amplifying his overconfidence. By 1995, Leeson's losses were in the hundreds of millions, and Barings had unwittingly sent him $1 billion. For a bank with around $700 million in capital base, they were legally only allowed to lend around a quarter of that, but no one questioned it. They were all blinded by his apparent success. At this point, his positions were so big, he estimates that he was probably half of the entire Nikkei futures market, so all he could do was buy himself some time and hope that the market went his way. And for a while, it worked. The economy was stable and he couldn't see anything on the horizon that would change this. But then disaster struck. [Reporter] Japan is tonight in a state of mourning and of shock. [Derek] On the 17th of January, 1995, the Great Hanshin Earthquake struck Japan 20 kilometers from the city of Kobe. With the magnitude of 6.9, it devastated the city, which was one of Japan's key ports. This devastation spread to the stock market. The Nikkei index plunged 1055 points. Leeson, attempting to double down again, risked even more money. He bet heavily that the Nikkei would make a rapid recovery, but it didn't. And in the end, in today's money, Leeson had lost $2.8 billion. On the 23rd of February, Leeson went on the run. And three days later, Barings, one of the oldest and most trusted banks in the world, collapsed. Overconfidence was at least partially to blame for its downfall. Realizing the walls were closing in, Leeson fled to Malaysia and then Thailand, but his escape was short-lived. He was eventually arrested in Germany and extradited to face justice, marking the end of a spectacularly destructive gamble. He was just 28 years old. Now, we don't all bring down banks, but we are all vulnerable to overconfidence. In a complex world with unclear, noisy feedback where our brains are overwhelmed, a set of simplistic biases can take over. And we all too often end up thinking we know more than we do. So, what can we actually do about this? I try to get better at calibrating my confidence judgments by keeping track and keeping score. So, when a colleague asks me, "How long is it going to take you to get me comments on this paper draft we're working on?" I don't promise I'll do it by Friday. I'm much more likely to say something like, "I think there's a 60% chance I can get you comments by Friday." They'll often react with a quizzical look or a laugh. [Derek] Well, practicing and being aware of our calibration is the obvious way to improve, but so is being intellectually humble. I think that the best medicine for overconfidence is not so much information as feedback, and I get plenty of that. Though also, I think people are right, that sometimes I do have a little bit of overconfidence. If we wanna become more accurate, we should capitalize on the wisdom of the crowd by listening more to others. In particular, we should listen to people who disagree with us. Understanding the best arguments of your critics, understanding what information those who disagree with you have that you lack is very helpful for making better decisions. [Derek] The best calibrated people aren't those who know the most. It's those who know what they don't know. So, true wisdom lies not in being certain, but in knowing the limits of your own certainty. And that's an idea that's inspired our latest project. All those questions I asked, they come from a new board game that we've made. It's called "Elements of Truth." The game contains over 800 fascinating science trivia questions with a twist. The number of points you win on each question depends on how confident you are. You can bid any number from 1 to 10. If you're not sure, you can play a low number or you can try to follow the lead of someone else who you think should know the answer. We've tested this game with scientists, teachers, and students, and what we've found is that it regularly leads to discussions that go way beyond the initial questions. The core game comes with 200 questions that cover all aspects of science, and there are five additional packs on specific topics like physics, technology, engineering, and astronomy. Plus, there's a Veritasium pack on concepts covered in many of our most popular videos. We're launching the game through Kickstarter to give you the opportunity to shape it with us. Over the next month, you'll be able to submit questions for a special community pack. This is your chance to etch your name into Veritasium history with a credited question in the game. To reserve your copy and get involved, use this QR code or the link in the description to head over to Kickstarter. It is only because of you that I've been able to make this channel and this game, so as always, I wanna thank you for your support and thank you for watching.