Gambler’s Fallacy And Why It Matters In Business

Gambler’s fallacy is a mistaken belief that past events influence future events. This fallacy can manifest in several ways. One example, if how individuals mistakenly conclude past events. Instead, to prevent the gambler’s fallacy, business people need to know that the real world is more complex and subtle than a game, and rather than relying on complex models, they can rely on solid time-proved heuristics.

Concept OverviewThe Gambler’s Fallacy, also known as the Monte Carlo Fallacy or the Fallacy of the Maturity of Chances, is a cognitive bias that occurs when individuals believe that future outcomes in a random process are influenced by past outcomes, even when the events are statistically independent. It is called a “fallacy” because it involves a misconception about probability and randomness. The fallacy often arises in games of chance, such as gambling, but it can affect decision-making in various contexts.
Key ElementsThe Gambler’s Fallacy involves several key elements:
Misconception of Probability: Individuals under the influence of this fallacy believe that if a particular event has occurred repeatedly, the opposite outcome is more likely to happen in the future to “balance” things out.
Independence of Events: In reality, events in games of chance, like coin flips or roulette spins, are statistically independent. The outcome of one event does not affect the outcome of the next.
Regression to the Mean: While the fallacy assumes that a series of one outcome (e.g., several consecutive coin flips resulting in “heads”) will be followed by the opposite outcome, in reality, events tend to regress toward the long-term average (e.g., a 50-50 ratio for coin flips).
CausesSeveral factors contribute to the Gambler’s Fallacy:
Pattern Recognition: Humans have a natural tendency to recognize patterns, even in random sequences. When they see a series of similar outcomes, they may incorrectly infer a pattern or trend.
Loss Aversion: People often want to avoid losses, so if they’ve experienced a series of losses, they may expect a win to occur soon, leading to the fallacy.
Limited Understanding of Probability: Many individuals have a limited understanding of probability and randomness, making them susceptible to fallacious thinking.
Emotional Influence: Emotions, such as frustration or excitement, can cloud rational judgment and lead to fallacious beliefs.
ExamplesExamples of the Gambler’s Fallacy can be found in various situations:
Casinos: Gamblers might believe that if a roulette wheel has landed on black several times in a row, red is “due” to come up, leading them to place bets on red.
Investing: Investors may believe that if a stock has been declining in value, it’s more likely to rebound soon, despite market forces being unrelated to past performance.
Sports: Fans might think that a sports team that has lost multiple games in a row is “due” for a win, even though each game is independent of previous results.
Lotteries: People may feel that if they’ve been playing a lottery for a long time without winning, their chances of winning are higher in the next draw, which is not true.
ConsequencesSuccumbing to the Gambler’s Fallacy can have various consequences:
Financial Losses: In gambling or investing, individuals may make irrational decisions based on fallacious beliefs, resulting in financial losses.
Inefficient Decision-Making: The fallacy can lead to inefficient decision-making and misguided strategies in various contexts.
Misallocation of Resources: In some cases, it can lead to the misallocation of resources or efforts based on false expectations.
Frustration and Regret: People who believe in the fallacy may experience frustration and regret when outcomes do not align with their expectations.
Prevention and MitigationPreventing the Gambler’s Fallacy involves:
Education: Educating individuals about probability, randomness, and statistical independence can help them recognize the fallacy.
Emotion Regulation: Encouraging emotional regulation and rational decision-making can mitigate the impact of the fallacy.
Awareness: Promoting awareness of the fallacy and its potential consequences in relevant contexts, such as gambling or investing, is essential for prevention.

Understanding the Gambler’s fallacy

The Gambler’s fallacy is based on unsound reasoning. 

It is often seen in gambling, where an individual might predict that a coin toss will land on heads based on the previous three results of tails. In reality, of course, the probability of either result occurring does not deviate from 50%. That is, each coin toss is an independent event with no relationship to previous or future tosses.

Nevertheless, many individuals are influenced by this fallacy because they underestimate the likelihood of sequential streaks occurring by chance. This results in a cognitive bias where an event is judged based on unrelated factors within a very small sample size.

Mistaken beliefs arising from the Gambler’s fallacy manifest in two ways:

  1. The belief that if an event occurs more frequently than usual, it is less likely to occur in the future.
  2. The belief that if an event occurs less frequently than usual, it is more likely to occur in the future.

Other applications of the Gambler’s fallacy

In investing, the fallacy causes investors to believe that a company reporting successive quarters of positive growth is primed for a period of negative growth. Using this reasoning, the investor might pre-emptively sell shares in a company even though the fundamentals leading to growth have not changed.

The reverse is also true. In the case of a company experiencing several quarters of negative growth, an investor may endure large capital losses in the mistaken belief that a profitable quarter is imminent.

Studies have also found evidence for Gambler’s fallacy decision making in:

  • Refugee asylum court decisions. Judges were more likely to reject applications for asylum if they approved the previous application.
  • Loan application reviews. Loan applications were more likely to be reversed if the following two decisions were made in the same direction. 
  • Major League Baseball umpiring. Umpires were less likely to call a strike if the previous pitch was called the same way. The effect was amplified significantly for pitches closer to the edge of the strike zone or if the previous two pitches were called the same way.

In each of the three examples, it was found that less experienced decision-makers were more likely to underestimate the likelihood of event streaks occurring by chance – particularly when occurring in quick succession.

Avoiding the Gambler’s fallacy

Businesses that operate in industries prone to the Gambler’s fallacy should first ensure that decision-makers are experienced and knowledgeable in their given fields.

Awareness of the fallacy itself is also crucial – though research shows that awareness alone is not enough to prevent against being influenced.

De-biasing techniques are often effective. These techniques involve emphasizing the independence of events by highlighting their inability to affect each other. The emphasis can be internalized by remembering the classic fallacies of a coin toss or the roll of a dice. De-biasing can also include slowing down the reasoning process and removing distractions. This makes it easier for individuals to think logically, avoiding cognitive biases.

The concept of cognitive biases was introduced and popularized by the work of Amos Tversky and Daniel Kahneman in 1972. Biases are seen as systematic errors and flaws that make humans deviate from the standards of rationality, thus making us inept at making good decisions under uncertainty.
Bounded rationality is a concept attributed to Herbert Simon, an economist and political scientist interested in decision-making and how we make decisions in the real world. In fact, he believed that rather than optimizing (which was the mainstream view in the past decades) humans follow what he called satisficing.

Examples of the Gambler’s Fallacy:

  • Coin Toss: A person is flipping a fair coin and gets heads five times in a row. They mistakenly believe that the next coin toss is more likely to be tails since “it’s due to happen.” In reality, the probability of getting heads or tails on the next toss remains 50%, as each toss is an independent event.
  • Roulette: In a casino, a gambler observes that red numbers have appeared multiple times in a row on the roulette wheel. Believing in the Gambler’s Fallacy, they start betting heavily on black, assuming that black is now “due” to come up. However, the outcome of the roulette wheel is still random, and previous spins do not influence future spins.
  • Stock Market Trading: An investor notices that a stock has been consistently increasing in value for several days. Fearing the Gambler’s Fallacy, they decide to sell their shares, thinking that the stock is due for a decline. In reality, stock prices can be influenced by various factors, and past performance does not guarantee future outcomes.
  • Sports Betting: A sports bettor has been betting on a basketball team that has won the last five games. Following the Gambler’s Fallacy, they decide to bet heavily against the team in the next game, assuming that their winning streak is bound to end. However, the team’s performance in the previous games does not affect their chances of winning the next one.
  • Lottery Numbers: Someone buys lottery tickets and chooses the same set of numbers that have not won in previous draws. They believe that these numbers are now “due” to win. In reality, lottery number draws are entirely random, and past outcomes do not influence future draws.
  • Weather Patterns: A farmer notices that it has been raining for several days in a row. Fearing the Gambler’s Fallacy, they assume that it will not rain tomorrow because it has rained so much already. However, weather patterns are influenced by complex atmospheric conditions, and past weather does not dictate future conditions.
  • Business Decisions: A business owner experiences several consecutive months of high profits. They become overly cautious, fearing the Gambler’s Fallacy, and decide to cut back on investments and new projects, assuming that a decline in profits is imminent. However, business performance is influenced by various internal and external factors, and past profits do not guarantee future success.
  • Hiring Decisions: A hiring manager notices that the last three candidates they hired turned out to be excellent employees. Following the Gambler’s Fallacy, they believe that the next candidate they hire will also be exceptional. However, each candidate’s qualifications and suitability for the role are independent of previous hires.

Key takeaways

  • The Gambler’s fallacy is a cognitive bias where an individual mistakenly believes that past events influence the outcome of independent future events.
  • The Gambler’s fallacy occurs because of the underestimation of the likelihood of sequential events occurring by chance. As a result, it is seen in many industries where seemingly related events occur in quick succession.
  • Avoiding the Gambler’s fallacy starts with awareness and ensuring that decision-makers are highly experienced. De-biasing techniques can also be employed to reinforce logical reasoning and reduce cognitive load.

Key Highlights

  • Definition of the Gambler’s Fallacy: The Gambler’s Fallacy is a cognitive bias where individuals mistakenly believe that past events, especially in random or independent processes, influence future events. This leads to erroneous expectations and decisions based on perceived patterns that do not actually exist.
  • Examples of the Fallacy in Different Scenarios:
    • Coin Toss: Assuming that after a series of coin tosses resulting in heads, tails is more likely to appear, even though each toss is independent.
    • Roulette: Betting on the opposite color because one color has appeared multiple times in a row, incorrectly believing that a “balance” will occur.
    • Stock Market: Selling stocks after a series of gains, fearing an upcoming decline based on the idea that a reversal is due.
    • Sports Betting: Betting against a team that has won multiple games in a row, thinking their winning streak will end soon.
    • Lottery: Selecting numbers that haven’t won in previous draws, assuming they are more likely to win now.
    • Business Decisions: Making decisions based on the belief that a series of positive or negative outcomes will continue indefinitely.
  • Bias in Decision-Making: The Gambler’s Fallacy arises due to a misperception of randomness and independence. People tend to underestimate the likelihood of streaks or clusters of events occurring by chance and assume that patterns will continue.
  • Business and Real-World Applications:
    • Investing: Investors misjudge stock market trends based on past performance, leading to mistaken decisions.
    • Decision-Making: Business leaders may overreact to short-term successes or failures, making hasty decisions.
    • Hiring: Hiring managers may base their selection process on previous hires, assuming a consistent pattern.
  • Counteracting the Gambler’s Fallacy:
    • Experience and Expertise: Decision-makers with experience and expertise are less likely to succumb to the fallacy.
    • Awareness: Recognizing the Gambler’s Fallacy and understanding its influence can help in avoiding its pitfalls.
    • De-Biasing Techniques: Techniques like emphasizing independence of events, slowing down the decision-making process, and minimizing distractions can counteract the fallacy.
  • Relation to Cognitive Biases: The Gambler’s Fallacy is part of a broader array of cognitive biases that affect human decision-making. These biases often deviate from rational thinking and lead to suboptimal choices.
  • Bounded Rationality and Satisficing: Herbert Simon’s concept of bounded rationality suggests that humans make decisions based on limited information and cognitive resources. Satisficing, as opposed to optimizing, acknowledges that decision-makers settle for solutions that are good enough rather than ideal.
  • Amos Tversky and Daniel Kahneman: These psychologists introduced cognitive biases in their work, highlighting systematic errors in human judgment and decision-making. Their research paved the way for understanding biases like the Gambler’s Fallacy.

Connected Thinking Frameworks

Convergent vs. Divergent Thinking

Convergent thinking occurs when the solution to a problem can be found by applying established rules and logical reasoning. Whereas divergent thinking is an unstructured problem-solving method where participants are encouraged to develop many innovative ideas or solutions to a given problem. Where convergent thinking might work for larger, mature organizations where divergent thinking is more suited for startups and innovative companies.

Critical Thinking

Critical thinking involves analyzing observations, facts, evidence, and arguments to form a judgment about what someone reads, hears, says, or writes.


The concept of cognitive biases was introduced and popularized by the work of Amos Tversky and Daniel Kahneman in 1972. Biases are seen as systematic errors and flaws that make humans deviate from the standards of rationality, thus making us inept at making good decisions under uncertainty.

Second-Order Thinking

Second-order thinking is a means of assessing the implications of our decisions by considering future consequences. Second-order thinking is a mental model that considers all future possibilities. It encourages individuals to think outside of the box so that they can prepare for every and eventuality. It also discourages the tendency for individuals to default to the most obvious choice.

Lateral Thinking

Lateral thinking is a business strategy that involves approaching a problem from a different direction. The strategy attempts to remove traditionally formulaic and routine approaches to problem-solving by advocating creative thinking, therefore finding unconventional ways to solve a known problem. This sort of non-linear approach to problem-solving, can at times, create a big impact.

Bounded Rationality

Bounded rationality is a concept attributed to Herbert Simon, an economist and political scientist interested in decision-making and how we make decisions in the real world. In fact, he believed that rather than optimizing (which was the mainstream view in the past decades) humans follow what he called satisficing.

Dunning-Kruger Effect

The Dunning-Kruger effect describes a cognitive bias where people with low ability in a task overestimate their ability to perform that task well. Consumers or businesses that do not possess the requisite knowledge make bad decisions. What’s more, knowledge gaps prevent the person or business from seeing their mistakes.

Occam’s Razor

Occam’s Razor states that one should not increase (beyond reason) the number of entities required to explain anything. All things being equal, the simplest solution is often the best one. The principle is attributed to 14th-century English theologian William of Ockham.

Lindy Effect

The Lindy Effect is a theory about the ageing of non-perishable things, like technology or ideas. Popularized by author Nicholas Nassim Taleb, the Lindy Effect states that non-perishable things like technology age – linearly – in reverse. Therefore, the older an idea or a technology, the same will be its life expectancy.


Antifragility was first coined as a term by author, and options trader Nassim Nicholas Taleb. Antifragility is a characteristic of systems that thrive as a result of stressors, volatility, and randomness. Therefore, Antifragile is the opposite of fragile. Where a fragile thing breaks up to volatility; a robust thing resists volatility. An antifragile thing gets stronger from volatility (provided the level of stressors and randomness doesn’t pass a certain threshold).


Ergodicity is one of the most important concepts in statistics. Ergodicity is a mathematical concept suggesting that a point of a moving system will eventually visit all parts of the space the system moves in. On the opposite side, non-ergodic means that a system doesn’t visit all the possible parts, as there are absorbing barriers

Systems Thinking

Systems thinking is a holistic means of investigating the factors and interactions that could contribute to a potential outcome. It is about thinking non-linearly, and understanding the second-order consequences of actions and input into the system.

Vertical Thinking

Vertical thinking, on the other hand, is a problem-solving approach that favors a selective, analytical, structured, and sequential mindset. The focus of vertical thinking is to arrive at a reasoned, defined solution.

Metaphorical Thinking

Metaphorical thinking describes a mental process in which comparisons are made between qualities of objects usually considered to be separate classifications.  Metaphorical thinking is a mental process connecting two different universes of meaning and is the result of the mind looking for similarities.

Maslow’s Hammer

Maslow’s Hammer, otherwise known as the law of the instrument or the Einstellung effect, is a cognitive bias causing an over-reliance on a familiar tool. This can be expressed as the tendency to overuse a known tool (perhaps a hammer) to solve issues that might require a different tool. This problem is persistent in the business world where perhaps known tools or frameworks might be used in the wrong context (like business plans used as planning tools instead of only investors’ pitches).

Peter Principle

The Peter Principle was first described by Canadian sociologist Lawrence J. Peter in his 1969 book The Peter Principle. The Peter Principle states that people are continually promoted within an organization until they reach their level of incompetence.

Straw Man Fallacy

The straw man fallacy describes an argument that misrepresents an opponent’s stance to make rebuttal more convenient. The straw man fallacy is a type of informal logical fallacy, defined as a flaw in the structure of an argument that renders it invalid.

Google Effect

The Google effect is a tendency for individuals to forget information that is readily available through search engines. During the Google effect – sometimes called digital amnesia – individuals have an excessive reliance on digital information as a form of memory recall.

Streisand Effect

The Streisand Effect is a paradoxical phenomenon where the act of suppressing information to reduce visibility causes it to become more visible. In 2003, Streisand attempted to suppress aerial photographs of her Californian home by suing photographer Kenneth Adelman for an invasion of privacy. Adelman, who Streisand assumed was paparazzi, was instead taking photographs to document and study coastal erosion. In her quest for more privacy, Streisand’s efforts had the opposite effect.

Compromise Effect

Single-attribute choices – such as choosing the apartment with the lowest rent – are relatively simple. However, most of the decisions consumers make are based on multiple attributes which complicate the decision-making process. The compromise effect states that a consumer is more likely to choose the middle option of a set of products over more extreme options.

Butterfly Effect

In business, the butterfly effect describes the phenomenon where the simplest actions yield the largest rewards. The butterfly effect was coined by meteorologist Edward Lorenz in 1960 and as a result, it is most often associated with weather in pop culture. Lorenz noted that the small action of a butterfly fluttering its wings had the potential to cause progressively larger actions resulting in a typhoon.

IKEA Effect

The IKEA effect is a cognitive bias that describes consumers’ tendency to value something more if they have made it themselves. That is why brands often use the IKEA effect to have customizations for final products, as they help the consumer relate to it more and therefore appending to it more value.

Ringelmann Effect 

Ringelmann Effect
The Ringelmann effect describes the tendency for individuals within a group to become less productive as the group size increases.

The Overview Effect

The overview effect is a cognitive shift reported by some astronauts when they look back at the Earth from space. The shift occurs because of the impressive visual spectacle of the Earth and tends to be characterized by a state of awe and increased self-transcendence.

House Money Effect

The house money effect was first described by researchers Richard Thaler and Eric Johnson in a 1990 study entitled Gambling with the House Money and Trying to Break Even: The Effects of Prior Outcomes on Risky Choice. The house money effect is a cognitive bias where investors take higher risks on reinvested capital than they would on an initial investment.


As highlighted by German psychologist Gerd Gigerenzer in the paper “Heuristic Decision Making,” the term heuristic is of Greek origin, meaning “serving to find out or discover.” More precisely, a heuristic is a fast and accurate way to make decisions in the real world, which is driven by uncertainty.

Recognition Heuristic

The recognition heuristic is a psychological model of judgment and decision making. It is part of a suite of simple and economical heuristics proposed by psychologists Daniel Goldstein and Gerd Gigerenzer. The recognition heuristic argues that inferences are made about an object based on whether it is recognized or not.

Representativeness Heuristic

The representativeness heuristic was first described by psychologists Daniel Kahneman and Amos Tversky. The representativeness heuristic judges the probability of an event according to the degree to which that event resembles a broader class. When queried, most will choose the first option because the description of John matches the stereotype we may hold for an archaeologist.

Take-The-Best Heuristic

The take-the-best heuristic is a decision-making shortcut that helps an individual choose between several alternatives. The take-the-best (TTB) heuristic decides between two or more alternatives based on a single good attribute, otherwise known as a cue. In the process, less desirable attributes are ignored.

Bundling Bias

The bundling bias is a cognitive bias in e-commerce where a consumer tends not to use all of the products bought as a group, or bundle. Bundling occurs when individual products or services are sold together as a bundle. Common examples are tickets and experiences. The bundling bias dictates that consumers are less likely to use each item in the bundle. This means that the value of the bundle and indeed the value of each item in the bundle is decreased.

Barnum Effect

The Barnum Effect is a cognitive bias where individuals believe that generic information – which applies to most people – is specifically tailored for themselves.

Anchoring Effect

The anchoring effect describes the human tendency to rely on an initial piece of information (the “anchor”) to make subsequent judgments or decisions. Price anchoring, then, is the process of establishing a price point that customers can reference when making a buying decision.

Decoy Effect

The decoy effect is a psychological phenomenon where inferior – or decoy – options influence consumer preferences. Businesses use the decoy effect to nudge potential customers toward the desired target product. The decoy effect is staged by placing a competitor product and a decoy product, which is primarily used to nudge the customer toward the target product.

Commitment Bias

Commitment bias describes the tendency of an individual to remain committed to past behaviors – even if they result in undesirable outcomes. The bias is particularly pronounced when such behaviors are performed publicly. Commitment bias is also known as escalation of commitment.

First-Principles Thinking

First-principles thinking – sometimes called reasoning from first principles – is used to reverse-engineer complex problems and encourage creativity. It involves breaking down problems into basic elements and reassembling them from the ground up. Elon Musk is among the strongest proponents of this way of thinking.

Ladder Of Inference

The ladder of inference is a conscious or subconscious thinking process where an individual moves from a fact to a decision or action. The ladder of inference was created by academic Chris Argyris to illustrate how people form and then use mental models to make decisions.

Goodhart’s Law

Goodhart’s Law is named after British monetary policy theorist and economist Charles Goodhart. Speaking at a conference in Sydney in 1975, Goodhart said that “any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes.” Goodhart’s Law states that when a measure becomes a target, it ceases to be a good measure.

Six Thinking Hats Model

The Six Thinking Hats model was created by psychologist Edward de Bono in 1986, who noted that personality type was a key driver of how people approached problem-solving. For example, optimists view situations differently from pessimists. Analytical individuals may generate ideas that a more emotional person would not, and vice versa.

Mandela Effect

The Mandela effect is a phenomenon where a large group of people remembers an event differently from how it occurred. The Mandela effect was first described in relation to Fiona Broome, who believed that former South African President Nelson Mandela died in prison during the 1980s. While Mandela was released from prison in 1990 and died 23 years later, Broome remembered news coverage of his death in prison and even a speech from his widow. Of course, neither event occurred in reality. But Broome was later to discover that she was not the only one with the same recollection of events.

Crowding-Out Effect

The crowding-out effect occurs when public sector spending reduces spending in the private sector.

Bandwagon Effect

The bandwagon effect tells us that the more a belief or idea has been adopted by more people within a group, the more the individual adoption of that idea might increase within the same group. This is the psychological effect that leads to herd mentality. What in marketing can be associated with social proof.

Moore’s Law

Moore’s law states that the number of transistors on a microchip doubles approximately every two years. This observation was made by Intel co-founder Gordon Moore in 1965 and it become a guiding principle for the semiconductor industry and has had far-reaching implications for technology as a whole.

Disruptive Innovation

Disruptive innovation as a term was first described by Clayton M. Christensen, an American academic and business consultant whom The Economist called “the most influential management thinker of his time.” Disruptive innovation describes the process by which a product or service takes hold at the bottom of a market and eventually displaces established competitors, products, firms, or alliances.

Value Migration

Value migration was first described by author Adrian Slywotzky in his 1996 book Value Migration – How to Think Several Moves Ahead of the Competition. Value migration is the transferal of value-creating forces from outdated business models to something better able to satisfy consumer demands.

Bye-Now Effect

The bye-now effect describes the tendency for consumers to think of the word “buy” when they read the word “bye”. In a study that tracked diners at a name-your-own-price restaurant, each diner was asked to read one of two phrases before ordering their meal. The first phrase, “so long”, resulted in diners paying an average of $32 per meal. But when diners recited the phrase “bye bye” before ordering, the average price per meal rose to $45.


Groupthink occurs when well-intentioned individuals make non-optimal or irrational decisions based on a belief that dissent is impossible or on a motivation to conform. Groupthink occurs when members of a group reach a consensus without critical reasoning or evaluation of the alternatives and their consequences.


A stereotype is a fixed and over-generalized belief about a particular group or class of people. These beliefs are based on the false assumption that certain characteristics are common to every individual residing in that group. Many stereotypes have a long and sometimes controversial history and are a direct consequence of various political, social, or economic events. Stereotyping is the process of making assumptions about a person or group of people based on various attributes, including gender, race, religion, or physical traits.

Murphy’s Law

Murphy’s Law states that if anything can go wrong, it will go wrong. Murphy’s Law was named after aerospace engineer Edward A. Murphy. During his time working at Edwards Air Force Base in 1949, Murphy cursed a technician who had improperly wired an electrical component and said, “If there is any way to do it wrong, he’ll find it.”

Law of Unintended Consequences

The law of unintended consequences was first mentioned by British philosopher John Locke when writing to parliament about the unintended effects of interest rate rises. However, it was popularized in 1936 by American sociologist Robert K. Merton who looked at unexpected, unanticipated, and unintended consequences and their impact on society.

Fundamental Attribution Error

Fundamental attribution error is a bias people display when judging the behavior of others. The tendency is to over-emphasize personal characteristics and under-emphasize environmental and situational factors.

Outcome Bias

Outcome bias describes a tendency to evaluate a decision based on its outcome and not on the process by which the decision was reached. In other words, the quality of a decision is only determined once the outcome is known. Outcome bias occurs when a decision is based on the outcome of previous events without regard for how those events developed.

Hindsight Bias

Hindsight bias is the tendency for people to perceive past events as more predictable than they actually were. The result of a presidential election, for example, seems more obvious when the winner is announced. The same can also be said for the avid sports fan who predicted the correct outcome of a match regardless of whether their team won or lost. Hindsight bias, therefore, is the tendency for an individual to convince themselves that they accurately predicted an event before it happened.

Read Next: BiasesBounded RationalityMandela EffectDunning-Kruger EffectLindy EffectCrowding Out EffectBandwagon Effect.

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