simpson-paradox

The Simpson Paradox And Why It Matters In Business

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In statistics, the Simpson Paradox happens when a trend clearly shows up in clusters/brackets of data. But it disappears or, at worse it reverses when the data is grouped and combined. In short, the Simpson paradox shows that when the data moves from clusters to combined data, it hides several distributions, which end up creating a biased overall effect.

AspectExplanation
DefinitionSimpson’s Paradox, named after British statistician Edward H. Simpson, is a statistical paradox where a trend or association observed within subgroups of data can reverse or disappear when the subgroups are combined. In other words, what seems true for individual parts of the data may not hold when the data is analyzed as a whole.
Key Concepts1. Subgroup vs. Aggregate: The paradox revolves around the distinction between examining data within subgroups (i.e., disaggregated data) and analyzing the data as a whole (i.e., aggregated data).
2. Causality vs. Association: Simpson’s Paradox highlights the difference between a causal relationship and a statistical association. An apparent association between variables may not imply causality when considering the entire dataset.
Causes1. Heterogeneous Subgroups: Simpson’s Paradox often occurs when subgroups within the dataset have significantly different characteristics or sample sizes. These differences can lead to skewed results when aggregated.
2. Hidden Variables: Sometimes, there are unobserved or unaccounted-for variables that influence both the grouping and the outcome, resulting in the paradoxical reversal of trends.
3. Weighted Averages: Aggregating data with unequal sample sizes can give disproportionate weight to certain subgroups, affecting the overall trend.
Examples1. Medical Studies: Simpson’s Paradox is commonly encountered in medical research. A treatment that appears to be less effective in a subgroup may be more effective when considering the entire patient population.
2. Educational Outcomes: Test scores within different schools or districts may suggest that one school performs better, but when considering all schools together, a different conclusion may emerge.
3. Sports Statistics: A baseball player may have a higher batting average in different seasons or against different teams, but the overall average for all seasons may be lower.
Consequences1. Misleading Interpretations: Failing to recognize Simpson’s Paradox can lead to incorrect conclusions and potentially poor decision-making based on aggregated data.
2. Inaccurate Policies: In areas like healthcare or education, misinterpreting data can result in the implementation of policies that are ineffective or even detrimental.
3. Loss of Insights: If analysts focus solely on aggregated data, they may overlook valuable insights that exist within subgroups.
Mitigation Strategies1. Data Disaggregation: Consider analyzing and reporting data at both the subgroup and aggregate levels to gain a comprehensive understanding.
2. Identifying Confounding Variables: Carefully examine potential confounding variables that might influence the relationship between the variables under study.
3. Transparent Reporting: When presenting data, clearly communicate the presence of Simpson’s Paradox, especially if it could impact decision-making.
4. Expert Consultation: Seek input from statistical experts or data analysts to ensure the validity of your interpretations, especially when working with complex datasets.
ConclusionSimpson’s Paradox serves as a reminder of the nuances and potential pitfalls in statistical analysis. It underscores the importance of considering data from multiple angles and being cautious when drawing conclusions based on aggregated information. By understanding and addressing the paradox, analysts and decision-makers can make more informed choices and avoid misinterpretations.

The Simpson paradox origin story

As Tom Grigg explained exceptionally well, the Simpson paradox took its name from Edward Hugh Simpson thanks to a technical paper in 1951.

Yet it was made famous when another statistician, Peter Bickel, was called – in 1971 – to analyze the admission data at UC Berkley’s suspected gender bias.

As the story goes, the university feared a lawsuit, so they had the data analyzed by Bickel.

When the data were combined, it really gave the impression that more males had been selected over women.

In fact, of the total male applicants, 44% were selected, and of the total female applicants, 35% were selected.

Yet when the data were analyzed by the department, it showed something completely different.

The admissions were biased toward women in four of the six departments analyzed.

But, as women applied to departments where fewer applicants were selected when the data combined, it gave an impression of bias toward male applicants.

Understanding the Simpson paradox

A good example is Nassim Taleb’s video on the topic.

While this is related to vaccine data, it can be easily translated into business, as we’ll see.

As Taleb explained about the vaccine data.

When the data are grouped under the same umbrella, after having been analyzed in clusters and homogeneous groups, it suddenly gives an opposite effect.

It’s like the data not only doesn’t give the same result when analyzed in brackets, but it gives the reverse effect.

This is what happens when the Simpson paradox messes up the statistics data.

Why?

Intuitively, when data, before compared under brackets, get combined, it disperses, thus making that worthless for the initial scope.

In the case of the vaccine, because many people over 60s were vaccinated, and a few people under 20s were vaccinated, when the data gets combined, it’s skewed toward the mortality of people over 60s, thus creating a bias and.

Beware of the Lurking variable

To keep things short, hidden variables in the combined spurs the overall analysis, making it worthless.

This is known as a “lurking variable” or a variable that affects the data at the point of creating a “spurious association” (in short, the cause-effect relationship ceases).

The Simpson paradox in business

The Simpson paradox can hide in many of the business and marketing analyses, as when the data is combined, it’s easy to mistake a correlation for causation.

Take the case of, as explained by adexchanger.com, for instance, when deciding on a programmatic campaign, when looking at the data for gender only, it shows how the male budget has seemingly more conversions, thus skewing the data toward males.

Yet from an age analysis, you figure that females between 18-24 have higher conversion rates.

If you don’t understand this bias, it’s easy to overspend on an overrepresented audience, not because it’s more aligned with your audience but because you’re misreading the data.

And as you can imagine, this can have substantial consequences on your bottom line (money wasted on ineffective campaigns and lost revenues as you’re not targeting the right audience).

Quantitative vs. Qualitative Research

Dealing with data is extremely hard.

It’s one of the hardest things in business.

And as most businesses now have a lot of data available, it’s easy to fall into the trapping of misusing it.

For that, it’s critical to establish project business processes, whereas it gets clear to the internal team when to use quantitative vs. qualitative data or both.

characteristics-of-quantitative-research-characteristics-of-quantitative-research
The characteristics of quantitative research contribute to methods that use statistics to make generalizations about something. These generalizations are constructed from data used to find patterns and averages and causal test relationships.

Quantitative research, if used in the proper context, can be incredibly effective.

Companies like Amazon have learned how to balance that with qualitative research.

characteristics-of-qualitative-research
Qualitative research is performed by businesses that acknowledge the human condition and want to learn more about it. Some of the key characteristics of qualitative research that enable practitioners to perform qualitative inquiry comprise small data, the absence of definitive truth, the importance of context, the researcher’s skills and are of interests.

Indeed, quantitative data is extremely helpful to improve business processes.

However, it’s critical to know when human judgment needs to kick in, when some qualitative data is available that completely flips things upside down.

For instance, companies like Amazon have launched successful projects, like reviews, Kindle, Prime, and third-party stores, which were absolutely the result of human judgment rather than quantitative understanding.

Indeed, if Amazon was going to look into these endeavors with a quantitative mindset, it would have never undertaken them as they did not make sense from a quantitative standpoint.

Yet, the intuitive understanding of how those things that might seem negative from a first-order effect standpoint (losing profits in the short-term) might make complete sense from a second-order effect standpoint (becoming way more successful in the long run).

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 any eventuality. It also discourages the tendency for individuals to default to the most obvious choice.

Understanding the implications of second-order effects is something that qualitative understanding and human judgment together can do.

Whereas quantitative data can be extremely useful to improve, in the short-term, business processes to make them way more efficient, which also, in the long-term, if properly used can create a competitive moat for the business.

For instance, going back to Amazon’s example, the company processes like inventory management and order fulfillment are part of its core strategic advantage, and they are driven by quantitative data!

Case Studies

  • Healthcare:
    • Scenario: A hospital wants to determine which treatment is more effective for a certain illness. At first glance, Treatment A seems to have a higher recovery rate than Treatment B. However, when data is broken down by severity of illness, Treatment B is more effective for severe cases, while Treatment A is more effective for mild cases.
    • Simpson’s Paradox: The aggregate data suggests Treatment A is better, but a more detailed analysis shows that Treatment B is better for severe cases.
  • Sports:
    • Scenario: A baseball player, Player X, has a higher batting average than Player Y in both the first and second half of a season. However, when combining the two halves, Player Y has a higher overall batting average.
    • Simpson’s Paradox: Individual performance in each half of the season does not necessarily predict overall performance.
  • Economics:
    • Scenario: A country’s unemployment rate decreases both this year and the previous year. However, when looking at the two-year period as a whole, the unemployment rate has increased.
    • Simpson’s Paradox: Annual data may show positive trends, but longer-term trends might reveal a different story.
  • Education:
    • Scenario: Students from School A score higher on math tests than students from School B in both 9th and 10th grades. However, when combining scores from both grades, students from School B have a higher average.
    • Simpson’s Paradox: Performance in individual grades doesn’t necessarily predict overall academic performance.
  • Real Estate:
    • Scenario: City A has seen a decline in house prices in both the east and west sectors. However, overall, the city’s house prices have increased.
    • Simpson’s Paradox: Individual sectors of the city might show a decline, but the overall city might see an increase due to factors in smaller unexamined areas.
  • Environment:
    • Scenario: Factory A reduces its carbon emissions in both 2020 and 2021. Factory B increases its emissions in both years. However, when the total emissions of both years are combined, Factory A has a larger increase in emissions than Factory B.
    • Simpson’s Paradox: Individual yearly reductions can be overshadowed by larger overall increases when data is combined.
  • Transportation:
    • Scenario: Car model X has fewer accidents than car model Y in both urban and rural settings. However, when combining the data, car model Y has fewer accidents in total.
    • Simpson’s Paradox: Safety performance in individual settings doesn’t necessarily predict overall safety performance.

Key takeaways

  • The Simpson paradox is an effect that in statistics and probability can create biased analyses. In fact, when present the data combined from an analysis gives a reverse effect compared to the data analyzed in buckets.
  • The Simpson paradox can create biased analyses also in business and marketing creating overspending toward the wrong audience.
  • The Simpson paradox also makes it much harder to make decisions in business when doing statistical analysis.

Key highlights

  • Definition of the Simpson Paradox: The Simpson Paradox is an effect in statistics and probability where a trend appears in clusters of data but disappears or reverses when the data is combined, leading to biased overall effects.
  • Origin and Famous Case: The paradox is named after Edward Hugh Simpson and gained fame when statistician Peter Bickel analyzed UC Berkeley’s admission data, revealing biases in gender representation.
  • Occurrence in Business and Marketing: The Simpson Paradox can hide in business and marketing analyses, leading to mistaken correlations for causation and overspending on misinterpreted data.
  • Impact of Hidden Variables: Hidden variables, known as “lurking variables,” affect combined data, causing spurious associations and disrupting cause-effect relationships.
  • Importance of Proper Data Analysis: Proper data analysis and understanding when to use quantitative and qualitative research can mitigate the effects of the Simpson Paradox in business decision-making.
  • Balancing Quantitative and Qualitative Research: Companies like Amazon have demonstrated the importance of balancing quantitative data with qualitative understanding and human judgment for more effective decision-making.
  • Strategic Implications: Understanding the implications of second-order effects and combining qualitative understanding with quantitative data can create competitive advantages and long-term success in business.

Connected Thinking Frameworks

Convergent vs. Divergent Thinking

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

Biases

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.

Second-Order Thinking

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
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
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

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

occams-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

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

antifragility
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

ergodicity
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
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
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
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

einstellung-effect
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

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

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

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

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

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

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

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

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

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.

Heuristic

heuristic
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

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

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

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

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

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

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

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
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
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

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

goodharts-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

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

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

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

Bandwagon Effect

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

moores-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
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
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

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

groupthink
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.

Stereotyping

stereotyping
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

murphys-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

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
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
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
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: Biases, Bounded Rationality, Mandela Effect, Dunning-Kruger Effect, Lindy Effect, Crowding Out Effect, Bandwagon Effect.

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