Outcome bias is judging a decision by how it turned out rather than by how sound it was given what was known when it was made. Psychologists Jonathan Baron and John Hershey showed it in 1988: people rated the same decision, and the person who made it, more favourably when the outcome happened to be good, even though they said outcomes should not count.
Someone makes a call with incomplete information and some element of chance.
02
The result arrives
Luck and outside events shape the outcome as much as the choice did.
03
The result becomes the grade
Observers rate the thinking, and the person, by the outcome rather than the process.
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Lessons get distorted
Lucky bad decisions are repeated and unlucky good ones are abandoned.
Where you see it today
AI pilot reviews
A first AI pilot that happens to land gets scaled; one that hits a data problem gets cancelled. Neither verdict says much about whether the plan was sound, which is what Baron and Hershey found people struggle to separate (Baron and Hershey, 1988).
Training AI on outcomes vs process
AI labs face a version of the same problem. Researchers compared rewarding a model only for final answers with rewarding each reasoning step, and found step-level (process) supervision trained more reliable maths solvers (Lightman et al., 2023).
Investment and venture
A bet that paid off is often treated as proof of skill. Investors who keep decision journals try to record the reasoning before the result is known, so they can later tell skill from luck.
Performance reviews
Sales or trading results reward people for outcomes that partly reflect market conditions. Reviewing how decisions were made, not only what they produced, gives a fairer and more useful read.
Outcome bias vs related ideas
Outcome bias
What goes wrongDecision quality is judged by the result
Example"The launch flopped, so it was a bad call."
Hindsight bias
What goes wrongThe result seems to have been predictable all along
Example"We always knew that would fail."
Survivorship bias
What goes wrongOnly the winners are studied
ExampleCopying the habits of successful founders only
Fundamental attribution error
What goes wrongResults are pinned on character, not circumstances
Example"She lost the deal because she is careless."
Process review (the fix)
What goes wrongJudges the reasoning at the time
Example"Given what we knew, was this a good bet?"
How to spot and avoid it
Write down the reasoning, expected range of outcomes and key assumptions before the result is known.
In reviews, ask first what was knowable at the time, then look at the result.
Grade AI pilots on design, data and evaluation method, not only on the headline result.
Look at several decisions of the same kind together, since one outcome says little about skill.
Reward people for good process even when luck goes against them.
The Business Engineer lens
AI makes it cheap to run many small experiments, which only helps if the organisation learns from process and not from a few lucky or unlucky results. The Business Engineer argues that judging AI bets on their design and fit, industry by industry, is what separates durable advantage from noise.
The complete explainer, cleaned up and reformatted for mobile.
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.
Outcome Bias
ExplanationOutcome bias is a cognitive bias that occurs when the evaluation of a decision or action is based on the outcome or result, rather than the quality of the decision-making process. In other words, it involves judging the wisdom of a choice based on how things turned out, rather than whether the decision was reasonable given the information available at the time. Outcome bias can lead to unfair assessments of decisions and can hinder learning and improvement.
Key Concepts
Explanation– Judging Decisions by Outcomes: Outcome bias involves the tendency to judge the quality of a decision or action based on whether it led to a positive or negative result. – Hindsight Bias: It is closely related to hindsight bias, where individuals believe that events were predictable after they have occurred. – Risk and Uncertainty: Outcome bias is often influenced by the level of risk and uncertainty associated with a decision.
Examples
Explanation– Investment Decisions: An investment manager may be praised for a risky investment that happened to yield high returns but criticized for a similarly risky investment that resulted in losses. – Medical Diagnoses: A doctor’s diagnosis may be viewed as competent if a patient recovers, even if the diagnosis was based on uncertain symptoms.
Effect on Decision-Making
Explanation– Risk Aversion: The fear of negative outcomes due to outcome bias can lead individuals to become risk-averse and avoid making decisions with uncertain outcomes. – Inhibition of Innovation: In organizations, outcome bias can discourage innovation and experimentation, as individuals fear negative repercussions for unsuccessful ventures.
Mitigation
Explanation– Focus on Process, Not Just Outcomes: To mitigate outcome bias, it’s essential to evaluate decisions based on the quality of the decision-making process, including the information available and the reasoning behind the choice. – Encourage Learning: Creating a culture that encourages learning from both successes and failures can help mitigate the negative effects of outcome bias.
Cognitive Biases
ExplanationOutcome bias is related to several other cognitive biases, including: – Confirmation Bias: The tendency to search for, interpret, and remember information that confirms one’s preconceptions. – Hindsight Bias: The belief that events were predictable after they have already occurred. – Overconfidence Bias: The tendency to overestimate one’s own abilities and the accuracy of one’s beliefs.
Applications
Explanation– Legal System: Outcome bias can influence legal judgments, where the outcome of a case can disproportionately affect perceptions of the fairness of a legal decision. – Performance Evaluation: In the workplace, employees may be evaluated based on the success or failure of specific projects, rather than on their competence and effort.
Implications
Explanation– Fairness: Outcome bias can lead to unfair evaluations of decisions and actions, as individuals are often not in control of external factors that influence outcomes. – Innovation and Risk-Taking: Organizations may discourage innovation and risk-taking if they excessively punish failures without considering the decision-making process.
Learning and Improvement
ExplanationTo promote learning and improvement while mitigating outcome bias: – Encourage a culture of transparency, where individuals feel safe admitting mistakes. – Emphasize the importance of reviewing and learning from both successful and unsuccessful outcomes. – Focus on the decision-making process and the factors that were within an individual’s control.
Conclusion
ExplanationOutcome bias is a cognitive bias that can significantly impact decision-making, fairness, and learning. By recognizing its presence and taking steps to mitigate its effects, individuals and organizations can make more informed and fairer evaluations of decisions, fostering a culture of continuous improvement and innovation.
Understanding outcome bias
Outcome bias is common in humans because we tend to be self-evaluative.
We tend to look back at what we’ve done and use any lessons learned to measure our future performance.
This can be a useful trait in some circumstances, but it can also be a problem when something bad happens.
When a decision results in a poor outcome, we tend to place more importance on the outcome of a decision.
We may be overly self-critical or indeed critical of others when compared to instances where a decision resulted in a positive outcome.
It does not matter if the decision-making process was well considered or if the likelihood of success was down to chance.
This is not to say that outcome bias does not occur when there is a favorable outcome.
Consider an individual who decides to invest in real estate after learning that a friend made a significant capital gain.
Outcome bias causes the individual to become preoccupied with how much money was made and in the process, ignore the mechanisms behind their friend’s success.
Perhaps a government stimulus package for new home builders was a contributing factor, or maybe a combination of low-interest rates and a knack for identifying undervalued property was the cause.
The outcome bias in business
In business, an overemphasis on performance is creating an outcome-centric culture in which someone must lose in order for someone else to win.
As a result, outcome bias is present in many performance-related situations including:
Recruitment
A hiring manager is only considered successful if the employee they recruit performs well.
With less emphasis on the reasoned and fair recruitment process, employees are led to believe that they are either good at their job or bad at their job.
When evaluations are based on a binary result and not on the quality of an employee’s decision-making, good luck is rewarded over competence or expertise.
Product management has become a key role within most organizations and startups as it combines product development with experimentation to create a successful product in the market. Product management requires a combination of strategic thinking, problem-solving skills, and a relentless focus on customer needs and delivering the right product at the right time. Top product managers use a customer obsession approach to build and launch successful products.
Products are judged according to how well they were received in the market, rather than the product development-related processes and systems that made the product a reality in the first place.
Leadership
Once an outcome is known, the outcome bias also hinders our ability to evaluate whether a leadership decision was good or bad.
Fearful of negative repercussions, outcome bias can make some leaders risk-averse.
Conversely, irresponsible leaders who make reckless decisions are rewarded if their decision results in a positive outcome.
In this case, the subordinates who doubted the leader’s ability may be subject to harsh treatment from others.
Avoiding outcome bias
Critical thinking is one way of avoiding outcome bias. Instead of focusing on outcomes, we need to focus on the process as a whole.
Like many cognitive biases, however, outcome bias can be difficult to address on our own.
We may sabotage ourselves by quitting too early or ignoring certain information we don’t like.
In this situation, it can be helpful to collaborate with a colleague or superior to understand the underlying causes of the bias.
In any case, consider these questions
What led us to make the decision?
Was there a better process we could have followed in making the decision?
Could we have liaised with other people?
What information did we have at our disposal? What information did we not have?
Could we have obtained more data?
Was it necessary to decide at the point the decision was made?
Were there previously unknown external factors that may have skewed the decision?
Outcome bias and hindsight bias
As we saw, outcome bias can be pretty tricky.
Indeed, in the current business world, where we all claim to be looking at results, it can be very easy to fall into the trap of overestimating the outcome toward understanding whether the process makes sense in the first place.
The combination of the outcome bias with other fallacies might lead to a complete misjudgment of business events.
Indeed, when judging for outcomes, it’s critical not to fall into the 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.
This can lead to underestimating, for instance, the outcome of business events by overestimating our own ability to predict the future based on the past.
In short, take the case of a classic example in business, where a product like the BlackBerry phone was overtaken by the iPhone.
While in hindsight, it’s very easy to perform all analyses and conclude that it was clear that the iPhone was on a path to disrupt the BlackBerry.
That is a huge fallacy as if you were living in the moment, the real world was way more ambiguous, opaque and noisy than we like to admit.
In this specific case, we fall into the trap of overestimating our ability to analyze the past and underestimate the ability of BlackBerry’s management team (of the time) to respond to the threat of the iPhone!
We might want to call it also “the analyst bias” or the belief that you can look at past events with today’s understanding of them.
That, in turn, might lead to overestimating one’s ability to predict the future while underestimating other people’s ability to do the same.
Thus, it’s critical to balance out this bias by looking at the past and understanding that, while things could have been done differently, it’s also hard to predict future events based on what’s happening now.
As the real world is extremely noisy, opaque, and non-linear.
In the case of the iPhone, for instance, it might be that BlackBerry’s management did understand the threat but could not move fast enough, as the iPhone took off so quickly that, like with a snowball, BlackBerry found it swept up, in a very short time range.
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.
Whereas we overstate the behavior of an individual and his characteristics while understating the context and environment in which this individual acted.
A classic example is in all the self-improvement literature, which looks at individual success as if it was a predictable path, yet as usual, in hindsight.
For instance, books that focus on the features of successful individuals are often skewed toward over-emphasizing the personal habits of those people, as those have a direct relationship with the outcome.
Take the case of the classic self-improvement book which looks at the habits of successful people.
Yet, this doesn’t tell you that many unsuccessful people might also carry the same habits.
In other words, these habits are selected in hindsight based on the outcome rather than considering that those might be random traits shared by many individuals.
And yet, most of those individuals who carry those habits don’t turn successful.
Take the case of statements like “successful people wake up early.”
As if all the people that wake up very early are successful.
This leads to many more people following false patterns, believing that those are what creates success rather than focusing on building their own way of doing things.
Outcome bias and survivorship bias
Another huge risk when falling into the outcome bias is to look at successful people and try to make a pattern of it.
Survivorship bias is a pervasive fallacy that exists in business, where people focus on the few survived players, in any given market, without realizing that most initial players in that given market are dead, or went into oblivion. In short, survivorship bias transforms the past into a linear story, by removing uncertainty from it.
Going back to the self-improvement literature, which studies successful people, often their habits and features are overstated to make the case and sell more of these books.
But in reality, this literature only studies what’s visible now without considering what’s not visible anymore.
For instance, take the case of the book, which shows you what great companies do by looking at the list of dominant/leading companies in the marketplace.
It’s easy to extrapolate successful processes from these companies as if you can also build a successful company by copying them.
Yet, this falls into the outcome bias, where many other companies which followed the same procedures didn’t make it at all!
Case Studies
Sports:
Decision: A soccer coach decides to substitute a key player during a crucial game.
Outcome: The substitute player scores the winning goal.
Bias: Everyone praises the coach’s decision as brilliant, even if the substitution was due to the key player being injured and not a strategic move.
Investments:
Decision: An investor decides to put a significant portion of their portfolio into a relatively unknown start-up.
Outcome: The start-up becomes the next big tech giant.
Bias: Everyone considers the investor a genius, ignoring the countless other similar bets that didn’t pan out.
Medical:
Decision: A doctor decides to try an experimental treatment on a critically ill patient.
Outcome: The patient recovers fully.
Bias: The doctor is hailed as innovative and brilliant, even though the decision had a high risk of not succeeding.
Entertainment:
Decision: A movie director decides to cast an unknown actor in the lead role of a big-budget film.
Outcome: The movie becomes a blockbuster, and the actor becomes an overnight sensation.
Bias: The director’s decision is labeled as visionary, even though many factors could have contributed to the movie’s success.
Business Strategy:
Decision: A CEO decides to pivot the company into a completely new product line.
Outcome: The new product line becomes a market leader.
Bias: The CEO’s decision is seen as a masterstroke, even if the decision was more of a desperate move due to failing existing products.
Research & Development:
Decision: A research team decides to abandon a project they’ve been working on for years to pursue a new idea.
Outcome: The new idea leads to a revolutionary discovery.
Bias: The team’s decision is seen as a stroke of genius, overlooking the resources and time spent on the abandoned project.
Marketing:
Decision: A marketing manager decides to invest heavily in an unconventional advertising campaign.
Outcome: The campaign goes viral, leading to record sales.
Bias: The manager’s risky decision is celebrated, even though many similar campaigns fail to make an impact.
Politics:
Decision: A leader decides to take a controversial stand on a divisive issue.
Outcome: Public opinion shifts in their favor, and they win the next election.
Bias: The leader’s decision is seen as bold and visionary, ignoring the myriad of other factors that influence elections.
Education:
Decision: A teacher decides to implement a new teaching method in the classroom.
Outcome: Students’ grades improve significantly.
Bias: The teacher’s method is hailed as revolutionary, even though other external factors (like smaller class sizes or additional resources) might have contributed.
Real Estate:
Decision: A property developer decides to invest in a dilapidated area of the city.
Outcome: The area becomes a trendy hotspot, and property values soar.
Bias: The developer’s decision is seen as a masterful understanding of market trends, even if other macro factors (like improved public transport or city-wide development initiatives) played a part.
Key Takeaways
Outcome bias occurs when a decision is based on the outcome of previous events without regard for how those events developed.
Outcome bias in business tends to occur in the recruitment process, product development, and leadership. Most conspire to create an outcome-centric culture in organizations where one person has to lose for another to win.
Outcome bias can be avoided with critical thinking and a commitment to focusing on processes. Discussing the bias with a trusted colleague or supervisor can be a good way to uncover its underlying causes.
Key Highlights
Outcome Bias: Outcome bias is the tendency to evaluate a decision based on its outcome rather than considering the process by which the decision was made.
Self-Evaluative Nature: Humans are self-evaluative beings, often using the outcome of decisions to measure future performance.
Positive and Negative Outcomes: Outcome bias can occur when a decision results in either a positive or negative outcome.
Business Application: Outcome bias can be present in various business situations, such as recruitment, product development, and leadership decisions.
Avoiding Outcome Bias: To avoid outcome bias, critical thinking is essential, focusing on the decision-making process rather than just the outcome.
Hindsight Bias: Be cautious not to fall into hindsight bias, perceiving past events as more predictable than they actually were.
Attribution Error: Avoid fundamental attribution error by considering environmental and situational factors, not just personal characteristics, when judging the behavior of others.
Survivorship Bias: Be aware of survivorship bias, which focuses on successful individuals or companies without considering the failures that may have occurred in the same context.
Outcome Bias
DescriptionOutcome bias is a cognitive bias that involves judging the quality of a decision based on its outcome rather than the quality of the decision-making process itself. It occurs when individuals or organizations evaluate past decisions as good or bad solely based on the resulting outcome, rather than considering the factors and information available at the time of decision-making.
When to Apply– When discussing decision-making processes and performance evaluations. – Particularly in understanding how biases influence judgments about past decisions and the importance of assessing decision quality independent of outcomes.
Hindsight Bias
DescriptionHindsight bias is a cognitive bias that involves believing that an event was predictable or foreseeable after it has occurred, even if there was little or no objective basis for predicting it beforehand. It leads individuals to overestimate their ability to predict outcomes retrospectively, based on their knowledge of the actual outcome, rather than acknowledging the uncertainty and ambiguity inherent in decision-making.
When to Apply– When discussing risk assessment and learning from past experiences. – Particularly in understanding how biases distort perceptions of past events and the challenges of learning from history without succumbing to overconfidence or hindsight bias.
Confirmation Bias
DescriptionConfirmation bias is a cognitive bias that involves seeking, interpreting, or recalling information in a way that confirms one’s preexisting beliefs or hypotheses while ignoring or discounting contradictory evidence. It leads individuals to selectively perceive information that supports their existing views, reinforcing their biases and reducing their willingness to consider alternative perspectives or information.
When to Apply– When discussing information processing and critical thinking. – Particularly in understanding how biases shape information interpretation and decision-making, and in exploring strategies to mitigate confirmation bias and promote open-mindedness and objectivity.
Overconfidence Bias
DescriptionOverconfidence bias is a cognitive bias that involves overestimating one’s own abilities, knowledge, or judgment relative to objective criteria or the actual outcomes of one’s decisions. It leads individuals to have excessive confidence in their predictions, assessments, or skills, often resulting in suboptimal decision-making, increased risk-taking, and vulnerability to errors or failures.
When to Apply– When discussing risk management and self-awareness. – Particularly in understanding how biases influence risk perceptions and decision-making, and in exploring techniques to counter overconfidence bias, such as probabilistic thinking, scenario analysis, and peer feedback.
Anchoring Bias
DescriptionAnchoring bias is a cognitive bias that involves relying too heavily on the first piece of information encountered (the “anchor”) when making decisions, even if subsequent information contradicts or outweighs the initial anchor. It leads individuals to insufficiently adjust their judgments away from the initial reference point, resulting in systematic errors in judgment and decision-making.
When to Apply– When discussing negotiation strategies and pricing decisions. – Particularly in understanding how biases influence judgment formation and negotiation outcomes, and in exploring techniques to mitigate anchoring bias, such as considering multiple reference points, using objective benchmarks, and employing decision aids.
Availability Heuristic
DescriptionAvailability heuristic is a mental shortcut that involves estimating the likelihood of an event or the frequency of a phenomenon based on the ease with which relevant instances or examples come to mind. It leads individuals to overestimate the probability of events that are more readily available in memory, such as vivid or recent experiences, while underestimating the likelihood of less memorable events.
When to Apply– When discussing risk perception and probability judgment. – Particularly in understanding how biases influence risk assessments and decision-making under uncertainty, and in exploring strategies to counter availability heuristic, such as diversifying information sources, considering base rates, and using systematic decision frameworks.
Sunk Cost Fallacy
DescriptionSunk cost fallacy is a cognitive bias that involves making decisions based on past investments of time, money, or resources, rather than the expected future outcomes or benefits. It leads individuals to continue investing in a course of action despite evidence suggesting it is no longer the best choice, solely because of the resources already committed, resulting in irrational decision-making and loss aversion.
When to Apply– When discussing resource allocation and decision justification. – Particularly in understanding how biases influence decision inertia and escalation of commitment, and in exploring ways to overcome sunk cost fallacy, such as focusing on future costs and benefits, separating sunk costs from future decisions, and seeking objective feedback.
Loss Aversion
DescriptionLoss aversion is a cognitive bias that involves the tendency to prefer avoiding losses over acquiring equivalent gains. It leads individuals to perceive losses as more significant or painful than equivalent gains, resulting in risk aversion, reluctance to change, and suboptimal decision-making aimed at minimizing potential losses rather than maximizing gains.
When to Apply– When discussing risk tolerance and investment behavior. – Particularly in understanding how biases influence attitudes towards risk and decision-making under uncertainty, and in exploring techniques to mitigate loss aversion, such as reframing decisions, diversifying portfolios, and focusing on long-term objectives.
Framing Effect
DescriptionFraming effect is a cognitive bias that involves making different decisions based on how information is presented or framed, rather than the actual content of the information. It occurs when individuals react differently to the same information depending on whether it is presented in terms of potential gains or losses, highlighting the subjective nature of decision-making and the influence of framing on preferences and choices.
When to Apply– When discussing communication strategies and marketing tactics. – Particularly in understanding how biases influence consumer behavior and decision-making, and in exploring techniques to mitigate framing effect, such as providing balanced information, using neutral framing, and emphasizing objective criteria.
Escalation of Commitment
DescriptionEscalation of commitment is a behavioral phenomenon that involves persisting with a failing course of action or investment despite negative outcomes, in order to justify past investments or avoid admitting failure. It occurs when individuals or organizations increase their commitment to a decision or project in the face of mounting losses or evidence of poor performance, leading to irrational decision-making and further losses.
When to Apply– When discussing project management and organizational behavior. – Particularly in understanding how biases influence decision inertia and organizational culture, and in exploring strategies to prevent escalation of commitment, such as setting clear exit criteria, fostering a culture of accountability, and promoting open discussion and feedback.
Gennaro Cuofano is a CRO and tech executive who has worked in AI since late 2015, starting with bringing NLP, natural-language generation, voice and chatbot products to the marketing industry. His background is in law and finance: he holds a Master's degree in Law and an International MBA with an emphasis on corporate finance (LUISS Business School and the University of San Diego, 2012), and worked as a financial analyst at a real-estate investment firm in San Diego and as an assistant controller. His work focuses on business model strategy, business engineering and, more broadly, structural analysis: how companies make money, read from their own filings. He created FourWeekMBA and leads research there and at The Business Engineer, his newsletter on AI and business strategy, with over 95,000 subscribers and more than 1,000 published analyses. His writing has also appeared on Entrepreneur (for example, How to Digitize a Family Business in Europe) and HackerNoon. Find him on LinkedIn and Substack. See how we source.
