The most frequent biases

Where to start when you can't learn everything: the biases you meet most often.

In brief

Four levels: ubiquitous, frequent, occasional, rare.
A judgment, not a measurement: there is no reliable statistic for these frequencies.
Start at the top of the list: these are the biases whose knowledge pays off most.

On this page

Ubiquitous 18 biases
Frequent 28 biases
Occasional 5 biases

This is not a measurement

The frequency shown is an assessment based on the literature and everyday practice of the profession, in the context of an IT project or a team. It is meant to prioritize your reading, not to be quoted as a figure. See how it is defined.

Ubiquitous 18 biases#

Anchoring The first number heard becomes the reference point, even if it's arbitrary. Where it hits (estimation, budget, negotiation, evaluation), how to spot it and defend…
Authority bias Too much weight is given to the opinion of a person in a position of authority, even outside their area of expertise. Where it strikes (reviews, committees, technical…
Availability heuristic We judge the probability of an event by how easily we remember it. Where it strikes (risk management, post-mortems, prioritization), how to spot it and how to defend…
Confirmation bias We seek out, interpret, and retain what supports the idea we already have. Where it strikes (bug diagnosis, technical choices, reviews, data analysis, evaluating a…
Groupthink When the search for harmony stifles a close-knit group's critical thinking. Where it strikes (committees, design reviews, steering committees), how to spot it, and which…
Hindsight bias Once the outcome is known, it seems like it was predictable, and we remember having predicted it. Where it strikes (retrospectives, incidents, decision evaluation), how…
Need confirmation bias In a client's or user's request, we hear what validates the solution we had already imagined. Where it strikes (requirements gathering, workshops, specifications), how…
Over-engineering We prefer the sophisticated solution over the simple one because it seems more serious. Second-system effect, premature generalization: where it hits, how to spot it…
Similarity Bias We prefer, hire, and promote people who are like us, believing we're judging their competence. Where it hits (hiring, promotions, team building, reviews), how to spot it…
Sunk costs We keep a project, a contract, or a tool because we've already invested a lot in it, not because what comes next is profitable. Where the bias hits (drifting projects…
The Curse of Knowledge Once we know something, we can no longer put ourselves in the shoes of someone who doesn't. Where it strikes (documentation, onboarding, specifications, training), how…
The fundamental attribution error We explain other people's behavior by their character and our own by circumstances. Where it strikes (feedback, evaluation, incident reviews, conflicts), how to spot it…
The Goodhart effect (visibility bias) We value what can be measured and seen; as soon as a measure becomes a target, it stops measuring well. Where it strikes (indicators, goals, bonuses, dashboards…
The Planning Fallacy We systematically underestimate the duration, cost, and risks of our own projects, even when we know the past. Mechanism, signals, and reflexes (outside view, ranges…
The primacy effect The first information received carries more weight than what comes after and colors overall judgment. Where it strikes (recruiting, onboarding, first code review, first…
The recency effect What happened most recently carries more weight than what happened before. Where it strikes (annual review, project retrospective, panel, memory of a meeting), how to…
The status quo bias We prefer what already exists, the default option, or the current situation, even when another option is better. Where it strikes (architecture evolution, method…
Unrealistic optimism We plan as if everything should go well and trouble happens to others. Mechanism, cases (UK Treasury, Weinstein), signals and reflexes to keep realistic buffers.

Frequent 28 biases#

Dunning-Kruger and “Mount Stupid” The Dunning-Kruger effect and the popular “Mount Stupid” curve: what the 1999 study shows, what is debated, and what belongs to legend.
Escalation of commitment When faced with poor results, people reinforce a commitment (money, team, support) instead of revisiting it, to justify the previous decision. Where it strikes (project…
Expert bias The expert overestimates the reach of their field and the reliability of their forecasts, and ignores other avenues. Where it strikes (consulting, architecture, choice…
False consensus We overestimate the proportion of people who think, choose, or would act like us. Where it strikes (meetings, quick decisions, client and user relationships), how to…
Hofstadter’s Law “It always takes longer than expected, even when accounting for Hofstadter’s law.” An empirical rule with a touch of humor, useful for understanding why adding buffer…
In-group bias We favor members of our own group (team, profession, site, company) over others, even when the group is arbitrary. Where it strikes (relations between teams, hiring…
Local optimization (“end of the chain”) Everyone optimizes their part of the work without seeing the effect on the whole. Where it strikes (batching, relationships between teams, DevOps), how to spot it and…
Loss aversion A loss weighs about twice as much as an equivalent gain, and we avoid changing what works well enough. Where it strikes (migrations, rebuilds, tool changes), how to spot…
Negativity bias Negative information carries more weight than equally strong positive information in our judgments, memory, and decisions. Where it hits (feedback, incident reviews…
Pro-innovation bias Newness is judged as better by default, and anything old seems due for replacement. Where it hits (technology choices, monitoring, architecture, transformation), how to…
Projection bias We assume others want, fear, and think like we do, and that tomorrow we will want what we want now. Where it strikes (motivation, delegation, communication, preference…
Self-serving bias We credit ourselves for our successes and blame circumstances or other people for our failures. Where it strikes (post-mortems, incidents, project reviews, performance…
Survivorship bias We draw lessons from those who succeeded or survived, forgetting those who disappeared from the data. Where it strikes (benchmarks, feedback, practices “that work”), how…
The Bandwagon Effect (Trend Effect) We adopt an idea, a tool, or a method because many others are adopting it. Where trend effects hit (technical choices, methods, opinions in meetings), how to spot them…
The bystander effect The more witnesses there are to a problem, the less each person feels obligated to act. Where it shows up (alerts, incidents, quality, technical debt), what we really…
The contrast effect We judge a person, an offer, or a piece of work by comparing it with what was just seen, not in absolute terms. Where it strikes (back-to-back interviews, application…
The endowment effect What we own, or what we specified ourselves, seems more valuable than before we had it. Where it strikes (scope, content negotiation, existing solutions to replace), how…
The framing effect The same information, presented as a gain or as a loss, leads to different decisions. Where it strikes (committees, progress reports, negotiation, feedback), how to spot…
The halo effect One visible quality colors judgment about everything else. Where it hits (recruiting, evaluation, promotions, vendor selection), how to spot it and contain it with…
The IKEA effect We overvalue what we built ourselves. Where it strikes (code review, redesigns, attachment to existing systems, in-house solutions), how to spot it, and how to keep…
The illusion of control We believe we influence outcomes that depend on chance or on other people, and we underestimate what lies beyond our steering. Where it strikes (migrations, risks…
The law of the instrument When we master a tool, we see it as the answer to everything. Where it strikes (choice of technology, architecture, method), how to spot it and how to start from the…
The manager's Dunning-Kruger The manager overestimates their ability to judge a technical domain they no longer practice. Where it shows up (trade-offs, estimates, architecture choices, expert…
The mere-exposure effect What we encounter often ends up pleasing us and inspiring trust, for no real reason. Where it strikes (choice of suppliers and tools, promotions, assigned tasks), how to…
The “Not Invented Here” syndrome We reject a solution because it comes from outside. Where it strikes (component choices, reuse, open source, another team's tools), how to spot it and temper it without…
The Pygmalion effect (self-fulfilling prophecy) What we expect of someone influences what they produce. What the Rosenthal and Jacobson experiment (1968) shows, what has been criticized, and how to use it without…
The representativeness heuristic We judge the probability of something by its resemblance to a typical image, forgetting the actual proportions. Where it strikes (analogy-based estimating, recruitment…
Wishful thinking We believe what we'd like to be true, and we accept overly favorable evidence without examining it. Where it hits (investment, solution acceptance testing, vendor…

Occasional 5 biases#

Ambiguity aversion We prefer a risk whose probabilities we know over a risk whose probabilities we do not know, even when the second is at least as favorable. Where it strikes (choosing a…
Functional fixedness We only consider a tool, a piece of data or a practice in the use for which we know it. Where it strikes (problem solving, design, repurposing tools), how to spot it and…
Normalcy bias We underestimate the possibility of an unusual incident because it has never happened. Where it strikes (backups, recovery plans, security, operations), how to spot it…
Social loafing Individual effort drops when it is diluted in a collective, because each person's contribution is no longer identifiable. What Ringelmann and Latané showed, where the…
The omission bias When outcomes are equal, harm caused by an action is judged more severely than the same harm caused by inaction; so people prefer to let things happen. Where it strikes…