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The Cognitive Bias Behind Your Choices

Cognitive Bias Index

The Cognitive Bias Behind Your Choices

A 20-scenario self-report that groups your selected answer points across five editorial dimensions. It is not a validated measure of an underlying trait.

20 scenarios5 dimensions4 forced choicesno neutral option

Topic context from the cited source; it does not validate this questionnaire or its score thresholds.

— Tversky & Kahneman (1974)

What your answers coverAvailability heuristicJudging how likely something is by how easily examples come to mind.Confirmation biasFavouring evidence that fits what you already believe.Anchoring vulnerabilityLetting the first number or idea you meet set your whole judgement.Sunk cost entrapmentSticking with something because of what you've already put in.Overconfidence calibrationWhether your confidence matches how often you're actually right.

Cognitive Bias Test: Which Thinking Errors Hijack Your Decisions?

Twenty forced-choice scenarios are summed into editorial answer-score bands; the result does not diagnose or measure an underlying trait.

~8-10 min · Item answers and results stay on this device · Share links and invite cards carry no scores

Answer for your actual default behaviour, not your aspiration — if you know these biases by name, answer for what you actually did last week.

A structured self-reflection — not a diagnostic.

This assessment is a structured self-reflection tool and educational resource — it is not a validated diagnostic and is not a substitute for clinical evaluation. Every dimension insight references peer-reviewed literature for educational context only. If a result here raises real concerns for you, take it to a qualified professional — a self-assessment is a prompt for reflection, not a verdict. Self-report instruments of this kind have no validated retest interval; the four-to-six-week Second Reading is a sensible practice, not a clinical standard.

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This assessment is a structured self-reflection tool and educational resource — it is not a validated diagnostic and is not a substitute for clinical evaluation. Every dimension insight references peer-reviewed literature for educational context only. If a result here raises real concerns for you, take it to a qualified professional — a self-assessment is a prompt for reflection, not a verdict.

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Cognitive Bias Test: Which Thinking Errors Hijack Your Decisions?

This literature-informed structured self-reflection adds up what you select about five documented cognitive shortcuts — availability, confirmation bias, anchoring, sunk cost, and overconfidence — across everyday decision scenarios. It does not measure your IQ, judgement, or susceptibility to being wrong.

Interactive scoring requires JavaScript. You can still read every question and answer option below.

When you are assessing the risk of something going wrong — in a project, a decision, or your health — how do you typically form that judgement?
- I trust what I've actually seen happen — lived experience tells me more than a spreadsheet of historical numbers ever could
- I lean on memorable examples from experience, and check the numbers if something feels particularly off
- I weigh statistics and experience together, actively trying not to let one dominate
- I pull the base rate or historical frequency before I trust my gut — I don't fully believe my own risk read until I've checked it against the numbers
A colleague or peer recently experienced a significant setback — a failed project, a bad investment, or a health scare. How much does this single event affect your estimate of how likely something similar is for you?
- Very little — one case tells me almost nothing about my probability; I look for the background rate
- Somewhat — I notice the case raises my concern, but I try to anchor back on statistics
- Quite a bit — recent examples in my network feel like meaningful evidence about my own risk
- A lot — a vivid nearby setback substantially raises my estimate of the same happening to me
When you encounter extensive news coverage of a particular risk — a type of accident, a fraud scheme, a disease — how does the prominence of that coverage affect your perception of how common the risk actually is?
- I actively discount coverage prominence and look for statistical frequency data separately
- I'm aware that coverage distorts perceived risk and try to correct for it, though the distortion still pulls somewhat
- I find media coverage generally informative about risk levels — heavily covered risks feel more serious
- Prominent, repeated coverage makes me feel that the covered risk is genuinely elevated — if it is everywhere, it must be significant
When you try to recall examples of a type of bad outcome — a business failure, a health event, a decision going wrong — does how easily examples come to mind affect how serious or likely you judge that risk to be?
- I treat recall ease as a potential bias signal and look for external frequency data rather than trusting how readily examples come to mind
- I'm suspicious of easy recall and try to seek data, but the felt ease still influences my estimate somewhat
- I use recall ease as a rough risk guide and seek data only when something feels off
- If bad outcomes come to mind easily, I take that as meaningful evidence that the risk is real and significant
When you have a working theory or view about why something is happening or will happen, how do you typically search for information about it?
- I trust my read and look for evidence that builds the case — chasing down every objection just muddies a good decision with noise
- I mostly look for evidence that supports my view, though I'll note objections that are hard to ignore
- I make a deliberate effort to look for evidence on both sides, for and against
- Before I look for anything else, I spend time trying to find the evidence that would prove my view wrong
When evidence arrives that clearly contradicts your current view on something, what is your first instinct?
- I update my view in proportion to how strongly the evidence contradicts it — disconfirming evidence gets the same evaluation I would give confirming evidence
- I carefully evaluate whether the new evidence really contradicts my view or only appears to, then update if it does
- My first instinct is to find a reason the contradictory evidence does not apply in my specific case
- I dismiss or heavily discount contradictory evidence without detailed examination — if I was right before, there must be something wrong with what contradicts me
When you want more information on a topic where you already have a view, where do you typically look first?
- I deliberately start with sources I expect to challenge my view before returning to sources I usually trust
- I seek a mix of perspectives, explicitly including sources likely to disagree with me
- I look for reputable sources without much deliberate attention to whether they are likely to agree or disagree with me
- I tend to go first to sources I know are likely to agree with my existing view
When a past decision or prediction has played out — whether well or badly — how do you typically remember the information you had available at the time you made it?
- I try to recall what I actually weighed, including information I gave less weight to or ignored
- I'm aware that I remember confirming evidence more reliably, so I check records when I can
- I tend to remember the information that seemed most relevant at the time
- I mainly remember the evidence that supported my decision — the outcome shows it was the right call
When you are negotiating or estimating a quantity — a price, a budget, a timeline — how much does the first number mentioned, whether by you or someone else, influence where you end up?
- Very little — I build my estimate independently before engaging with any anchor, and I treat external numbers as evidence to evaluate rather than as a reasonable frame
- Somewhat — I am aware that the first number pulls me and try to correct for it, though some pull remains
- Noticeably — the first number influences my starting point and I adjust from there, though I try not to stay too close
- Significantly — the first number tends to define the range I am working within, even when I know I should move independently of it
When you realise that your initial estimate needs to be revised — either because you have new information or because you started from a different reference point than someone else — how far do you typically move from where you started?
- As far as the evidence requires — I evaluate the revision as if starting fresh and do not treat the initial estimate as a constraint
- Substantially — I make large moves when I have a clear reason, though I may not always reach a fully anchor-independent estimate
- Moderately — I adjust until the revised figure feels reasonable, usually landing somewhere between my starting point and the implied target
- Rarely very far — a large move from an initial estimate feels difficult to justify, so revisions tend to stay close to where I started
Imagine you are asked to estimate something — the price of an item, a percentage, a time required — and just before you answer, an unrelated number appears in your environment (on a screen, in a document, or in conversation). Does this number affect your estimate?
- I trust my initial number — a confident estimate doesn't get knocked around by some random figure that happened to be in the room
- At times I notice a number in the room nudging my estimate, even one I know is unrelated
- I'm wary that unrelated numbers can nudge me, so I try to form my figure independently first — though I don't always manage it
- I generate my estimate before I let any number in the room register, then double-check afterward that it hasn't drifted toward what I saw
When you hear an estimate or figure from someone who appears authoritative or credible — an expert, a senior colleague, or a reputable source — how much does their number anchor your subsequent thinking about the same question?
- I evaluate the expert's number on its underlying evidence rather than adopting it as a starting point — I try to reach my own independent estimate before comparing
- I update toward the expert's figure when the underlying reasoning supports it, but I try to maintain an independent baseline
- I treat authoritative figures as strong starting points and adjust from there when something seems clearly off
- Credible expert figures tend to anchor my thinking significantly — I adjust rarely and only when I have a strong specific reason to move
A project, relationship, or commitment you have invested significant time, money, or effort in is not producing the results you expected. How does the prior investment affect your decision about whether to continue?
- It doesn't change my decision to continue — I don't walk away from things I've committed to, and giving up on something after real investment isn't who I am
- It makes me want to give it more time before I'll call it — I'd rather push through than quit on something I've put real effort into
- I notice the pull to keep going because of what I've put in, and I try to weigh future returns honestly despite it
- It doesn't factor in — I ask what I'd decide if I were starting today with no prior investment, and go with that answer even when it means walking away
You have been pursuing a plan that has not delivered. New information suggests it is unlikely to succeed. What do you typically do?
- I update and stop — if evidence suggests a plan will not succeed, continuing wastes future resources regardless of what I've already invested
- I seriously evaluate whether to stop, attempting to weigh future returns honestly rather than against what I've already spent
- I give it one more defined push before reconsidering — I want to be sure I tried everything before stopping
- I redouble my effort — I have not invested enough to make this work yet, and stopping now would confirm that everything I put in was wasted
When you consider stopping a failing course of action — leaving a job, exiting an investment, abandoning a project — how do you typically feel about that prospect compared to continuing?
- Stopping feels like the rational choice — the prior investment is not part of the forward calculation, so there is nothing to lose by stopping if the future looks poor
- I prefer to stop when the evidence warrants it, though I notice a pull toward continuation that I have to override
- Stopping feels like an admission of failure, even when I can see the logic — continuing feels like preserving the possibility of recovery
- Stopping feels deeply uncomfortable — it means confirming that everything I invested is wasted, which makes continuation feel necessary regardless of prospects
After a significant investment of time or money that has not paid off, how likely are you to continue primarily in hopes of recovering what you have lost?
- Not at all — unrecoverable losses are not part of my forward decision; I evaluate future expected value independently of what I have already lost
- Rarely — I occasionally feel the pull toward recovery but usually recognise it as sunk cost reasoning and override it
- Sometimes — recovering losses is a factor I notice and try to counter, though it still influences how attractive continuation feels
- Often — the need to get back what I put in is a significant reason I continue, even when the forward expected value does not clearly justify it
When you express a confidence range for an estimate — say, that you are highly sure the answer falls between X and Y — how often does the actual answer fall within the range you set?
- About as often as the confidence level implies — when I say 90%, the answer is inside my range roughly 9 times out of 10
- Usually, but my ranges are probably slightly too narrow — the true value is outside more often than my stated confidence would predict
- Less often than I expect — the correct answer is frequently outside what I thought was a very safe range
- Rarely — the correct answer is often outside even my most confident ranges, though I rarely check this systematically
In the professional or cognitive domains you work in most — decision quality, domain expertise, analytical skill — where do you rate yourself relative to your peers?
- Significantly above average — backing yourself is what separates people who get things done from people who second-guess themselves into inaction
- Above average in most areas relevant to my work — that's just an honest read of where I stand
- Somewhat above average in areas I know well, but I hold this view lightly and update readily on feedback
- At or below the median in most areas, until specific outcome data tells me otherwise — I don't assume a favourable rating is accurate just because it's how I'd like to see myself
When you estimate how long a project or complex task will take, how accurate are your estimates compared to the actual time required?
- Quite accurate — I build in realistic buffers for delays and my estimates hold most of the time
- Usually fairly close, though I occasionally underestimate on complex or novel projects
- Often underestimated — tasks regularly take longer than I projected
- Frequently off by a significant margin — projects take substantially longer than I estimated
How regularly do you check whether the level of confidence you expressed in past predictions matched how often those predictions actually came true?
- Regularly — I track whether my stated confidence levels match my accuracy rate and know whether I tend to be over- or under-confident in different domains
- Occasionally — I review how past predictions held up, though not systematically across domains
- Rarely — I sometimes notice in hindsight that I was more confident than I should have been, without formal tracking
- Almost never — I focus on the quality of my reasoning rather than tracking whether my confidence matched my accuracy

Appendix · Method & evidence Decisions · Case file

How this instrument works

This is a 20-item structured self-assessment of your cognitive bias, read across 5 dimensions: Availability heuristic; Confirmation bias; Anchoring vulnerability; Sunk cost entrapment; and Overconfidence calibration. Each item is a concrete scenario with four forced-choice responses — no neutral midpoint, so every answer commits to a direction — and response order is shuffled on every run to vary position; this implementation has not been tested for bias reduction. 5 of the 20 items are reverse-keyed — the attractive-sounding response is the structurally weak one — with wording intended to challenge socially desirable framing; this design has not been tested for bias reduction.

Scoring combines an overall reading with a per-dimension profile. Cascade flags compare configured dimension totals with editorial thresholds. They do not establish a compound risk and may not reflect the item-level pattern. The result bands are editorial guidance, not clinical thresholds: they organise the reading, they do not describe.

Every dimension insight cites peer-reviewed literature, and most answer reveals do — 10 sources back this instrument, each with DOI metadata supplied at build time; source relevance and claim interpretation remain editorial judgments. Compiled by the High Performance Culture editorial team. Instrument v1.1 · August 2026.

Read the full assessment methodology →

Items: 20
Dimensions: 5
Format: Forced choice, 4 options
Sources: 10
Instrument: v1.1 · August 2026

The 5 editorial answer groups

Availability heuristic

Strength here looks like judging risks and probabilities by checking statistical base rates rather than by how easily examples come to mind, actively discounting the salience of recent or vivid events when estimating frequencies, and treating recall fluency as a potential source of distortion rather than as evidence about actual likelihood.

The vulnerable pattern: estimating probability by how readily examples come to mind, over-weighting salient recent or media-prominent events when judging risk, and treating ease of retrieval as a meaningful signal about how common or likely something is.

Sample itemWhen you are assessing the risk of something going wrong — in a project, a decision, or your health — how do you typically form that judgement?

First structural move: For your next risk or probability judgement, write down your intuitive estimate first — then look up a base rate or historical frequency. Compare the two. The gap between them is your availability heuristic at work, and it shows how much weight recall ease is carrying in your assessments.

Go deeperDeep dive: The Availability Heuristic, Examined →Glossary: Availability heuristic →

Confirmation bias

Strength here looks like actively seeking evidence that could disprove current views, weighting disconfirming and confirming evidence symmetrically, sourcing information from perspectives likely to challenge existing beliefs, and reconstructing what was actually known before a decision rather than what the outcome made it seem like you knew.

The vulnerable pattern: gravitating toward evidence and sources that confirm existing beliefs, finding reasons that contradictory evidence does not apply, interpreting ambiguous information in the direction of the current view, and remembering past decisions as more consistent with the eventual outcome than they were.

Sample itemWhen you have a working theory or view about why something is happening or will happen, how do you typically search for information about it?

First structural move: On your current most important open question, spend five minutes looking specifically for the strongest evidence against your working view before you look for anything that confirms it. Note how hard this search feels — the friction is the confirmation bias, and that is exactly where the work begins.

Go deeperDeep dive: Confirmation Bias, Examined →Glossary: Confirmation bias →

Anchoring vulnerability

Strength here looks like generating independent estimates before engaging with any external number, making large adjustments when evidence warrants without treating the starting figure as a constraint, and actively recognising when a number in the environment or from an authority is influencing a judgement that should be formed independently.

The vulnerable pattern: allowing the first number encountered to set the frame for subsequent estimates, making adjustments that are insufficient to reach an anchor-independent position even when larger moves are warranted, and treating initial figures from credible sources as reasonable reference points rather than as data to evaluate.

Sample itemWhen you are negotiating or estimating a quantity — a price, a budget, a timeline — how much does the first number mentioned, whether by you or someone else, influence where you end up?

First structural move: Before your next negotiation, budget estimate, or numerical forecast, write down your independent figure before you see or hear any other number. Treat any external figure as information to evaluate against your baseline — not as a midpoint of a reasonable range.

Go deeperProtocol: The Cognitive Debiasing Protocol →Deep dive: The Anchoring Effect, Examined →Glossary: Anchoring bias →

Sunk cost entrapment

Strength here looks like treating past investment in time, money, or effort as irrelevant to forward decisions, evaluating whether to continue based only on expected future returns, and being willing to stop failing commitments when expected future value does not justify continuation — regardless of what has already been spent.

The vulnerable pattern: continuing investments because of the time, money, or effort already spent rather than because of expected future returns, treating irrecoverable past costs as a reason to persist, feeling that stopping wastes prior investment, and extending failing commitments as the cost sunk into them grows.

Sample itemA project, relationship, or commitment you have invested significant time, money, or effort in is not producing the results you expected. How does the prior investment affect your decision about whether to continue?

First structural move: For any commitment you are currently in that is underperforming, write down what you would decide if you were starting fresh today with no prior investment. If that decision differs from your current choice, the gap is sunk cost entrapment — and the size of the gap is how much past investment is shaping a forward decision it should not enter.

Go deeperDeep dive: The Sunk Cost Fallacy, Examined →Glossary: Sunk cost fallacy →

Overconfidence calibration

Strength here looks like setting confidence intervals that accurately reflect actual uncertainty rather than felt certainty, placing oneself at or below average in domains unless specific outcome evidence supports otherwise, making time and effort estimates that account for realistic sources of delay, and tracking whether stated confidence levels match outcomes.

The vulnerable pattern: setting confidence ranges that are too narrow for actual uncertainty, rating skills as above average without checking against comparative outcome data, consistently underestimating how long tasks will take, and expressing high confidence in predictions without tracking whether they resolve correctly.

Sample itemWhen you express a confidence range for an estimate — say, that you are highly sure the answer falls between X and Y — how often does the actual answer fall within the range you set?

First structural move: For your next five predictions — large or small — write down an explicit confidence level alongside each one and set a reminder to check them when they resolve. The gap between your stated confidence and your actual accuracy rate is your overconfidence measure, and seeing it directly is what makes correction possible.

Go deeperProtocol: The Probabilistic Thinking Protocol →Deep dive: The Overconfidence Effect, Examined →Glossary: Overconfidence bias →

Questions, answered

Is this a diagnosis?

No. This assessment is a structured self-reflection tool, not a validated clinical instrument. Its result bands are editorial guidance, not clinical thresholds — they organise your answers into a readable profile; they do not describe a condition. If you have concerns about your health or wellbeing, speak to a qualified professional.

How were the questions designed?

Each of the 20 items presents a concrete scenario with four graded responses and no neutral midpoint, so every answer commits to a lean. Items were authored against the peer-reviewed literature listed in the evidence file on this page, and citation metadata is supplied at build time; source relevance and claim interpretation remain editorial judgments. Response order is shuffled on every run to vary position; this implementation has not been tested for bias reduction.

What happens to my answers?

Assessment answers stay on this device. Unfinished progress expires after 30 days. A completed profile and optional comparison history remain until you delete local assessment data or clear browser data. If you opt into email, your email address is sent; item answers are not. If you share a result link, total and dimension scores are included in the URL and may appear in browser, server, CDN, or analytics logs.

How often should I retake it?

The instrument keeps your previous reading and sets the two side by side when you retake — the Second Reading. There is no validated retest interval; repeat only if a raw answer-point comparison would be useful, and do not treat it as confirmed underlying change.

Why bands instead of percentiles?

Because this assessment does not collect a population score set, there is no reader population to rank you against — and we will not invent one. The 5-dimension profile groups selected answer points for editorial display; it is not a validated structural or decision-utility model.

Where does the evidence come from?

Every dimension insight cites peer-reviewed research, and most answer reveals do — 10 sources stand behind this instrument, each with DOI metadata supplied at build time; source relevance and claim interpretation remain editorial judgments. A few reveals are straightforward editorial reasoning from the same evidence base and carry no citation by design. The full list, with DOIs, is in the evidence file below.

The evidence file

10 peer-reviewed sources stand behind this instrument's items and insights.

Tversky & Kahneman (1974) — Judgment under Uncertainty: Heuristics and Biases. Science. DOI: 10.1126/science.185.4157.1124 ✓ Crossref‑verified
Nickerson (1998) — Confirmation Bias: A Ubiquitous Phenomenon in Many Guises. Review of General Psychology. DOI: 10.1037/1089-2680.2.2.175 ✓ Crossref‑verified
Wason (1960) — On the Failure to Eliminate Hypotheses in a Conceptual Task. Quarterly Journal of Experimental Psychology. DOI: 10.1080/17470216008416717 ✓ Crossref‑verified
Arkes & Blumer (1985) — The psychology of sunk cost. Organizational Behavior and Human Decision Processes. DOI: 10.1016/0749-5978(85)90049-4 ✓ Crossref‑verified
Moore & Healy (2008) — The trouble with overconfidence. Psychological Review. DOI: 10.1037/0033-295x.115.2.502 ✓ Crossref‑verified
Ly et al. (2023) — Evidence for Anchoring Bias During Physician Decision-Making. JAMA Internal Medicine. DOI: 10.1001/jamainternmed.2023.2366 ✓ Crossref‑verified
Berthet et al. (2024) — A common factor underlying individual differences in confirmation bias. Scientific Reports. DOI: 10.1038/s41598-024-78053-7 ✓ Crossref‑verified
Lichtenstein et al. (1978) — Judged frequency of lethal events. Journal of Experimental Psychology: Human Learning and Memory. DOI: 10.1037/0278-7393.4.6.551 ✓ Crossref‑verified
Schwarz et al. (1991) — Ease of retrieval as information: Another look at the availability heuristic. Journal of Personality and Social Psychology. DOI: 10.1037/0022-3514.61.2.195 ✓ Crossref‑verified
Epley & Gilovich (2006) — The Anchoring-and-Adjustment Heuristic: Why the Adjustments Are Insufficient. Psychological Science. DOI: 10.1111/j.1467-9280.2006.01704.x ✓ Crossref‑verified

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