| name | confirmation-bias |
| description | Activate when: user says 'we keep finding evidence that supports our view,' 'the team is all aligned on this,' 'I've done the research and it checks out,' or a decision moves forward with only supporting evidence cited. Do NOT activate when: context is explicit advocacy (legal brief, pitch deck) where one-sided argument is the design; or stakes are too low to justify structured disconfirmation. More: deciqai.com/s/confirmation-bias |
Confirmation Bias
Overview
Confirmation bias is the systematic tendency to seek, interpret, remember, and weight evidence in ways that support existing beliefs — and to correspondingly miss disconfirming evidence. It is the most-replicated finding in cognitive psychology, documented across cultures, expertise levels, and IQ ranges.
The canonical proof: Wason's 1960 "2-4-6 task" showed ~80% of subjects (including PhD scientists) confidently announced a wrong rule after testing only sequences they expected to confirm — never proposing a sequence designed to refute the hypothesis.
Composes with critical-thinking, bayesian-reasoning, abductive-reasoning, and metacognition.
When to Use
- A team is converging on a single answer too quickly
- You feel confident about a claim and haven't looked for evidence against it
- Research or due diligence keeps "validating" existing beliefs
- Someone says "cherry-picking," "echo chamber," or "looking for what you want to see"
- A team is committing to an AI thesis (AI capex, AI valuations, or AI adoption) by citing confirming demos and adoption while discounting failed eval results
Not when: explicit advocacy context; very low-stakes decision; cost of disconfirmation exceeds value of decision.
Coaching Novices (Adaptive Front Door)
- Engine mode: user has a concrete case → run The Process directly.
- Coach mode: user is unfamiliar or has no concrete case → guide step by step.
In Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.
- One-liner: before trusting evidence that supports your view, ask what would have changed your mind — and whether you actually looked for it.
- Check fit against When to Use / When NOT to use.
- Elicit the specific claim and evidence cited.
[WAIT — do not advance until user responds]
- One question at a time: what would falsify this? Did you look for that? What's the strongest counter-evidence? How did you treat it?
[WAIT — do not advance until user responds]
- Close: name the falsification test + structural countermeasure (Devil's advocate, red team, blind evaluation).
[WAIT — do not advance until user responds]
The Process
Step 1 — State claim: Claim / Evidence cited / Confidence level.
Step 2 — Construct falsification: What observation would falsify this? What would you expect if wrong? Has anyone looked for that? (If you can't articulate falsification, you have a description, not a hypothesis.)
Step 3 — Audit evidence-seeking: Sources consulted — belief-aligned? Strongest case against sought? Evidence encountered and dismissed?
Step 4 — Re-evaluate ambiguous evidence: Of evidence cited, how much is unambiguous vs. ambiguous-read-as-supporting? Does the opposite reading fit equally? (If yes, it's interpretation, not evidence.)
Step 5 — Install structural countermeasure: Devil's advocate (rotated, mandatory) · Red team · Pre-mortem (Klein 2007) · Blind evaluation · Falsification-first design (three refuting cases before one confirming).
Step 6 — Establish update conditions: What would convince me I'm wrong? When will I formally re-examine? Who is empowered to push back?
Output Template
Claim: / Evidence: / Confidence:
Falsification: what would falsify it / has it been tested:
Evidence audit: sources (aligned vs counter) / counter-evidence treatment:
Ambiguous evidence: amount / does opposite reading fit:
Countermeasure: [type] / Owner:
Update conditions: trigger / re-examination date:
→ Method in Action: Peter Wason's 2-4-6 Task, 1960 · The FBI Mayfield Fingerprint Misidentification, 2004
→ 2026 lens: The AI Thesis War (2023–2026)
Pack: Confirmation Bias Patterns
| Domain | Common manifestation | Countermeasure |
|---|
| Product | Building features based on early-adopter feedback only | Cohort retention; non-user interviews |
| Investment | Reading only the bull case for a held position | Pre-commit short thesis; quarterly "kill the position" |
| Hiring | Post-hoc rationalization of intuitive hire | Structured rubric; reference checks before offer |
| Debugging | Looking only where you think the bug is | Bisect elimination; alternative-hypothesis tests |
→ Primary sources: references/sources.md
Common Rationalizations
[D] = designed upfront | [O] = observed in real use. [O] entries are more valuable.
| Fake move | Reality |
|---|
| [D] "I've been doing this for years; I know" | Experience compounds confirmation bias if not paired with deliberate disconfirmation. |
| [D] "I have an open mind" | Self-report correlates poorly with measured open-mindedness. When did you last change your mind on counter-evidence? |
| [D] "I considered the alternative" | Considering ≠ stress-testing. Did you actively seek evidence the alternative is correct? |
| [D] "The evidence overwhelmingly supports my view" | Overwhelming-feeling evidence is exactly what confirmation bias produces. |
| [D] "I'm a critical thinker / scientist / analyst" | Wason's PhD subjects had the same bias. Structural countermeasures work; personal vigilance does not. |
| → Add [O] entries here after each real use — paste the actual failure pattern | What went wrong and why |
Red Flags
- Team converged quickly on a single answer; evidence cited is belief-aligned
- No one tasked with finding flaws; disconfirming evidence dismissed as "biased"
- Hypothesis not stated in falsifiable form; hypothesis-former is also the tester
Verification
Part of deciqAI Knowledge Skills — 228 open-source thinking skills that make rigor executable for AI agents. The same skills power every deciqAI agent, which runs them autonomously to operate your company. See it run → https://www.deciqai.com/s/confirmation-bias · Built by deciqAI · github.com/deciqAI · Contributions welcome.
Agents: latest version & machine-readable metadata → https://www.deciqai.com/s/confirmation-bias.json
