The decision question
How can I check a claim without confusing familiarity, repetition, agreement, or a weak hypothesis test with evidence?
Reviewed lenses
- Continued Influence Effect – when corrected information still shapes reasoning — The continued influence effect describes a specific pattern: false or outdated information can keep affecting later reasoning even after a clear correction. It does not mean the correction had no effect, that the person rejected the correction, or that belief in the false claim became stronger. Corrections often reduce misinformation reliance without removing it completely. That distinction matters because continued influence is much broader than the rarer backfire outcome.
- Backfire Effect – when a correction can strengthen a false belief — Corrections usually improve factual accuracy. A true backfire effect, where a correction makes the targeted false belief stronger, appears to be uncommon and sensitive to context and measurement. It should not be treated as the default response to being corrected.
- Confirmation Bias – when a belief shapes how evidence is searched and judged — Confirmation bias is an umbrella label for several ways existing beliefs or hypotheses can influence information search and interpretation. It should not be reduced to one behaviour such as reading only agreeable news, and a preference for confirming tests is not irrational in every task or environment.
- Congruence Bias – when the test fits one hypothesis but does not separate alternatives — Congruence Bias is a narrower hypothesis-testing pattern than the broad umbrella of confirmation bias. In classic work, people overvalued tests that were likely to return a positive result if their leading hypothesis were true, even when other tests were more diagnostic among competing hypotheses. The useful claim is not that every positive test is irrational: a positive test strategy can be efficient in some environments, and the problem depends on whether the chosen test can actually distinguish the focal hypothesis from alternatives.
- Hungry Judge Effect – a famous parole-board finding with an uncertain cause — The famous 2011 parole-board study found favorable decisions were more common early in a decision session and immediately after food breaks. It did not directly measure hunger, glucose, fatigue, or mood, and the authors explicitly could not separate eating from rest. A contemporaneous critique argued that non-random case ordering could explain the pattern; the original authors disputed that critique with additional analyses. Treating this as a general law that hungry judges become harsher goes beyond the evidence.
- Illusory Truth Effect – when repetition makes a claim feel more true — Repeated claims receive higher truth ratings than comparable new claims on average, but the effect is heterogeneous and does not generalize equally to every kind of statement. A 2026 systematic review and meta-analysis covering 182 studies, 366 effect sizes and more than 31,000 participants found a reliable corrected average effect. A later 2026 paper with two preregistered experiments found little or no meaningful repetition effect for social-political opinion statements. The useful claim is therefore narrower than 'repeat anything and people will believe it.'
- Conservatism Bias – when a probability estimate changes too little — Here, conservatism means under-updating: new evidence changes a probability judgment less than a Bayesian benchmark would predict. It should not be confused with the separate memory phenomenon in this corpus that pulls remembered extreme values toward the middle.
- Curse of Knowledge – when what you know distorts what you expect others to know — Knowing more can make it harder to estimate what a less-informed person knows or understands, but the size and mechanism of the effect depend on the task. The useful claim is not that experts are unable to teach beginners; it is that one's own knowledge can contaminate judgments about another person's knowledge unless the perspective gap is made explicit.
- Planning Fallacy – when plans make completion times look too optimistic — The planning fallacy is a well-documented tendency for people to predict their own task completion times too optimistically. The effect has been observed across different kinds of tasks, but it is not a rule that every plan will run late. Project overruns can also come from changing scope, dependencies, incentives, poor data, deliberate underestimation, or genuinely unusual events, so a late project should not automatically be diagnosed as a planning fallacy.
- Automation Bias – when automated advice replaces independent checking — Automation bias is a documented pattern of inappropriate reliance on automated cues or recommendations. It can produce commission errors when a user follows incorrect advice and omission errors when a user fails to act because automation did not signal a problem. This does not mean automation is generally harmful: decision support can improve overall performance, and the relevant question is whether reliance remains calibrated when the system is wrong, incomplete, or difficult to verify.
Decision guides
- Work & project decisions
- AI-assisted decisions
- Project estimation & delivery
- Checking claims & misinformation
- Reviewing KPIs & proxy metrics
- Continue, change, or stop a project
Decision situations
Use the evidence in a real decision instead of stopping at a bias label.
Test whether repetition represents independent evidence or only makes a claim feel familiar.
Review this decision →Use AI to expand research without treating fluent text, repeated output or model confidence as verification.
Review this decision →How to use this page
Start with the decision, not with a label for a person. Open the evidence behind the lenses that fit, compare nearby concepts when needed, and keep uncertainty visible.