Decision context
Checking claims & misinformation
Use these evidence-reviewed lenses when deciding whether a repeated claim is trustworthy, checking a correction, or testing an explanation against plausible alternatives.
Decision skill
Evidence evaluation
Judge whether a claim deserves confidence before repetition, fluency or preference turns into evidence.
Learning outcome: You can separate a persuasive claim from the evidence that should change your mind.
When it matters
Notice the situation before naming the bias.
- A team already prefers one explanation and is gathering support for it.
- A claim appears in many places but may trace back to one source.
- A vivid example is dominating broader evidence.
- An AI answer sounds convincing but important claims are still unverified.
Practice the skill
Use a better reasoning move.
- Write what evidence would weaken the preferred conclusion before searching for more support.
- Trace repeated claims back to genuinely independent sources.
- Compare vivid examples with a base rate, reference class or broader sample.
- Keep verified facts, plausible inferences and open questions separate.
Decision contexts
Apply the skill to real kinds of decisions.
Decision context
AI-assisted decisions
Use these evidence-reviewed lenses when a chatbot, model, recommendation system, or automated decision aid is influencing what you believe or do.
Decision context
Work & project decisions
Use these evidence-reviewed lenses when a team is choosing a direction, interpreting results, deciding whether to continue, or turning metrics into action.
Evidence-reviewed lenses
Patterns worth checking, not labels for people.
Several patterns can fit the same situation. Use these lenses to ask better questions, then inspect the evidence behind each concept.
Confirmation Bias
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.
well established, broad construct Read evidence review
Illusory Truth Effect
Repeated information is, on average, judged as more truthful than comparable new information. The size of the effect varies with the material and procedure, and repetition does not make every claim believable or erase all prior knowledge.
robust Read evidence review
Availability Heuristic
Availability is a judgment heuristic: people can use how easily examples or scenarios come to mind when estimating frequency or probability. That shortcut is not automatically an error because memorable or accessible examples can correlate with real frequency. Bias appears when accessibility is driven by factors that are not diagnostic of the quantity being judged.
established heuristic; bias is context-dependent Read evidence review
Continued Influence Effect
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.
well-established persistence after correction; corrections usually still help Read evidence review
Practice this skill
Apply the capability across real decisions.
A candidate gives a confident opening answer. The panel spends the rest of the interview interpreting mixed evidence as proof that the candidate is strong.
Skill: Evidence evaluationOpen scenario →The team already prefers one candidate. Interviewers ask that candidate to explain strengths, while competing candidates are pressed mainly on weaknesses.
Skill: Evidence evaluationOpen scenario →A previous hire performed very well. The team now treats every feature of that hiring decision as a proven success formula, although several factors were uncertain at the time.
Skill: Evidence evaluationOpen scenario →A vendor presents a fluent AI assistant in a polished demo. The team begins describing the product as reliable and intelligent, although the critical workflow was not tested with company data.
Skill: Evidence evaluationOpen scenario →A vendor opens with a three-year price of €900,000. Internal discussions now focus on whether a 15% discount is enough, although the team never estimated value or alternatives independently.
Skill: Evidence evaluationOpen scenario →A team prefers a new platform and highlights its strong benchmark score. It dismisses workload, data quality and operating constraints that are not measured by the benchmark.
Skill: Evidence evaluationOpen scenario →A support team meets its response-time KPI by closing tickets early and reopening them later. Leaders call the process successful because the dashboard is green.
Skill: Evidence evaluationOpen scenario →