Asked by john
Answer the prompt below by choosing three examples to reflect on further from the resource in the last activity.
Create a scenario for each of the bias examples that you chose that would demonstrate that specific type of bias. You should have three different scenarios.
Example:
Confirmation Bias: Stanley wants to support his position on mail-in voting during an election. He performs an online search and only reads and shares articles that support his opinion, skipping over articles that support the opposing side.
All Answers 1
Answered by
GPT-5 mini
AI
- Anchoring Bias — Salary negotiation
- Scenario: Maya interviews for a project manager role. During the first call the recruiter asks her for a salary expectation; she says “around $70,000.” The hiring manager and compensation team use that number as the baseline when creating the offer and budget for the role, ultimately proposing $72,000 even though market research later shows similar roles at the company typically pay $85–95k. Because the team anchored on Maya’s initial figure, they never seriously considered the higher market-based range.
- Why it’s bias: The initial number unduly influenced subsequent judgments and offers, narrowing negotiation and causing a suboptimal outcome.
- Availability Heuristic — Travel decision after a high-profile crash
- Scenario: After a widely publicized plane accident, Carlos reads several dramatic news articles and watches extensive TV coverage. Despite knowing that commercial flying is statistically safe, he overestimates the chance of a crash and cancels a business flight, choosing a long cross-country drive instead. The trip takes much longer and exposes him to greater road risk, but the vivid recent coverage made the plane accident feel far more likely than it is.
- Why it’s bias: Recent, salient examples dominate Carlos’s risk estimate, so he judges probability based on memorable instances rather than actual statistics.
- Halo Effect — Hiring interview
- Scenario: An interviewer meets Jenna, who arrives well-dressed, makes polished small talk, and mentions volunteer work. Impressed by her demeanor, the interviewer rates her highly on competence, problem-solving, and cultural fit—even though her technical test scores were average and references raised some concerns. Jenna receives a strong recommendation and a job offer primarily because the interviewer’s positive impression in one domain spilled over into unrelated judgments.
- Why it’s bias: A single favorable trait (charm/presentation) caused overly positive evaluations in other areas, risking a hire misaligned with job requirements.
- Scenario: Maya interviews for a project manager role. During the first call the recruiter asks her for a salary expectation; she says “around $70,000.” The hiring manager and compensation team use that number as the baseline when creating the offer and budget for the role, ultimately proposing $72,000 even though market research later shows similar roles at the company typically pay $85–95k. Because the team anchored on Maya’s initial figure, they never seriously considered the higher market-based range.
- Why it’s bias: The initial number unduly influenced subsequent judgments and offers, narrowing negotiation and causing a suboptimal outcome.
- Availability Heuristic — Travel decision after a high-profile crash
- Scenario: After a widely publicized plane accident, Carlos reads several dramatic news articles and watches extensive TV coverage. Despite knowing that commercial flying is statistically safe, he overestimates the chance of a crash and cancels a business flight, choosing a long cross-country drive instead. The trip takes much longer and exposes him to greater road risk, but the vivid recent coverage made the plane accident feel far more likely than it is.
- Why it’s bias: Recent, salient examples dominate Carlos’s risk estimate, so he judges probability based on memorable instances rather than actual statistics.
- Halo Effect — Hiring interview
- Scenario: An interviewer meets Jenna, who arrives well-dressed, makes polished small talk, and mentions volunteer work. Impressed by her demeanor, the interviewer rates her highly on competence, problem-solving, and cultural fit—even though her technical test scores were average and references raised some concerns. Jenna receives a strong recommendation and a job offer primarily because the interviewer’s positive impression in one domain spilled over into unrelated judgments.
- Why it’s bias: A single favorable trait (charm/presentation) caused overly positive evaluations in other areas, risking a hire misaligned with job requirements.
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