Practice UX research bias vocabulary: social desirability bias, confirmation bias, facilitator bias, survivorship bias, and recency bias mitigation strategies.
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What is 'social desirability bias' in user research?
Social desirability bias occurs when participants give answers they believe are socially acceptable or expected rather than their true behaviour. For example, participants may say they always read terms and conditions, when in reality they never do. Mitigation techniques include: asking about past behaviour rather than intentions, framing questions as 'what do most people do?', and prioritising behavioural observation over self-report.
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What is 'confirmation bias' in UX research analysis?
Confirmation bias in analysis means the researcher (consciously or unconsciously) seeks out, emphasises, and remembers evidence that supports what they already believed, while minimising evidence that contradicts it. Mitigations: involve multiple researchers in analysis, use structured note-taking, conduct affinity mapping with the whole team, and actively seek disconfirming evidence.
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What is 'facilitator bias' in qualitative research?
Facilitator bias occurs when the researcher conducting the session inadvertently influences participant responses — through leading questions ('did you find the checkout confusing?'), nodding or reacting to certain answers, or their tone of voice. Neutral facilitation requires open-ended questions, neutral probes ('can you tell me more?'), and trained awareness of non-verbal reactions.
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What is 'survivorship bias' in user research?
Survivorship bias in user research means you only hear from users who are still using the product — missing the perspective of people who tried and abandoned it. This skews findings towards a more positive view of the product. To counter it, recruit churned users or look at data from users who dropped off during onboarding, not just those who successfully completed the flow.
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'We control for recency bias by triangulating across sessions.' What is recency bias in research synthesis?
Recency bias in synthesis means the researcher gives disproportionate weight to findings from the last few sessions because they're most fresh in memory. Sessions from weeks earlier fade. Triangulation (systematically reviewing all session notes and recordings, not just recalling from memory) and structured analysis methods like affinity mapping combat recency bias by treating all sessions equally in the synthesis.
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Code Review Comment: 'I'm seeing a lot of users selecting option A. Maybe we should prioritize that feature?' This comment demonstrates which type of research bias?
This comment reflects Social Desirability Bias, where the reviewer is interpreting the data (option A selection) to align with a preferred outcome—what users *should* want rather than what they actually *do*. The concern about prioritizing option A suggests confirmation of a prior assumption. Anchoring bias involves an initial piece of information unduly influencing decisions; Hawthorne effect refers to behavioral changes due to observation.
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Slack Message: 'The user survey results show 95% of respondents want dark mode. We should definitely build that!' What potential bias is present in this message's interpretation?
This situation exemplifies Survivorship Bias. The message only considers the responses from users who *completed* the survey, ignoring those who didn't (perhaps due to technical difficulties or differing preferences). Focusing solely on completed surveys provides a skewed picture of overall user sentiment; the other options describe different cognitive biases.
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PR Description: 'The A/B test showed version B had a 20% higher click-through rate. Let's deploy version B immediately!' What's the primary risk associated with this decision?
The PR description demonstrates Recency Bias. Focusing solely on the 20% higher click-through rate from the most recent period (likely the test's final phase) without considering the broader historical data or potential fluctuations is a significant risk. A robust decision would require analyzing longer trend data to determine statistical significance.
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Standup Update: 'We're seeing a lot of positive feedback on the new onboarding flow. Users seem to be really enjoying it.' What potential bias might be influencing this assessment?
This update illustrates the Availability Heuristic. The developer is likely focusing on and overemphasizing the *most recent* positive interactions with the onboarding flow, potentially ignoring negative feedback or longer-term usage patterns. The other options represent different biases – framing influences perception, social desirability describes responding to perceived expectations, and confirmation bias involves seeking evidence to support pre-existing beliefs.
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API Response (JSON): {
"user_segment": "new_users",
"engagement_score": 75,
"platform": "iOS"
} – The team is using this data to understand user behavior. What potential bias could be present if only 'new users on iOS' are analyzed?
This scenario highlights Sampling Bias. By exclusively analyzing 'new users on iOS,' the team isn't capturing the broader spectrum of user behavior across different segments (e.g., existing users, Android users, older demographics). This restricted sample can lead to skewed insights and conclusions about overall engagement.
This exercise, "Research Bias Vocabulary", tests your understanding of ux research vocabulary and phrasing through 10 multiple-choice questions drawn from real workplace scenarios.
Is this exercise free to use?
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How many questions does this exercise have?
This exercise has 10 questions. Each one presents a realistic sentence or scenario with multiple-choice options and an explanation once you answer.
What happens after I answer a question?
You'll see immediate feedback showing whether your answer was correct, along with a short explanation of why — then a button to move to the next question.
Can I retry the exercise if I get questions wrong?
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No — progress within an exercise resets if you navigate away or reload. Each exercise is short enough to complete in a few minutes in one sitting.
Who is this UX Research exercise for?
It's designed for IT professionals and learners who want to sound natural discussing ux research topics in English — useful for meetings, documentation, interviews, and day-to-day communication with English-speaking teams.
How is this different from reading a glossary or blog article?
Exercises like this one are active recall drills — you have to choose the correct term or phrasing yourself, which builds retention faster than passively reading a definition.
Where can I find more UX Research exercises?
Browse the full UX Research exercises hub for more practice, or explore other exercise categories covering vocabulary, grammar, interviews, and workplace communication.