Avoiding Bias in User Research: Practical Insights for Better UX

User research is the cornerstone of great design, but it can be fraught with challenges—bias being one of the most pervasive. Even the most seasoned researchers can unintentionally lead participants or misinterpret responses, resulting in skewed data and faulty conclusions. Fortunately, by crafting thoughtful questions and maintaining a neutral mindset, you can reduce bias and gather meaningful insights.

This blog, inspired by Sundar Subramanian’s practical guide, explores common pitfalls in UX research and how to avoid them.

The Danger of Leading Questions

Leading questions subtly nudge participants toward a particular response, often invalidating the data. Let’s look at some examples and how to reframe them:

❌ “Would you recommend [a product] to a friend or colleague?”
✅ “Have you referred [a product] to a friend or colleague this year?”
✅ “Have you discouraged someone from using [a product]?”

Reframing questions to focus on actual behavior rather than intentions prevents assumptions about the participant's future actions.

Avoid Ambiguity: Be Specific and Contextual

Vague questions lead to vague answers. Asking about “likes” and “dislikes” might not give you actionable insights. Instead, anchor your questions in specific contexts:

❌ “What did you like or dislike while using [a product]?”
✅ “Can you describe the last time you used [a product]?”
✅ “What adjectives fit best to describe your experience?”

By asking for specific examples, you’re encouraging participants to recall concrete details, making their responses more reliable.

Focus on Actions, Not Preferences

When comparing designs or features, avoid asking for subjective preferences. Instead, evaluate usability through task-based questions:

❌ “Which of these two versions do you prefer, and why?”
✅ “Please complete the following tasks in these two versions.”
✅ “Could you describe your experience in both?”

This approach reduces bias by prioritising actual performance and user feedback during interaction, rather than personal opinion.

Observe How People Solve Problems

Understanding how users complete tasks is more insightful than asking if they’ve used a specific feature:

❌ “Have you ever used this feature in [a product]?”
✅ “How do you usually complete this task?”
✅ “What features do you use frequently in [a product]?”

This allows you to see whether the feature supports the task effectively or if users rely on workarounds.

Avoid Hypotheticals, Focus on the Past

When asking about technical support or issues, steer clear of hypotheticals and anchor questions in real experiences:

❌ “Do you often need the support of a technical person?”
✅ “What technical issues do you experience frequently?”
✅ “How do you usually manage these technical problems?”

Framing questions around past actions provides richer, more actionable insights compared to speculative responses about the future.

Randomisation Matters

When presenting lists or options to participants, be mindful of the influence that order can have on responses. Instead of alphabetical lists, randomize the options to avoid bias:

❌ “Choose the most useful features in the alphabetical list below.”
✅ “Choose features that you use often in the randomised list below.”
✅ “Which features do you find confusing in your work?”

Randomisation ensures that your results reflect actual preferences rather than positional bias.

Key Principles for Bias-Free Research

1. Observe, Don’t Assume

What people say, do, think, and feel often differ. Always measure actions like task completion times and success rates. Be cautious when interpreting verbal feedback, as participants might frame their responses based on what they think you want to hear.

2. Validate Insights

Always check and recheck conclusions. Repeat users’ words back to them to clarify meaning. Playing devil’s advocate to your own assumptions helps prevent confirmation bias.

3. Manage the Anchoring Effect

Beware of overemphasising data from the first and last interviews. Break down sessions into smaller, randomised chunks during analysis to ensure objectivity.

4. Frame Questions Around the Past

Good research is grounded in real actions, not future predictions. Focus on questions that explore previous experiences rather than hypothetical scenarios. For example:

• Instead of asking, “Would you use this feature?”, ask, “How have you completed this task in the past?”

Conclusion

Reducing bias in UX research isn’t about achieving perfection—it’s about fostering accuracy. By crafting thoughtful questions, grounding insights in real behaviour, and validating findings, you can make more informed design decisions that truly meet user needs.

Remember: the goal of research is not to confirm your assumptions but to uncover the truth. With this mindset, you’ll be well on your way to conducting impactful, bias-free UX research.