If you’ve ever been asked, “How satisfied were you with [product]?” then you’ve experienced a leading question. This question assumes that you had a satisfactory experience (how satisfied) and primes you to think positively about it, which can end up influencing your reply.
That is the essence of leading questions–they can strongly influence how participants answer them based on their structure and words. As a result, they impact your results and affect what your team decides to prioritize in product development.
Whenever we do any research, whether its user interviews, customer satisfaction surveys, or field research, we should keep our assumptions in check and ask better questions.
In this article, we’ll look at what leading questions are, how they influence UX research, identify four kinds of leading questions you should know about, and share actionable advice on how to avoid asking them.
Conducting user interviews is a key part of UX testing It provides insights into how customers really use your product.
However the way you phrase questions can influence the responses you get. Leading questions subtly guide users towards certain answers skewing your results.
In this article, I’ll explain what leading questions are, why they harm UX testing, and how to phrase neutral, open-ended questions.
What Are Leading Questions?
Leading questions subtly prompt the respondent to answer in a certain way. Here are some examples:
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“Don’t you find this checkout process frustrating?”
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“What do you like most about our new dashboard design?”
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“How much time would you say you’ve saved with our product?”
These questions “lead” users towards criticizing the checkout, praising the dashboard, and exaggerating time savings.
Leading questions often include:
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Assumptions – e.g. “you find this frustrating”
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Superlatives – e.g. “like most”
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Presumed knowledge – e.g. “time you’ve saved”
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Loaded terms – e.g. “frustrating”
The question structure steers users to respond in the direction you’ve suggested.
Dangers of Leading Questions
Leading questions harm UX research in several ways:
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Biased data: Users tend to agree with the question’s implications, rather than stating their true opinion. This skews results.
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Flawed analysis: Product teams may draw inaccurate conclusions about user needs.
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Wasted resources: Developers end up working on “solutions” to non-issues.
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Missed issues: Real UX problems get overlooked due to the skewed data.
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Inauthentic feedback: Users may repeat your phrasing rather than offering original thoughts.
Leading questions undermine UX research. They distort data and point teams in the wrong direction.
Examples of Leading vs Neutral Questions
Here are some examples contrasting leading and neutral UX interview questions:
| Leading | Neutral |
|---|---|
| Don’t you hate filling out long registration forms? | How do you feel about the registration process on our site? |
| What do you love most about our intuitive interface? | What’s your experience been like using our interface? |
| How much time have you saved using our product? | Can you describe how our product may have influenced your workflow? |
Notice how the neutral versions don’t make assumptions or load the question with specific language. They simply open the door for users to fully explain their thoughts.
Tips for Neutral, Open-Ended Questions
Here are some tips for phrasing open-ended, non-leading questions:
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Avoid assumptions about the user’s experience.
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Ask “what” and “how” rather than “why”.
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Use neutral terms, not superlatives or emotionally loaded words.
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Allow room for negative or critical feedback, don’t just fish for praise.
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Invite users to elaborate or provide examples to support their views.
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Ask follow-up questions for clarity if you receive a vague answer.
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Focus questions on specific UI elements, interactions or workflows.
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Make questions conversational and friendly to put users at ease.
Well-phrased, thoughtful questions will uncover user insights without pushing them in a certain direction.
Question Types to Guide Discussion
Certain question frameworks can encourage open-ended discussion:
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“Take me through…” – Ask users to verbalize walking through a task or workflow. Don’t interrupt, let them describe their real experience.
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“Tell me about…” – Invite users to expand on their background, needs and motivations beyond just your product experience.
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“Imagine you could…” – Get suggestions by having users envision ideal scenarios, without being limited by existing features.
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“What if…” – Spur creative problem-solving by proposing hypothetical changes and getting reactions.
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“Compare…” – Rather than asking their opinion of one thing, have users compare pros and cons of two options.
Using frameworks like these, rather than direct closed-ended questions, results in richer qualitative data.
Suggested Question Phrasing
Here are some suggested open-ended question stems:
- “Walk me through how you normally …”
- “Tell me about your experience…”
- “Describe what happens when…”
- “Explain your thoughts on…”
- “Talk me through your process for…”
- “Imagine you could suddenly…”
- “What if our product could…”
- “Compare how you…”
These question forms invite elaboration without injecting bias. Follow the user’s lead rather than steering the discussion.
Probing for More Detail
Asking probing follow-up questions yields deeper insights:
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“What do you mean when you say [X]?”
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“Can you expand on [Y]?”
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“Why do you feel that way?”
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“Could you provide an example of [Z]?”
These types of probes elicit more detailed explanations and concrete examples to illustrate users’ general statements.
READ Technique
The READ technique is another method for neutral probing:
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Repeat or rephrase their statement – “So you feel the checkout was confusing…”
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Elaborate – “Could you expand on what felt confusing about checking out?”
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Ask for examples – “Do any specific checkout issues come to mind?”
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Drill down – “Interesting, walk me through that issue step-by-step.”
This systematic approach prevents you from interjecting your own biases when following up.
Avoiding Body Language Bias
Your body language can also influence user responses, so appear neutral:
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Maintain open posture, don’t cross your arms.
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Avoid nods/raised eyebrows that may signal agreement or surprise.
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Lean forward slightly to show engagement.
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Take notes to remain focused on their words rather than nonverbally reacting.
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Verbally affirm their responses in an unbiased way, like “I see” or “that’s helpful context”.
Monitoring your real-time reactions ensures you don’t sway the conversation through unconscious cues.
Plan Ahead to Avoid Leading Questions
With some preparation, you can proactively phrase neutral questions:
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List topics – Know the areas you want to probe, but don’t draft rigid question lists.
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Consider biases – Reflect on any biases or assumptions you may have about the user or product. Set those aside.
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Have an objective – Instead of validation, focus on truly understanding user obstacles and needs.
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Draft open-ended questions – Practice non-leading phrasing and transitions to keep dialogue natural.
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Remain flexible – Be ready to adapt questions based on what you learn from each subject.
Planning ahead helps avoid inadvertently slipping into biased questioning in the moment.
Summary of Key Points
To recap, here are some key tips for avoiding leading questions in UX interviews:
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Know what types of questions lead users towards certain answers.
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Use neutral phrasing focused on the user’s real experience.
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Ask follow-up probes to uncover more details.
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Apply question frameworks that encourage open-ended narrative.
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Monitor your body language for unconscious reactions.
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Prepare guidelines, but don’t stick rigidly to question lists.
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Focus interviews on understanding, not validating assumptions.
With practice and awareness, you can conduct productive UX research based on authentic user perspectives, not biased interview questions. Feel free to reach out if you need any help formulating neutral questions for an upcoming study!
Consequential leading questions
These questions—which are often also called direct implication questions—ask people to predict their behavior and future events. Questions that require users to make predictions about their future behavior are ineffective because we’re not good at predicting our behavior reliably.
- If you found what you were looking for today, will you come back and shop with us again?
- Imagine our product helped you save more money. Would you open another account with us?
How to reframe consequential questions: Framing questions based on current or existing experiences that people have had will produce more accurate survey results. For example, the first leading question above can be redesigned into a multi-select question:
Based on your previous experience(s) shopping with us, please share what contributes to you visiting our store:
- The staff
- Cost of goods
- Selection of goods
- Quality of goods
- Location of the shop
- Other (please specify)
This allows the person to share why they visited the store, which yields more useful information than just asking if they will return or not.
Want more from your next customer survey? Use Maze AI to write dynamic follow-up questions that leave no insights undiscovered.
How leading questions affect UX research
In UX research, leading questions impact the accuracy of results and what a team prioritizes to build. If you’re running user interviews or UX surveys with leading questions, you can get false feedback—either too positive or negative feedback unrepresentative of people’s actual lived experiences.
Leading questions are often caused by UX cognitive biases, like the framing effect, where the way a question is presented (either positively or negatively) impacts how someone responds.
A frequently used example of a positively-framed, leading question is “How easy was this product to use?”. This question instantly assumes the product is easy to use.
When research questions are framed incorrectly, it leads to missed opportunities to learn how to improve your product, which is the inherent intent of user research.
Positively-framed questions over-index on delightful experiences, leaving no room for people to share what didnt go well or what they disliked. When research questions are framed incorrectly, it leads to missed opportunities to learn how to improve your product, which is the inherent intent of user research.
As UX practitioners, we should make our participants feel like they can share all types of feedback, whether positive or negative. We must allow them to do so by asking open-ended, non-biased questions that focus on actual experiences, not assumptions.
The ease of use question above makes an assumption based on how someone experienced a product. Other types of leading questions make assumptions about people’s feelings or emotions, how they compare to others, and the cause-and-effect of their future actions. Let’s look at four examples of the different types of leading questions.
Episode 72: Avoid Leading and Loaded Questions
Why are leading questions important in UX research?
In UX research, leading questions impact the accuracy of results and what a team prioritizes to build. If you’re running user interviews or UX surveys with leading questions, you can get false feedback—either too positive or negative feedback unrepresentative of people’s actual lived experiences.
What causes leading questions in UX?
Leading questions are often caused by UX cognitive biases, like the framing effect, where the way a question is presented (either positively or negatively) impacts how someone responds. A frequently used example of a positively-framed, leading question is “How easy was this product to use?”.
How do you avoid leading questions?
The order in which you ask questions is also critical to avoiding leading questions. First, confirm that respondents had the experiences you’re asking them to speak about, then ask a neutral question about their experiences. 2.
Are leading questions biased in a usability-study interaction?
This is particularly true in a usability-study interaction, where often the interviewer is the “authority” in the room and many participants will not want to disagree. Leading questions result in biased or false answers, as respondents are prone to simply mimic the words of the interviewer.
