Ensuring Objectivity in Customer Research: 10 Tips for Crafting Unbiased Interview Guides

Most bias in a customer interview is written into the discussion guide before any participant speaks, which makes the guide the cheapest place to remove it. This guide covers the eight kinds of bias a guide can introduce, ten fixes with before-and-after examples, and a checklist to run before launch. The same controls apply whether a human or an AI moderator runs the interviews.

Where bias enters an interview guide

Bias can enter a guide at five points. The wording of a question can carry the team’s assumption, and the order of questions can prime later answers. How the study and its sponsor are introduced shapes what participants think the team wants to hear. Assumptions about the participant can force invented answers, and the moderator’s follow-up probes can suggest a reason. In qualitative research the guide is the questionnaire, so a flaw in it reaches every transcript. In an AI-moderated study the guide matters even more, because the moderator follows it with every participant and a leading question repeats at scale.

A guide can introduce eight kinds of bias:

• Leading questions: The wording implies the answer the team wants.
• Loaded language: A charged word sets the participant’s verdict before they answer.
• Double-barreled questions: Two questions share one answer, and the team cannot tell which half it covers.
• Acquiescence: Participants agree with whatever is put in front of them, because agreeing is easier.
• Social desirability: Participants describe a better version of themselves than the one who made the purchase.
• Order effects: An early question primes the answer to a later one.
• Sponsor bias: Participants who know which brand is paying tell that brand what it wants to hear.
• Assumption bias: The question presumes an experience or opinion the participant does not have.

Ten tips for writing unbiased interview questions

The ten fixes address question wording and guide structure, from neutral probes to sponsor timing. Pilot sessions and a second reviewer check how the guide works before launch.

1. Replace loaded words with neutral ones

Swap charged words for neutral ones, because a loaded word sets the participant’s verdict before they answer.

Example (retail checkout):

• Before: “How frustrating is the checkout?”
• After: “Walk me through the last time you checked out.”

The retail team put its verdict into the question, and participants react to a charged word more than to the question around it. The rewrite carries no verdict and asks for an account, so if checkout was frustrating, the participant will say so in words the team can quote.

2. Ask what happened, not what people think they would do

Ask what participants did on a recent occasion, not what they would do, because hypotheticals invite optimistic forecasts and stated plans only loosely match behavior.

Example (fitness app workout):

• Before: “How often would you use a 10-minute guided workout?”
• After: “How many times did you work out last week?” with the probe “Walk me through the most recent one.”

Anchoring on a specific recent instance gives the team a behavior to count and a story to probe. A promise about next month gives it neither. It also reduces social desirability, the pull to describe an idealized self instead of the person who actually skipped the workout.

3. Split double-barreled questions

Ask about one thing per question, because a double-barreled question gets one answer to two questions and the team cannot tell which half it covers.

Example (investing app onboarding):

• Before: “How easy and fast was setup?”
• After: “How easy or difficult was it to set up your account?” followed by “How long did setup take?” with the probe “How did that compare with what you expected?”

Participants pick one half and ignore the other, and because different participants pick different halves, the answers cannot be compared. With two questions, the team gets two answers it can count. A participant who found setup slow but simple can now say both.

4. Do not reveal the sponsor or the hypothesis early

Hold back the sponsor and the hypothesis until the brand-specific section, because naming the brand early invites sponsor bias.

Example (airline loyalty tier):

• Before: “We’re calling on behalf of the airline and we’re testing a new loyalty tier. What do you like about it?”
• After: “Tell me about the last time you chose which airline to fly.” with the probe “What tipped the decision?” The tier concept and the airline’s name arrive after the category questions.

Participants who know which brand commissioned the study can soften their criticism, and loyal customers may do so without noticing. Introducing the study by topic keeps the first half of the interview about the participant’s own choices, so the concept lands on a baseline rather than a politeness reflex.

5. Order from broad to specific

Order the guide from broad to specific, because an early question can prime later answers.

Example (streaming ad-supported tier):

• Before: “Here is our new ad-supported plan. Which of your streaming services would you drop to pay for it?”
• After: “Which streaming services do you pay for right now?” with the probe “Which one did you add most recently?” The plan is shown only after unaided recall and current spending are on record.

Category questions and unaided recall come before any brand or concept appears, and the moderator holds back stimuli until then. Once the concept is on screen, every later answer is about it, so the unaided answers collected first are the only clean read of where the brand stood.

6. Avoid yes/no questions that invite agreement

Replace yes/no questions with open ones, because participants tend to agree with whatever is put in front of them.

Example (B2B scheduled reports):

• Before: “Would you use scheduled reports?”
• After: “How, if at all, would scheduled reports fit into your week?”

A B2B software team asking this as a yes/no will hear more yeses than the feature has earned. Krosnick’s 1999 Annual Review article pooled 10 studies and found 52% agreed with an assertion while only 42% disagreed with its opposite. The rewrite asks the participant to place the feature in a specific week, and someone who cannot say where it fits has answered the question the team cared about.

7. Make room for negative answers

Give participants explicit permission to say nothing stood out, because polite pressure pushes them to produce praise.

Example (retail loyalty program):

• Before: “What did you like about the new loyalty program?”
• After: “What, if anything, stood out about the new loyalty program, good or bad? It is fine to say nothing did.”

A guide that only asks what participants liked returns a list of likes, when the team wants to know whether the program registered at all. The “if anything” clause and the stated permission remove the implied requirement to produce praise. When the first answers come back as “nothing”, the team learns the program did not land.

8. Do not assume knowledge or experience

Establish who has the experience before asking about it, because a question that presumes experience forces participants to invent an opinion, which is assumption bias.

Example (airline mobile check-in):

• Before: “What do you like about checking in through the airline’s app?”
• After: The screener asks, “How did you check in for your most recent flight?” Only participants who used the app then get “Walk me through that check-in.”

The transcript cannot tell an invented opinion from one built on use. Screening on recent behavior puts the question in front of people who can answer it from memory, and conditional branching in the guide routes everyone else around it. The team ends up with fewer answers, all of them usable.

9. Write neutral follow-up probes into the guide

Write neutral follow-up probes into the guide, because a probe improvised in the moment often carries the moderator’s own theory.

Example (savings app churn):

• Before: “Was that because of the fees?”
• After: “What happened next?” and “Tell me more about that.”

A team asking why people stopped using a product should keep its own suspected reason out of the probe. In Smit, Dijkstra and van der Zouwen’s 1997 experiment across 235 interviews, the median score on one item was 15 under neutral probing and 50 when the interviewer suggested the answer. Written-in probes protect human and AI moderators alike. A human moderator has a bank of safe phrases to reach for mid-conversation, and an AI moderator draws on the neutral probes written into the guide as it follows up on each answer.

10. Pilot the guide and review it with fresh eyes

Pilot the guide and have a researcher who did not write it review it, because a drafting team stops seeing the leading wording in its own questions.

Example (streaming home screen pilot):

• Pilot question: “How much simpler is the new home screen?”
• What the pilot showed: Two of the three pilot participants answered “yeah, it’s simpler”, echoing the question’s own wording.
• Rewrite before launch: “How does the new home screen compare with the old one, if at all?”

An answer that repeats the question’s wording signals the participant took the answer from the question, so the question is leading and the team rewrites it before launch. NN/g’s pilot-testing guidance puts a pilot at one or two sessions. Running two or three, and reading them as transcripts, gives the team a second read. A second researcher who did not write the guide reads it cold and marks every question whose answer they could predict.

How bias shows up differently in human- and AI-moderated interviews

AI-moderated interviews are a newer research method in which an AI moderator holds a live, voice-to-voice conversation with each real participant, follows the researcher’s guide and adapts follow-up questions to what each person says. The method sits between surveys and in-depth interviews and brings adaptive follow-ups to survey scale.

A human moderator hears a participant mention price and improvises a probe that carries a theory the guide never wrote down. That same improvisation builds the rapport that draws out detail a script would miss. Who asks the question can also shift the answer, on factual questions as well as opinions.

An AI moderator follows the same guide with every participant, so any bias written into the guide repeats in every interview, with no moderator adjusting it on the fly. Industry pilots of AI moderation tools reported in Quirk’s in January 2026 found they can miss a hesitation or a shift in tone. Reading those signals, and deciding that a tangent outside the objectives deserves the next ten minutes, are researchers’ calls, made in the guide beforehand or by a human moderator in the room.

Teams choose human moderation for exploratory work where the questions are not yet known, for sensitive topics and for sessions they want to observe live. On Strella, teams choose AI, human or hybrid moderation study by study, and teams weighing the choice can try a sample interview to hear how the AI moderator follows up on an answer. If a study will run hundreds of AI-moderated interviews, spend an extra review cycle on the guide; if it turns on emotion a participant may struggle to voice, put a human moderator in the room.

A pre-fielding checklist for an unbiased guide

The ten tips compress into a review a second researcher can run on a draft:

• No loaded or evaluative words in any question
• No question asking two things at once
• Category questions before brand questions; concept questions last
• Sponsor and hypothesis withheld until the brand-specific section
• Every behavior question anchored on a specific recent instance
• Neutral probes written next to each open question
• “If at all” or “if anything” phrasing and explicit permission to say “none”
• Screener or branching in place for every question that presumes experience
• A pilot of two or three sessions read as transcripts for answers that echo the question
• Sign-off from a researcher who did not write the guide

How Strella helps teams write unbiased guides

Strella drafts a discussion guide from the objectives the team enters, and a built-in check flags leading or biased questions before launch. Researchers edit every question and decide what stays. Once the study runs, the AI moderator follows the same guide with every participant and asks follow-ups based on what each one says, in voice-to-voice conversations with real people, in 40+ languages.

Bring your research question to us and we will talk it through. See Strella in action.

FAQ

What is a leading question in an interview?

A leading question is one whose wording implies the answer the interviewer expects, so the participant confirms that theory instead of describing their own experience. The most common forms are closed questions that start with “did”, “was” or “is” (“Was that helpful?”) and clarifiers that supply a reason (“Did you choose it because it was cheaper?”).

How do you write unbiased interview questions?

Teams write unbiased questions by asking for an account of a specific recent event, in neutral words, one question at a time, with the probes written down in advance. They ask category questions before brand questions, then introduce the concept. They hold the sponsor back until it is needed and give participants explicit room to say nothing stood out. Then they pilot the guide and have a second researcher read the transcripts.

What are examples of biased survey or interview questions?

“How frustrating is the checkout?” is loaded, “How easy and fast was setup?” is double-barreled, “Would you use scheduled reports?” invites agreement, and “What do you like about checking in through the app?” assumes the participant has used the app.

How many pilot interviews should a team run?

Published pilot-testing guidance suggests one or two pilot sessions; running two or three gives a second read, though no threshold has been validated. The team reads transcripts for answers that repeat the question’s words.

Can AI-moderated interviews reduce interviewer bias?

AI-moderated interviews remove session-to-session inconsistency, because the moderator follows the same guide with every participant and does not change tone or improvise a different probe. They do not remove bias written into the guide; that bias repeats in every interview, which is why the guide check and pilot matter more in an AI-moderated study.

What is the difference between a leading and a loaded question?

The difference is where the pull sits: a leading question pulls through its structure, while a loaded question pulls through a charged word that carries a verdict. “Was setup easy?” leads; “How frustrating is the checkout?” is loaded. A question can be both, and the fix is the same: ask for the participant’s account of what happened.