Survey bias examples: 3 types of bias

Survey data plays a critical role in shaping business decisions, product strategies, and customer experiences. But even well-intentioned research can go off track if bias creeps into the process. When surveys are biased, results can misrepresent reality, leading teams to act on flawed insights with real consequences.

Understanding common survey bias examples, where they show up, and how to prevent them is essential for anyone designing or using research. In this article, we break down what survey bias is, how it affects results, and the three most common types of bias researchers encounter: sampling bias, response bias, and interviewer bias.

Along the way, we’ll look at examples of biased survey questions and practical steps you can take to improve data quality from design through fieldwork.

What is survey bias?

Survey bias occurs when the design, distribution, or execution of a survey systematically influences responses in a way that distorts results. Instead of accurately reflecting the target population’s views, biased surveys skew findings toward certain outcomes.

Survey bias can appear at nearly any stage of the research process, from who is invited to participate, to how questions are worded, to how responses are collected or interpreted. Left unaddressed, bias undermines the credibility of research and limits its usefulness for decision-making.

Broadly speaking, survey bias tends to fall into three categories:

• Sampling bias, when the people surveyed do not accurately represent the population of interest.
• Response bias, when respondents give answers that don’t fully reflect their true opinions or behaviors.
• Interviewer bias, when the presence or behavior of an interviewer influences responses.

In more severe cases, bias can overlap with issues like survey fraud, satisficing, or disengaged respondents, compounding data quality problems.

How survey bias affects research and results

The impact of survey bias is far-reaching. When bias is present, the data collected does not accurately represent the views or behaviors of the intended population. This can lead to several negative outcomes:

• Inaccurate Data Representation: Biased surveys misrepresent the target audience, leading to incorrect conclusions.
• Flawed Business Strategies and Decisions: Decisions based on biased data can result in ineffective or even harmful strategies.
• Loss of Trust and Credibility: Stakeholders may lose confidence in your research if they suspect the data is not reliable.
• Wasted Resources: Time and money spent on biased research is often wasted, as the insights generated are not actionable.

For example, a company that only surveys its most loyal customers may overestimate overall satisfaction, missing critical feedback from less engaged or dissatisfied customers. Similarly, a public health survey that fails to reach marginalized communities may overlook important health disparities.

Common types of survey bias

Sampling Bias

Sampling bias occurs when the survey sample does not accurately represent the target population. This can happen if certain groups are systematically excluded or underrepresented.

Examples:

• Non-response Bias: When specific groups are less likely to respond to a survey, their perspectives are underrepresented. For instance, younger people may be less likely to participate in phone surveys, skewing results toward older demographics.
• Survivorship Bias: This occurs when only those who “survive” a process are surveyed, ignoring those who dropped out. For example, surveying only customers who completed a lengthy onboarding process may miss insights from those who abandoned it.

Sampling bias can lead to over or underestimation of key metrics, making it difficult to generalize findings to the broader population. In market research, this can result in missed opportunities or misguided investments.

To help spot it, compare the demographics of your sample to the target population. Are certain groups missing or underrepresented? If so, your results may be biased.

Response Bias

Response bias occurs when respondents provide answers that are inaccurate, incomplete, or influenced by the survey design rather than their true views. This is one of the most common sources of survey bias and one of the hardest to fully eliminate.

Examples:

• Extreme Response Bias: This happens when respondents consistently choose the most extreme options (e.g., “strongly agree” or “strongly disagree”), regardless of their true feelings.
• Neutral Response Bias: At the other end, respondents may select neutral options to avoid expressing strong opinions.
• Acquiescence Bias (Yes-saying): Respondents may agree with statements regardless of their true opinions, especially if the survey is long or complex.
• Social Desirability Bias: Respondents may answer in ways they believe are more socially acceptable, rather than being truthful. For example, they may overreport healthy behaviors or underreport undesirable ones.
• Leading Question Bias: This happens when the wording of survey questions subtly influences a respondent’s answers (whether intentionally or not).

Response bias can distort key findings, making it difficult to identify true attitudes or behaviors. This is especially problematic in sensitive topics, such as health, finances, or workplace culture.

To help spot various types of response bias, look for patterns in the data, such as an unusually high number of extreme or neutral responses, or inconsistencies between related questions.

Interviewer Bias

Interviewer bias arises when the interviewer’s behavior or interaction with respondents influences how answers are provided. This is most relevant in in-person or phone surveys, where tone, body language, or question phrasing can sway responses.

In online surveys, classic interviewer bias is largely absent, since there is no direct human interaction. However, this does not mean online surveys are immune to similar influences. Instead, interviewer bias can manifest digitally through factors like question wording and tone, survey structure, and even prompts.

Examples:

• Demand Characteristics: Respondents may alter their answers based on cues from the interviewer, such as enthusiasm or approval.
• Reporting Bias: Interviewers may unconsciously emphasize or record certain answers based on their expectations.

If gone unrecognized, interviewer bias can lead to overreporting of positive outcomes or underreporting of negative ones, especially in sensitive topics.

To help spot it, compare results from different interviewers or survey modes. Significant differences may indicate interviewer bias.

Survey methods most prone to bias

Different survey methods are susceptible to different types of bias risks:

• Online Surveys: Prone to sampling bias if certain demographics (e.g., older adults, those without internet access) are excluded. Self-selection bias can also occur if only highly motivated individuals participate.
• Phone Surveys: Vulnerable to response bias, especially if respondents feel pressured to answer quickly or in a socially desirable way.
• In-Person Surveys: Susceptible to interviewer bias due to body language, tone, or facial expressions.
• Mail Surveys: Likely to suffer from non-response bias, as response rates are typically low.
• Panel Surveys: Risk response bias from participant fatigue, as frequent survey-takers may provide less thoughtful answers over time.

Understanding these risks helps researchers choose the right method and apply safeguards early in the process.

Strategies to prevent survey bias

Reducing survey bias starts with intentional design and thoughtful execution. Let’s look at a few tactics to help prevent common types of survey bias:

Sampling Bias Prevention

• Diverse Recruitment Sources: Any single research methodology will have limitations in terms of being representative. Regarding online sample design, the Profiles team at Kantar builds panels using a wide variety of recruitment methods and sources, not relying on just one or a few channels. This diversity helps research different demographic groups and minimize bias that could result from over-representing any single source.
• Clear Inclusion or Exclusion Criteria: If criteria are vague or applied inconsistently, you risk including people who don’t fit the target population or excluding those who do, skewing your results. Defining who qualifies for a study upfront (e.g. geographic location, behavioral traits, etc.) helps prevent inconsistencies that can introduce bias later.

Remember to regularly review your sampling methods and compare sample demographics to the target population to identify and correct imbalances.

Response Bias Prevention

• Use Neutral Question Design: Avoid leading or loaded questions that suggest a particular answer.
• Clear and Simple Language: Use straightforward wording to minimize misunderstandings. Additionally, using clear and simple language promotes obtaining more honest and accurate responses.
• Question Randomization: Altering up the sequence in which questions or response choices appear can help reduce order effects and encourage improved answers.

You can read more about what response bias is and get techniques to help minimize it here.

Interviewer Bias Prevention

• Interviewer Training: Train interviewers to use neutral language and avoid leading respondents.
• Standardized Procedures: Use scripts and protocols to ensure consistency.
• Manage Non-Verbal Cues: In in-person surveys, be aware of body language and tone.

For online surveys, use automated instructions and neutral prompts to help minimize bias.

Bias prevention strategies for online surveys

Designing surveys that yield accurate and reliable insights requires minimizing bias at every stage. Here are key strategies to ensure your data reflects genuine opinions:

1. Randomize Question Order

Presenting questions in a random sequence helps prevent order effects, where earlier questions influence responses to later ones. This technique keeps participants focused and reduces patterned answering.

2. Use Neutral Language

Avoid wording that nudges respondents toward a particular viewpoint. Neutral phrasing ensures answers reflect true opinions rather than the survey’s tone or implied preference.

3. Ensure Anonymity

When respondents feel their identity is protected, they’re more likely to provide honest feedback. Anonymity reduces social desirability bias and encourages candid responses.

4. Prevent Straight-Lining or Satisficing

Some participants may rush through surveys, selecting the same option repeatedly. Kantar’s Qubed uses machine learning and AI to detect patterns like straight-lining in real time. It flags respondents who repeatedly select the same option and applies quality checks to maintain data integrity.

5. Keep Language Clear and Simple

Complex or ambiguous wording can lead to misinterpretation, especially in diverse online audiences. Use plain language to make questions accessible and easy to understand.

6. Offer Balanced Response Options

Provide a full range of choices for each question. Balanced options prevent skewed results and allow respondents to select answers that truly represent their views.

7. Pilot Testing

Running a small version of a survey before fully launching helps researchers catch confusing wording, any programming glitches, or signs of bias early on.

Examples of biased survey questions

Understanding what makes a question biased is key to writing better surveys. Here are some common types of biased questions, with examples and improvements:

• Leading Question:
• Biased: “Should concerned parents use child-proof locks?”
• Improvement: “Should child proof locks be required for households with infants?”

   

• Double-Barreled Question:
• Biased: “I’m more active on X and Instagram than most people.”
• Improvement: “I’m more active on social media than most people.” Learn more about how to avoid double-barreled questions in your surveys in our article.

   

• Ambiguous Wording:
• Biased: “Do you regularly exercise?” (What does “regularly” mean?)
• Improvement: “How many days per week do you exercise?”

   

• Unbalanced Scales:
• Biased: “How satisfied are you? (Very satisfied, Satisfied, Somewhat satisfied, Not satisfied)”
• Improvement: Include both positive and negative options equally. (Very satisfied, Somewhat satisfied, Somewhat dissatisfied, Very dissatisfied). Learn more about the various types of survey scales from our article.

Tip: Always review your survey questions or do a test for potential bias before launching your survey.

How to build better surveys

Follow best practices

Accurate data starts with thoughtful design. Check out our 15 survey design best practices, including designing for smartphones, limiting list lengths, reducing repetition, using clear language, balancing scales, and critically testing surveys to ensure a smooth and enjoyable respondent experience.

Design with empathy

Empathy is essential for building trust and encouraging honest responses. Consider the respondent’s experience: keep surveys concise, respect their time, and ensure questions are relevant and sensitive to diverse backgrounds. Offer anonymity and reassure participants that their feedback is valued and confidential. For more on designing with empathy, see Kantar’s guide to applying empathetic survey design.

Conclusion

Survey bias is a critical issue that can undermine the validity of your research and the quality of your insights. By understanding the types of bias, recognizing where they can occur, and implementing strategies to prevent them, you can design better surveys and make more informed decisions. Remember, the best surveys are those that are thoughtfully designed, rigorously tested, and continually improved.

Want to learn more?

Watch our 8-minute module: The Impact of Bias in Surveys to explore factors that have the potential to prompt biased answers and what you can do to collect more reliable data. Or, get in touch with our experts that can provide you with further advice based on your specific research needs and objectives.