3 survey bias types to avoid (and why)

Surveys are essential tools for businesses, researchers, and policymakers to gather insights and make data-driven decisions. However, if survey bias is present, results can be misleading, resulting in poor conclusions and ineffective strategies. 

Imagine launching a new product based on customer feedback, only to find that the data was skewed due to biased survey design. The consequences can be costly, leading to wasted resources, misguided marketing campaigns, and lost revenue.

Fortunately, survey bias is preventable. We’ll review the most common types of bias in surveys and share how to avoid bias with survey best practices.

Survey bias is a deviation of feedback based on influences by the surveyor and respondent. It occurs when survey methods systematically favor specific outcomes, leading to results that inaccurately represent the target population.

Whether intentional or unintentional, survey bias can occur at any stage, from survey design to data analysis. Leading questions, targeting specific demographics, or ignoring non-respondents can all create bias and skew data.

For example, if a company only surveys its loyal customers, its results may be skewed positively, creating an inaccurate impression of customer sentiment. 

Similarly, the data won’t reflect respondents' true opinions if they feel pressured to give socially acceptable answers.

Poor survey distribution can also lead to bias. If a community service nonprofit only distributes a survey about community concerns online, it may exclude older individuals who are less active on digital platforms, leading to results that do not accurately represent the entire community’s concerns.

Survey bias is common in market research, customer feedback studies, and public opinion polls. Recognizing and mitigating bias is essential to making data-driven decisions based on reality rather than personal perceptions.

Bias in surveys can significantly impact research by distorting findings and leading to:

We'll show you how to address and eliminate survey response bias early to improve your research methods and confidently share your insights with stakeholders.

There are three common types of survey bias, each with unique challenges and implications:

Understanding and addressing these biases in survey research is crucial to ensure accurate feedback from a representative sample.

Let’s review each type of bias and their subcategories:

Sampling bias occurs when surveys are distributed in a way that excludes certain groups. 

To reduce this bias effectively, consider your survey process carefully and use a sampling method that allows inclusivity. 

Examples of sampling bias include:

Non-response bias happens when certain respondents systematically do not participate in a survey.

For example, if HR sends an employee satisfaction survey via email, disengaged or dissatisfied employees may choose not to respond, leading to overly inaccurate feedback. 

Similarly, political polling may be biased if certain demographic groups, such as young voters, are less likely to respond, skewing results toward older populations.

Survivorship bias occurs when the survey data includes only those who have completed a process and ignores non-respondents. 

If a company surveys only long-term customers, it may overlook those who stopped using the product due to dissatisfaction, resulting in an inaccurate assessment of the company’s customer satisfaction levels.

Response bias occurs when survey participants provide inaccurate or misleading answers due to the structure and external conditions of the survey.

Effective survey design can help you tackle response bias by encouraging respondents to answer honestly.

Extreme responding occurs when respondents consistently choose only the highest or lowest response options. Participants may exhibit this behavior by selecting ‘strongly disagree’ or ‘strongly agree’ on a Likert scale question.

Extreme responses can appear in satisfaction surveys, where participants may exaggerate their answers instead of selecting moderate options. For example, an employee engagement survey might show unusually high scores because employees feel pressured to give positive feedback.

Neutral response bias happens when respondents consistently select middle-of-the-road answers, avoiding extreme responses even when they have strong opinions. 

Respondents may return neutral responses to a customer feedback survey when they don’t want to appear overly critical or enthusiastic.

Acquiescence bias occurs when respondents agree with statements regardless of their genuine opinions. For example, in an employee satisfaction survey, participants may select “agree” for most statements out of habit or to avoid conflict rather than genuinely expressing their opinions.

Question order bias occurs when the order of survey questions influences respondents' answers. 

For example, suppose a survey asks about overall job satisfaction before asking about specific job benefits. In that case, respondents may be more likely to respond to the second question in a way that aligns with their previous response to the first question. 

Social desirability bias occurs when survey participants answer questions in a way they believe is socially acceptable rather than truthfully.

For example, in a health survey, respondents may underreport unhealthy behaviors such as smoking or fast-food consumption to appear healthier.

Interviewer bias occurs when the interviewer’s behavior, tone, or phrasing influences survey responses. 

Interview bias can appear through the interviewer's enthusiasm, question structure, or non-verbal cues like facial expressions and body language.

This bias occurs when respondents subconsciously alter their answers based on the interviewer's cues. For example, if the interviewer expresses enthusiasm about a particular product, respondents may be more likely to give positive feedback.

Reporting bias occurs when data analysis selectively emphasizes or ignores specific responses. For example, a company may highlight only positive customer feedback while downplaying negative responses.

Some survey methods are more prone to bias than others. Selecting the right method will ultimately depend on your survey’s goals, audience, and the resources available to you.

Consider these method types when determining how to distribute your survey:

Preventing survey bias requires support from every member of your research team. Create a research plan to avoid common survey question mistakes, and regularly review survey responses to identify and adjust for bias. 

Biased survey questions can lead to misleading responses and distorted data. Here are some survey question examples to help you spot and correct bias:

Leading questions encourage respondents to answer in a particular manner, often favoring one perspective over another. These questions can subtly push participants toward a desired response, skewing survey results.

Double-barreled questions ask about two different things in a single question, making it difficult for respondents to provide an accurate answer.

Loaded questions contain built-in assumptions that can pressure respondents into answering in a specific way.

Researchers who prioritize addressing and eliminating survey bias benefit from greater credibility, more effective strategies, and improved data accuracy. Take proactive steps to eliminate survey bias with survey design best practices. 

Start with SurveyMonkey to design efficient, impactful surveys that yield accurate, reliable insights.