What is Nonresponse Bias?
Nonresponse bias occurs when the people who do not answer a survey differ in important ways from those who do, causing results that are systematically skewed. It can make survey findings misleading even when many people responded.
Nonresponse bias arises when certain groups are less likely to participate (unit nonresponse) or skip particular questions (item nonresponse). If non-respondents differ from respondents on the topic being measured — for example, households with limited English proficiency, younger people, or people with lower income — the collected answers will not accurately represent the whole population. The result is biased estimates, incorrect conclusions and decisions that may overlook the needs or views of underrepresented groups.
Usage example
After the neighbourhood survey, staff realised responses came mostly from English-speaking households; they were worried nonresponse bias meant they were missing the views of residents who speak other languages at home.
Practical application
Understanding nonresponse bias matters because it affects the reliability and fairness of decisions based on survey data. Organisations should both measure and reduce it: measure by comparing respondent demographics to known population benchmarks and checking for patterns of missing answers; reduce by improving accessibility (shorter questions, multiple modes), targeted outreach, reminders and offering the survey in participants' preferred languages. For multilingual outreach, providing one translated survey that participants can use in their own language — and translating answers back for reviewers — directly reduces language-related nonresponse bias and helps ensure more representative feedback.
FAQ
Is a low response rate the same as nonresponse bias?
Not necessarily. A low response rate increases the risk of nonresponse bias, but bias only occurs if non-respondents differ systematically from respondents on the survey topic. A high response rate lowers risk but doesn't guarantee absence of bias.
Can statistical weighting fix nonresponse bias?
Weighting can reduce bias when you know how respondents differ from the target population on key characteristics (age, gender, geography). It cannot correct for unknown differences or for biases in open-text answers, so prevention (better outreach and accessibility) is usually more effective.
How does language access affect nonresponse bias?
Language barriers cause certain communities to participate less or provide shorter, lower-quality answers. Making surveys available in participants' preferred languages — and enabling reviewers to read translated responses — reduces exclusion of non-English speakers and lowers language-related nonresponse bias.