nonrepresentative

Nonrepresentative is an adjective used to describe a sample, data set, or group that does not reflect the characteristics of the population it is meant to represent. In statistics and research, nonrepresentativeness can limit the generalizability of findings and introduce bias if differences between the sample and population relate to the studied variables.

Nonrepresentative samples occur when the sampling method or data collection fails to capture the population’s diversity. Common causes include selection bias, undercoverage of subgroups, nonresponse, and reliance on convenience or volunteer samples. Framing errors and measurement biases can also contribute.

Implications include biased estimates of population parameters, distorted effect sizes, and incorrect inferences about relationships. Because external validity hinges on representativeness, researchers should assess and report how well the sample matches the population.

Examples include online surveys with self-selected participants, opt-in panels, or studies conducted via phone directories that exclude unlisted numbers. In experiments, lack of randomization or proper stratification can yield nonrepresentative samples even with large sizes.

Mitigation strategies emphasize probability-based sampling and replication. Techniques such as stratified sampling, quota sampling with weighting, and post-stratification adjustments can improve representativeness. Researchers compare sample demographics to population benchmarks and disclose limitations related to sampling and nonresponse.

Outside statistics, nonrepresentative may describe a person or item that does not typify the broader group, limiting its applicability as a model or example.