Representatiivust

Representatiivust (representativeness) refers to the extent to which a sample, observation set, or study setting reflects the characteristics of the population or phenomenon to which the results are intended to generalize. It is a central concern in statistics, survey research, and social sciences, because higher representativeness increases the external validity of findings.

In practice, representativeness is about matching distributions of key attributes such as age, gender, region, education, income, and other relevant variables. It also involves covering the full range of contexts in the population and avoiding systematic biases in selection, response, or measurement. Representativeness can be assessed by comparing sample statistics with population benchmarks, or by using probability-based sampling frames.

Strategies to improve representativeness include probability sampling methods (simple random, stratified, cluster sampling), systematic sampling, and ensuring adequate sample size. Quota sampling and stratification can target subgroups, while weighting and post-stratification adjust the sample to known population margins. Efforts to maximize response rates and minimize nonresponse bias also support representativeness.

Limitations exist: perfect representativeness is difficult; biases such as nonresponse, measurement error, and coverage gaps can distort it. In qualitative research, representativeness may be less critical than the depth of insight. Researchers should clearly report the sampling design and consider the degree of generalizability when interpreting results.