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Population representativeness refers to the degree to which a sample or dataset reflects the characteristics of the broader population from which it is drawn. A representative sample supports generalizing findings, estimating population parameters, and informing policy decisions. The concept contrasts with convenience samples or biased datasets that over- or under-sample certain groups.

Achieving representativeness typically involves probability-based sampling methods, such as simple random sampling, systematic sampling, stratified sampling, or cluster sampling, using a sampling frame that covers the population of interest. Random selection helps ensure that each member has a known chance of inclusion. Stratified sampling can preserve the distribution of key characteristics (age, sex, region) within the sample. When full representativeness is not possible, researchers may apply weights to adjust for differential selection probabilities and nonresponse; post-stratification and calibration can align sample distributions with known population totals.

Limitations include nonresponse bias, undercoverage, measurement error, and time lags between data collection and the current population structure. Some populations, such as marginalized groups or online-only populations, can be difficult to reach, reducing representativeness. Even well-designed samples may still miss unique subgroups that are rare or dynamic.

In reporting, researchers assess and document the sampling design, response rates, weighting schemes, and the degree of alignment with population characteristics to support interpretation of results and generalizability.