Sample representativeness refers to the degree to which a drawn subset (the “sample”) mirrors the population of interest in terms of the characteristics (behaviors, attitudes, etc.) being studied.
Statisticians use a variety of sampling approaches to build samples that are representative. One approach to generating a representative sample is random sampling where the sample is drawn in such a way that each member of the population of interest has a known and equal chance of inclusion.
Another approach is to stratify the sample based on incidence of key factors related to the characteristics being studied. Often these include demographics (e.g. gender, age, race, ethnicity, household income, education), geography and purchase behavior.
Such stratification does not necessarily remove biases in the sample from recruitment approach, survey compensation, and other sources but it can improve the precision of estimates. 
- Common Language in Marketing Project, 2021.