Research Topics:

Data Analysis | Data Quality

Content Type:

Glossary

Biased Sample Definition

A sample that misrepresents the population of interest.

A biased sample refers to a subset of a larger population that does not represent the whole population because of errors in the sampling process. This phenomenon leads to overrepresentation or underrepresentation of certain characteristics or groups within the sample. Bias can stem from factors like flawed sampling methods, non-random selection or intentional manipulation. Researchers must reduce incidences of biased samples so their studies are considered trustworthy, reliable and accurate.

Who should I care about a biased sample?

Researchers, marketing professionals, policymakers and anyone making decisions based on data can unintentionally or intentionally rely on biased samples. For instance, biased samples might be used to promote a particular agenda or viewpoint, thus leading to misleading conclusions. It is fair to say that no researcher intends on creating biased samples because bias undermines the credibility of findings. That said, surveys and studies created without care could contain a level of bias that adversely impacts their findings. Decision-making based on biased data can result in poor choices and ineffective strategies. Biased samples can perpetuate stereotypes, misinform the public and hinder progress by promoting policies that do not address the actual needs of the population.