Which is an example of sample bias?

• A. Systematic over- or under-representation
• B. Large sample size
• C. Random assignment
• D. A sampling method ensures representativeness

Answer: (A)

Sample bias arises when the sample systematically over- or under-represents certain subgroups of the population, so the findings don’t reflect the population as a whole. In this scenario, some groups appear more or less than their actual share, which distorts the results and makes them ungeneralizable. A large sample size reduces sampling error and improves precision, but it doesn’t inherently introduce bias. Random assignment helps create comparable groups in an experiment and controls for confounding, focusing on internal validity rather than how well the sample represents the population. A sampling method aimed at representativeness is what researchers use to avoid bias. Therefore, the description of over- or under-representation is the example of sample bias.