Sampling bias refers to which issue in research?

• A. Non-representative sample causing results not generalizable.
• B. A perfect random sample that makes results generalizable.
• C. A large sample size reducing measurement error.
• D. A statistical test that controls for confounding variables.

Answer: (A)

Sampling bias shows up when the group chosen for the study doesn’t reflect the broader population researchers want to learn about. When the sample is not representative, the findings mirror the specific characteristics of that group rather than the population as a whole, so the results can’t be generalized. This is why the issue described is about a non-representative sample leading to limited generalizability and weaker external validity.

For example, surveying only college students to infer about all adults would produce biased results because students differ in important ways from non-students. An ideal random sample helps prevent this bias, since every member of the population has a chance of being included. A large sample size helps reduce random error but doesn’t fix bias if the sampling method is not representative. Controlling for confounding variables relates to internal validity, not sampling bias.