If a survey has a biased sample, what effect does that have on results?

If a survey has a biased sample, what effect does that have on results?

• A. It undermines validity
• B. Results become more reliable
• C. It improves generalizability
• D. It undermines validity; results may not reflect the population

Answer: (D)

Biased sampling hurts external validity—the ability to generalize findings to the larger population. When the sample isn’t representative, the results reflect the traits of the groups included (and possibly excluded) rather than the population as a whole, so inferences about the population can be off. Reliability, meaning consistency of measurements, can still appear solid within the biased sample, but that doesn’t make the results accurate for the broader group. That’s why the best description is that the results undermine validity and may not reflect the population. The other options try to claim reliability improves or generalizability is enhanced, which isn’t true under sampling bias, and they miss the fuller impact on generalization.

Biased sampling hurts external validity—the ability to generalize findings to the larger population. When the sample isn’t representative, the results reflect the traits of the groups included (and possibly excluded) rather than the population as a whole, so inferences about the population can be off. Reliability, meaning consistency of measurements, can still appear solid within the biased sample, but that doesn’t make the results accurate for the broader group. That’s why the best description is that the results undermine validity and may not reflect the population. The other options try to claim reliability improves or generalizability is enhanced, which isn’t true under sampling bias, and they miss the fuller impact on generalization.