What is the difference between sampling bias and measurement error?

• A. Sampling bias arises when the sample is not representative; measurement error arises from inaccuracies in data collection.
• B. Sampling bias arises from measurement error
• C. Measurement error arises from sampling bias
• D. They are the same

Answer: (A)

The main idea is to separate where the error comes from: how the sample is chosen versus how the data are collected. Sampling bias happens when the way you select participants makes the sample systematically differ from the population, so the results don’t generalize. For example, surveying only people who shop at a particular store may miss other groups and tilt estimates toward that subgroup. Measurement error, on the other hand, comes from inaccuracies in the data you collect—imperfect instruments or methods that record wrong values. Think of a miscalibrated scale or a survey question that prompts biased answers; these errors affect the recorded data itself, regardless of who you sampled. So sampling bias threatens generalizability by biased selection, while measurement error threatens data accuracy by faulty collection or recording.