gen-define-sampling-bias-and-give-one-short-worked-example-35e9

Define sampling bias and give one short worked example.

# Sampling Bias **Sampling bias** occurs when some members of a population are systematically more likely to be selected for a sample than others, resulting in a sample that doesn't accurately represent the entire population. ## Short Worked Example **Scenario:** A university wants to assess student satisfaction with campus facilities, so they conduct an online survey and email it to all students. **The bias:** Only 15% of students respond—primarily those with strong opinions (very satisfied or very dissatisfied) and those who regularly check university emails. **The problem:** - Students who are moderately satisfied don't bother responding - Off-campus students who rarely use facilities are underrepresented - Students without reliable internet access can't participate **Result:** The survey results don't represent the true satisfaction levels of the entire student body. The university might overestimate problems (if complainers respond more) or miss issues affecting underrepresented groups entirely. **Better approach:** Use stratified random sampling, selecting students from different groups (year, residence, major) and following up with non-responders to increase participation.