21. Sampling Methods and Bias

A population is the entire group a study wants to learn about. A sample is a smaller subset of the population actually measured, used to make inferences about the whole population.

Common sampling methods:

• Random sampling: every member of the population has an equal chance of being selected, producing the most representative sample.
• Systematic sampling: selecting every nth member from a list.
• Stratified sampling: dividing the population into subgroups (strata) and sampling proportionally from each.
• Convenience sampling: selecting whoever is easiest to reach, which is fast but often unrepresentative.

Bias occurs when a sample systematically over- or under-represents parts of the population, leading to conclusions that do not generalize to the whole group.

Example of biased sampling: surveying shoppers at a single mall on a weekday afternoon to estimate the favorite sport of an entire city introduces bias, because the sample excludes people who work during the day, live far from that mall, or shop elsewhere.

GED® strategy: when a question describes a survey or study, check whether the sampling method could realistically represent the full population named in the question. A large sample size does not fix a biased sampling method.