Sampling Methods: Videos & Practice Problems

Sampling Methods focus on how a sample is chosen from a population and whether that sample can reasonably reflect the whole group. A simple random sample occurs when all possible members are placed in one large pool and each has the same chance of being selected, often using a random number generator. A sample can be random without being representative sample if important parts of the population are missing, while a non-SRS may still be representative if key groups are intentionally included.

Common methods are identified by how selection is organized. A systematic sample uses a fixed interval, such as selecting every $k$th item . A stratified sample divides the population into groups and randomly selects at least one subject from each group. A cluster sample uses natural groups, randomly selects some groups, and includes all members within the chosen groups. Distinguishing these methods helps evaluate fairness, coverage, and how well conclusions generalize to the full population.