When you collect data, the way you pick the units matters. A simple random sample gives every individual an equal chance to be chosen. A systematic sample picks every kth unit after a random start. In a stratified sample you first divide the population into non‑overlapping groups (strata) that share a characteristic, then draw a simple random sample from each stratum. A cluster sample groups the population into clusters (often natural groups like households or boxes) and then selects whole clusters at random, surveying every member inside. Anything that does not follow one of these rules is “none of these.” Knowing which design you used tells you how well the sample represents the whole population and what analysis methods are appropriate.
No problems in this chapter yet — imports and generated practice are on the way.