 Sampling design

A sampling design specifies a probability distribution over the possible samples from a defined population:For sampling distinct labelled units, $s$ is a subset of the population. The design includes the selection mechanism, not merely the final sample size. A procedure with repeated draws can instead require a distribution over ordered sequences; the resulting inclusion probabilities concern whether a label appears at least once.

The design determines inclusion probabilities and joint inclusion probabilities throughThese connect the physical sampling procedure to the Horvitz-Thompson estimator and its sampling variance.^[ref]

An original comparison uses four fixed values $(0,0,10,10)$ and selects two units. Under simple random sampling, every one of the six pairs has probability $1/6$. Each ${\pi }_i=1/2$, so the Horvitz-Thompson estimator is twice the selected sum. It takes values $0,20,40$ with probabilities $1/6,4/6,1/6$. Its expected value is the true finite population total $20$, and its variance isA second design chooses only $\{1,2\}$ or $\{3,4\}$, each with probability $1/2$. It has the same sample size and the same individual inclusion probabilities, but the estimator is now $0$ or $40$, giving variance $400$. Pair selection matters even when individual representation agrees.

A third design chooses uniformly among the four pairs containing one label from each of those two groups. Its total estimate is always $20$ for this particular population. This illustrates the potential gain from stratified sampling when groups correspond to relevant variation, not a claim that the third design dominates for every possible population. A reproducible design records the eligible labels and actual random selection rule before interpreting the observed sample.