Sampling Methods

Sampling is a matter of routine, and the effects of the outcomes can be felt in our day-to-day lives. This chapter discusses four different sample designs: simple random sampling, stratified random sampling, systematic random sampling, and cluster random sampling from a finite population. The primary objective of sampling is to make inferences about population parameters using information contained in a sample taken from that population. Simple random sampling is the most basic form of sampling design. There are two techniques to take a simple random sample from a finite population: sampling with replacement and sampling without replacement. In stratified random sampling, the population is divided into different non-overlapping groups called strata. Systematic random sampling design may be the easiest method of selecting a random sample. Cluster sampling is not only cost-effective but also a time-saver, since collecting data from adjoining units is cheaper, easier, and quicker than if the sampling units are spread out.