Systematic sampling, stratified sampling, and cluster sampling are other types of sampling approaches that may be used instead of simple random sampling.

Researchers choose simple random sampling to make generalizations about a population. Major advantages include its simplicity and lack of bias.

Learn how simple random sampling works and what advantages it offers over other methods when selecting a research group from a larger population.

Example 62.2: Simple Random Cluster Sampling This example illustrates the use of regression analysis in a simple random cluster sampling design. The data are from S rndal, Swenson, and Wretman (1992, ...

A sample of 100 customers is selected from the data set Customers by simple random sampling. With simple random sampling and no stratification in the sample design, the selection probability is the ...

In this paper we derive the exact covariance of some sample moments for 'simple random sampling with replacement' (SRSWR) of a finite population. An application of the results is given for estimating ...

The results obtained by the authors so far on controlled sampling with equal probabilities and without replacement have been consolidated in this paper in an integrated fashion. Utilizing these ...

A simple random sample is a subset of a statistical population where each member of the population is equally likely to be chosen.