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

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

In simple random sampling, each unit has an equal probability of selection, and sampling is without replacement. Without-replacement sampling means that a unit cannot be selected more than once.

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, ...

In stratified random sampling, one splits the population into non-overlapping groups (e.g., under 30 years of age, 30 years and over) and then uses systematic or simple random sampling to select ...

Although simple random sampling is the standard sampling procedure in Monte Carlo simulation, such practice is questioned in this paper. In any Monte Carlo application, sampled distributions are ...

The derivations are based on a direct use of the statistical properties of the sampling errors in the second stage. For the ease of exposition we examine the specific case that simple random sampling ...

They concluded that systematic or stratified random sampling patterns are more effective than simple random sampling for bulk powder testing.

When observations are costly or time-consuming but the ranking of the observations without actual measurement can be done relatively easily, rankedset sampling (RSS) can be employed instead of simple ...