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.

A simple random sample is used to represent the entire data population. A stratified random sample divides the population into smaller groups based on shared characteristics.

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 ...