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.

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

In a simple random sample, each individual in the population has an equal probability of being chosen. Additionally, each sample of size n has an equal probability of being the chosen sample. This ...

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.

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 ...

The case for the central limit theorem for the sample mean from finite populations under simple random sample without replacement, the parallel to the simplest case in the standard framework, is not ...

True experiments, unlike anecdotal evidence, often require random sampling and random assignment. In this post, I try to explain the importance of random sampling; in my next post, I will explore ...

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 ...

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.