What is stratified random sampling?

Stratified random sampling is a sampling technique where the population is divided into subgroups and a random sample is taken from each subgroup.

Stratified random sampling is a method of sampling that involves dividing the population into subgroups or strata based on certain characteristics. The strata are then sampled randomly, with the aim of ensuring that the sample is representative of the population as a whole. This technique is often used when the population is heterogeneous, meaning that it contains subgroups that differ significantly from one another.

To carry out stratified random sampling, the first step is to identify the relevant strata. This might involve grouping the population by age, gender, income, or any other relevant characteristic. Once the strata have been identified, a random sample is taken from each stratum. The size of each sample is proportional to the size of the stratum, so that larger strata are represented by larger samples.

The advantage of stratified random sampling is that it can increase the precision of the sample by reducing the sampling error. This is because the sample is drawn from each stratum, which ensures that each subgroup is represented in the sample. This can be particularly useful when the subgroups have different characteristics or when the population is highly variable.

In summary, stratified random sampling is a useful technique for ensuring that a sample is representative of a population. By dividing the population into subgroups and sampling from each stratum, this method can increase the precision of the sample and reduce the sampling error.