# Stratified random sampling differs from Cluster-random sampling

### September 26, 2019

Stratified random sampling differs from Cluster-random sampling
In stratified sampling, the sampling is done on elements within each stratum. In stratified sampling, a random sample is drawn from each of the strata, whereas in cluster sampling only the selected clusters are sampled. A common motivation of cluster sampling is to reduce costs by increasing sampling efficiency. Cluster Sampling and Stratified Sampling are probability sampling techniques with different approaches to create and analyze samples.
Description of Cluster Sampling
Cluster sampling is a sampling plan used when mutually homogeneous yet internally heterogeneous groupings are evident in a statistical population. It is often used in marketing research. In this sampling plan, the total population is divided into these groups and a simple random sample of the groups is selected.
Cluster Sampling is a method where the target population is divided into multiple clusters. Some of these clusters are selected randomly for sampling or a second stage or multiple stage sampling is carried out to form the target sample. Depending on the number of steps followed to create the desired sample, cluster sampling is divided using a single-stage, two-stage or multiple-stage sampling techniques. This sampling method is extremely cost-effective as it requires minimum efforts in sample creation and also convenient to execute.
Stratified Sampling
In statistics, stratified sampling is a method of sampling from a population which can be partitioned into subpopulations. In statistical surveys, when subpopulations within an overall population vary, it could be advantageous to sample each subpopulation independently. Stratified Sampling is a probability sampling method, also called random quota sampling, where a large population is divided into unique, homogeneous strata and further, members from these strata are randomly selected to form a sample. Elements of each of the samples will be distinct which will give the entire population an equal opportunity to be a part of these samples. Segregation based on age, religion, nationality, socioeconomic backgrounds, qualifications etc. can be done using this sampling technique.
Cluster Sampling – Key Points:
Naturally, existing groups are chosen to be a part of the final sample set.
Mainly used in market research, in this technique, a population is divided into clusters and these clusters are randomly chosen to be a part of the sample.
Information can also be collected from elements selected from each of the sub-clusters.
This method is usually applied in groups where there is diversity within the groups and not between clusters.
The only prerequisite is that all the clusters should be distinctive and non-overlapping.
Stratified Sampling – Key Points:
A population is divided into strata by random selection.
The simplest explanation of strata is a group of members of a population.
Simple random sampling is then performed on these strata to form samples.
One similarity that stratified sampling has with cluster sampling is that the strat formed should also be distinctive and non-overlapping.
By making sure each stratum is distinctive, the errors in results are drastically reduced.