There are, however, some potential drawbacks to using stratified sampling.

In such situations, usually stratified sampling will be done at some stages.

A real-world example of using stratified sampling would be for a political survey.

However the main advantage remains stratified sampling being the most representative of a population.

In stratified sampling, the analysis is done on elements within strata.

This contrasts with stratified sampling where the main objective is to increase precision.

In statistics, stratified sampling is a method of sampling from a population.

Quota sampling is like stratified sampling, but with an important variation.

If these conditions are not true, stratified sampling or cluster sampling may be a better choice.

If, on the other hand, a frame contains additional information about the population members, stratified sampling can improve both external validity and precision.

In stratified sampling, subsets of the population are created so that each subset has a common characteristic, such as gender.

Quota sampling is the non probability version of stratified sampling.

Oversampling Choice-based sampling is one of the stratified sampling strategies.

For this reason, benchmarking is generally used in situations where stratified sampling is impractical.

A solution to this problem is to use an alternate design strategy, e.g. stratified sampling.

Uniform sampling, stratified sampling and importance sampling are the most common.

The stratified sampling proposed by Kitagawa (1996) is optimal in terms of variance.

This is called stratified sampling.

Proportionate stratified sampling involves selecting participants from each strata in proportions that match the general population.

The advantage of stratified sampling is that the sample numbers in each stratum can be controlled for desired accuracy outcomes.

Clustered sampling usually decreases the precision of the statistics as compared to stratified sampling.

Data representing each subgroup are taken to be of equal importance if suspected variation among them warrants stratified sampling.

While it is usually best to sample randomly, concern with differences between specific subpopulations sometimes calls for stratified sampling.

Although cluster sampling and stratified sampling bear some superficial similarities, they are substantially different.

Stratified sampling works slightly differently.