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In (Simple) random sampling, all of the units in the sample have the same chance of being included in the sample. Units are selected randomly from a population by some random method that gives equal probability to each element.

In stratified random sampling, the entire population is divided into heterogeneous sub-popuation known as strata (sub-population with unequal variances) and a random sample is chosen from each of these stratum.

The reason when to use which depends on the situation and need of the experimenter.

Q: What is the difference between stratified an random sampling?

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cheese

Simple!

They include: Simple random sampling, Systematic sampling, Stratified sampling, Quota sampling, and Cluster sampling.

simple random, stratified sampling, cluster sampling

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Related questions

In a stratified sample, the sampling proportion is the same for each stratum. In a random sample it should be but, due to randomness, need not be.

yes

No.

cheese

stratified random sampling is a sample(strata) that a same and hemogenieous in group and that a different and heterogenious in group

a

There are many such methods: cluster sampling, stratified random sampling, simple random sampling.Their usefulness depends on the circumstances.

Simple!

Basically in a stratified sampling procedure, the population is first partitioned into disjoint classes (the strata) which together are exhaustive. Thus each population element should be within one and only one stratum. Then a simple random sample is taken from each stratum, the sampling effort may either be a proportional allocation (each simple random sample would contain an amount of variates from a stratum which is proportional to the size of that stratum) or according to optimal allocation, where the target is to have a final sample with the minimum variabilty possible. The main difference between stratified and cluster sampling is that in stratified sampling all the strata need to be sampled. In cluster sampling one proceeds by first selecting a number of clusters at random and then sampling each cluster or conduct a census of each cluster. But usually not all clusters would be included.

ang hirap!

Simple Random Sample Stratified Random Sampling Cluster Sampling Systematic Sampling Convenience Sampling

Basically in a stratified sampling procedure, the population is first partitioned into disjoint classes (the strata) which together are exhaustive. Thus each population element should be within one and only one stratum. Then a simple random sample is taken from each stratum, the sampling effort may either be a proportional allocation (each simple random sample would contain an amount of variates from a stratum which is proportional to the size of that stratum) or according to optimal allocation, where the target is to have a final sample with the minimum variabilty possible. The main difference between stratified and cluster sampling is that in stratified sampling all the strata need to be sampled. In cluster sampling one proceeds by first selecting a number of clusters at random and then sampling each cluster or conduct a census of each cluster. But usually not all clusters would be included.