Stratified samplingIt is a sampling method that first stratifies and then extracts. It is a commonly used sampling method in statistics.

Steps in stratified sampling

The surveyed population is divided into several sub-populations, called strata, based on certain measurement criteria. There are large differences between strata, but the individual differences within each stratum are not significant.

After distinguishing the layers, the required proportion of samples are drawn from each layer using simple random sampling, and the samples of each layer are combined to obtain the sample. The proportion of each layer drawn is the proportion of the layer in the population.

Advantages and disadvantages of stratified sampling

This ensures that the structure of the sample is close to that of the population, thereby improving the accuracy of the estimate.

Stratified sampling requires that the differences within the stratum be large, while the differences between the strata be small. Stratified sampling should not be used when the overall differences are not obvious.

Parent word: sampling

Synonyms: simple sampling, cluster sampling, systematic sampling, autonomous sampling