Advantages of stratified sampling

What is Stratified sampling?

Stratified sampling is a sampling technique in which the population is subdivided into groups based on specific characteristics relevant to the problem before sampling. The samples are drawn from this group with ample sizes proportional to the size of the subgroup in the population and combined to form the final sample. The purpose is to ensure that all subgroup is represented proportionally in the final sample....

When to use stratified sampling?

Population Heterogeneity: When the population can be divided into mutually exclusive subgroups based on a specific characteristic. Equal Representation: When we want to ensure a specific characteristics or group of characteristics is adequately represented in the final sample. Resource Constraints: When you want to generalize the study results to the entire population and ensure that the estimates are valid for each stratum, but the resources are limited....

Advantages of stratified sampling

Precision: By ensuring representation from all relevant subgroups, stratified sampling often provides more precise and reliable estimates for the entire population. Generalization: Results from the sample can be more confidently generalized to the entire population because each subgroup is accounted for in the sampling process. Reduced Variability: Stratified sampling can reduce the overall variability in the sample, leading to more accurate and meaningful statistical analyses....

Comparison started with other Sampling Techniques

Stratified sampling is just one of several sampling techniques used in research. Let’s compare stratified sampling with a few other common sampling techniques:...

Implementing Stratified Sampling

Let us load the iris dataset to implement stratified sampling....

Conclusion

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Frequently Asked Questions (FAQs)

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