What is stratified sampling and why is it important?

What is stratified sampling and why is it important?

Stratified sampling is a method of sampling that involves dividing a population into subgroups, or strata, that share similar characteristics. It's important because it ensures that each subgroup is adequately represented in the sample, leading to more accurate and reliable results.

Can you give me an example of how it's used in research?

Sure! For instance, if a researcher wants to study the voting behavior of a population, they might stratify the sample by age, gender, and income level to ensure all segments are represented.

What are the advantages of using stratified sampling over simple random sampling?

The main advantages include increased precision and reduced variability in the estimates. It also allows for comparisons between different strata.

Are there any disadvantages to stratified sampling?

Yes, it can be more complex and time-consuming to implement. Additionally, it requires detailed knowledge of the population to create appropriate strata.

How do I determine the size of each stratum in my sample?

The size of each stratum can be determined based on the proportion of each subgroup in the population or by using equal allocation if you want to ensure each stratum is equally represented.