Stratified Sampling | An Effective Method for Accurate Population Representation and Reduced Bias

stratified

In statistics, stratified refers to the process of dividing a population into distinct subgroups or strata based on certain characteristics or variables

In statistics, stratified refers to the process of dividing a population into distinct subgroups or strata based on certain characteristics or variables. This is done to ensure that the sample represents the diversity and heterogeneity within the population more accurately.

Stratified sampling is a sampling method that is commonly used in research and surveys. It involves randomly selecting samples from each stratum in proportion to its size or importance in the population. This approach helps to ensure that each subgroup is adequately represented in the sample, minimizing bias and providing a more accurate estimate of the population parameters.

Stratified sampling can be particularly useful when the population exhibits significant variability or when there are important subgroups that need to be specifically studied. By dividing the population into strata and selecting samples from each subgroup, researchers can obtain more reliable and precise results.

For example, let’s say a researcher wants to conduct a survey on the job satisfaction levels of employees in a company that has various departments such as marketing, finance, and operations. By using stratified sampling, the researcher can randomly select a proportionate number of employees from each department, ensuring that the sample represents the diversity of job roles within the company. This approach can help to obtain more accurate insights into the job satisfaction levels of different employee groups within the organization.

Overall, stratified sampling is a useful and effective technique to ensure representativeness and reduce sampling bias when dealing with diverse populations. It allows researchers to capture the nuances and variations within a population, leading to more accurate statistical analysis and findings.

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