Improving Data Accuracy: A Guide To Optimal Sampling With Stratified Random Sampling Technique

stratified random sampling

Population divided into subgroups (strata) and random samples taken from each strata

Stratified random sampling is a sampling technique used in statistics where the population is divided into multiple subgroups or strata based on certain characteristics (such as age, income, gender, etc.). Then, a random sample is drawn from each stratum to constitute the final sample.

This sampling method is used to ensure that the sample is representative of the population and that each stratum is proportionally represented in the sample. By dividing the population into strata, the variation within the subgroups is reduced, meaning that the sample is more likely to accurately reflect the population.

The steps involved in Stratified random sampling are:

1. Identifying the population: The first step involves identifying the population of interest to be studied.

2. Dividing the population into strata: Next, the population is divided into strata or subgroups based on relevant characteristics.

3. Determining the sample size: The sample size for each stratum is determined based on the proportion of the population represented by each stratum.

4. Drawing samples from each stratum: A random sample is drawn from each stratum to constitute the final sample.

By using stratified random sampling, a sample that accurately represents different segments of the population can be obtained, which can improve the accuracy and reliability of statistical analysis.

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