Stratified Random Sample

Definition

A stratified random sample is a sampling method where the population is divided into distinct subgroups, known as strata, that share similar characteristics. Then, a random sample is taken from each stratum to ensure that all relevant segments of the population are represented. This approach improves the accuracy of estimates and enhances the reliability of results in statistical analysis.

5 Must Know Facts For Your Next Test

1. Stratified random sampling helps to reduce variability in the sample and provides a more precise estimate of the population parameters.
2. Each stratum should be mutually exclusive and collectively exhaustive, meaning every member of the population must belong to one and only one stratum.
3. When using stratified sampling, researchers can apply different sampling techniques within each stratum, such as simple random sampling or systematic sampling.
4. Stratified random samples can improve the power of statistical tests by ensuring adequate representation from all subgroups, especially when certain groups are small.
5. In chi-square tests for homogeneity or independence, using a stratified random sample can lead to more reliable results when examining relationships between categorical variables.

Review Questions

• How does using a stratified random sample improve the accuracy of statistical estimates compared to simple random sampling?
  • Using a stratified random sample enhances accuracy by ensuring that all relevant subgroups within the population are represented. This minimizes bias and increases precision in estimates, particularly when certain subgroups may be underrepresented in a simple random sample. By carefully selecting samples from each stratum based on their proportions in the population, researchers can obtain more reliable insights into the overall population characteristics.
• Discuss the implications of using stratified random sampling when setting up a chi-square test for independence.
  • When conducting a chi-square test for independence, using stratified random sampling ensures that each category in the contingency table has sufficient data for analysis. This is crucial for validating the assumptions of the test and obtaining meaningful results. By controlling for different strata, researchers can examine how variables interact across various subgroups and make more informed conclusions about relationships within categorical data.
• Evaluate how stratified random sampling might affect the interpretation of results in research studies aimed at understanding societal issues.
  • Stratified random sampling plays a significant role in research aimed at societal issues by allowing for nuanced insights into diverse populations. By ensuring representation across different strata such as age, income, or ethnicity, researchers can analyze trends and relationships that might be overlooked with other sampling methods. The resulting data offers a richer understanding of how various factors intersect within society, leading to more effective policies and interventions based on comprehensive evidence.

Related terms

Population:

The entire group of individuals or instances about whom we hope to learn or draw conclusions.

Sample Size:

The number of observations or data points collected from a subset of the population for analysis.

Random Sampling:

A sampling technique where each member of the population has an equal chance of being selected, ensuring that the sample is representative.