Stratified sampling

Definition

Stratified sampling is a method of sampling in which the population is divided into distinct subgroups, or strata, that share similar characteristics, and samples are drawn from each stratum. This approach ensures that all relevant subgroups are represented in the sample, leading to more accurate and generalizable results in research. By focusing on specific segments of the population, stratified sampling helps reduce sampling bias and increases the reliability of the conclusions drawn from data analysis.

5 Must Know Facts For Your Next Test

1. Stratified sampling enhances representativeness by ensuring each subgroup within the population is adequately represented in the sample.
2. This method can lead to greater precision in estimates compared to simple random sampling, especially when there are significant differences between strata.
3. Researchers need to determine how many samples to take from each stratum, which can be proportional (based on size) or equal (the same number from each stratum).
4. Stratified sampling is particularly useful in studies where specific subgroups are of interest, such as demographic or behavioral characteristics.
5. It helps improve the validity and reliability of findings, making it easier to generalize results back to the larger population.

Review Questions

• How does stratified sampling contribute to reducing bias in research studies?
  • Stratified sampling contributes to reducing bias by ensuring that all relevant subgroups within a population are represented in the sample. By dividing the population into strata based on shared characteristics and then sampling from each stratum, researchers can capture the diversity within the population. This method minimizes the risk of excluding certain groups that could skew results, leading to more accurate and generalizable findings.
• Discuss the advantages of using stratified sampling over simple random sampling when conducting research.
  • The advantages of using stratified sampling over simple random sampling include increased representativeness and precision. While simple random sampling treats every member equally, stratified sampling specifically targets different subgroups, which helps to ensure that significant segments of the population are included. This targeted approach often leads to more reliable data and insights, especially when studying populations with diverse characteristics where differences may affect outcomes.
• Evaluate how stratified sampling can impact the overall validity of a study's conclusions and why it's crucial in the context of AI and machine learning validation.
  • Stratified sampling can significantly enhance the overall validity of a study's conclusions by ensuring that data reflects the true diversity present in the target population. In AI and machine learning validation, this is crucial because models trained on data that doesn't accurately represent all relevant subgroups may perform poorly when applied in real-world scenarios. By using stratified sampling to create balanced datasets, researchers can improve model robustness and ensure that predictions made by AI systems are reliable across different segments of users or conditions.

Related terms

Population: The complete set of individuals or items that is the subject of a statistical analysis.

Sample: A subset of individuals or items selected from a larger population for the purpose of conducting research.

Sampling bias: A systematic error that occurs when certain members of a population are less likely to be included in a sample, potentially skewing results.