Stratified Sampling

Definition

Stratified sampling is a probability sampling technique where the population is divided into distinct subgroups or strata, and samples are randomly selected from each stratum in proportion to the stratum's size. This method ensures that the sample is representative of the overall population, allowing for more precise estimates and inferences.

5 Must Know Facts For Your Next Test

1. Stratified sampling is used to ensure that the sample is representative of the population, especially when the population is heterogeneous and can be divided into distinct subgroups.
2. The strata are formed based on one or more characteristics of the population, such as age, income, gender, or geographic location.
3. Stratified sampling can lead to more precise estimates and reduced sampling error compared to simple random sampling, as it ensures that each subgroup is proportionally represented in the sample.
4. The allocation of the sample size to each stratum can be done proportionally, disproportionally, or optimally, depending on the research objectives and the characteristics of the population.
5. Stratified sampling is particularly useful in experimental design and when studying finite populations, as it can help reduce the finite population correction factor.

Review Questions

• Explain how stratified sampling can be used to improve the representativeness of a sample in the context of data, sampling, and variation in data and sampling.
  • Stratified sampling is a powerful technique for ensuring that a sample is representative of the overall population, especially when the population is heterogeneous and can be divided into distinct subgroups. By dividing the population into strata based on relevant characteristics, such as age, income, or geographic location, and then randomly selecting samples from each stratum proportional to the stratum's size, stratified sampling can help reduce sampling error and provide more precise estimates of population parameters. This is particularly important in the context of data, sampling, and variation, as it helps to minimize the impact of population heterogeneity on the data collected and the conclusions drawn from the analysis.
• Discuss how stratified sampling can be used in the design of experiments and its relationship to ethical considerations in experimental design.
  • Stratified sampling is an important tool in the design of experiments, as it can help ensure that the sample is representative of the population and that the experimental results are generalizable. By dividing the population into relevant strata and then randomly selecting participants from each stratum, researchers can reduce the risk of bias and improve the validity of their findings. This is particularly important from an ethical standpoint, as it helps to ensure that the benefits and risks of the experiment are distributed fairly across the population, and that the results can be applied to the broader population. Additionally, stratified sampling can help researchers identify and address potential sources of heterogeneity in the population, which is crucial for understanding the underlying mechanisms and drawing meaningful conclusions from the experimental data.
• Explain how the use of stratified sampling can influence the finite population correction factor in the context of sampling from a finite population.
  • The finite population correction factor is a statistical adjustment used when sampling from a finite population, as opposed to an infinite population. Stratified sampling can influence the finite population correction factor in several ways. First, by dividing the population into distinct strata, stratified sampling can help reduce the overall population size, as each stratum is considered a separate, smaller population. This can lead to a larger finite population correction factor, which can in turn reduce the standard error of the sample estimates and improve the precision of the inferences drawn from the data. Additionally, the allocation of the sample size to each stratum can be optimized to further minimize the finite population correction factor and enhance the statistical power of the analysis. Overall, the use of stratified sampling in the context of finite populations can be a valuable strategy for improving the reliability and accuracy of the research findings.

Related terms

Simple Random Sampling: A basic probability sampling method where each member of the population has an equal chance of being selected for the sample.

Cluster Sampling: A probability sampling technique where the population is divided into clusters, and a random sample of clusters is selected, with all members within the selected clusters included in the final sample.

Systematic Sampling: A probability sampling method where the sample is selected by choosing every kth element from the population, starting from a random point.