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🎲intro to statistics review

key term - Cluster Sampling

Citation:

Definition

Cluster sampling is a probability sampling technique in which the population is divided into distinct groups, called clusters, and a random sample of these clusters is selected for inclusion in the study. The members within the selected clusters are then surveyed or observed, rather than selecting individual members from the entire population.

5 Must Know Facts For Your Next Test

  1. Cluster sampling is often used when the population is geographically dispersed or when a complete list of all members is not available.
  2. Cluster sampling can be more cost-effective and efficient than other probability sampling methods, especially when the population is spread out over a large area.
  3. The accuracy of cluster sampling depends on the homogeneity within the clusters and the heterogeneity between the clusters.
  4. Cluster sampling can be used in both experimental and observational studies, such as data collection experiments and sampling experiments.
  5. Cluster sampling can be used to estimate probabilities of independent and mutually exclusive events, as the selected clusters can be treated as independent units.

Review Questions

  • Explain how cluster sampling relates to the concept of data, sampling, and variation in data and sampling.
    • Cluster sampling is a probability sampling technique that is used to collect data from a population. It helps address the issue of variation in data and sampling by dividing the population into distinct groups or clusters, and then randomly selecting a sample of these clusters to study. This approach can reduce the variability within the sample, as the members within each selected cluster tend to be more homogeneous. Additionally, cluster sampling can be useful when the population is geographically dispersed, as it allows for more efficient data collection compared to other sampling methods.
  • Describe how cluster sampling can be used in the context of data collection experiments.
    • In data collection experiments, cluster sampling can be employed to select the experimental units. For example, if the experiment involves studying the effects of a new educational program on student performance, the researcher could divide the schools in the population into clusters (e.g., by geographic location or socioeconomic status), and then randomly select a sample of these school clusters to participate in the experiment. This approach can be more practical and cost-effective than selecting individual students from the entire population, while still maintaining the benefits of probability sampling.
  • Analyze how cluster sampling can be used to estimate probabilities of independent and mutually exclusive events.
    • In the context of sampling experiments and the study of independent and mutually exclusive events, cluster sampling can be a useful technique. By randomly selecting clusters of individuals or units, the researcher can treat the selected clusters as independent units when estimating probabilities. This is because the members within each cluster are more likely to be homogeneous, while the clusters themselves are more likely to be heterogeneous. This property of cluster sampling allows the researcher to make inferences about the probabilities of independent and mutually exclusive events occurring within the population, based on the data collected from the sampled clusters.