Lesson 6 of 23 · All lessons in this module
  1. The central limit theorem
  2. The standard error of the sample meanVideo lesson
  3. Confidence intervals for the mean
  4. Value at risk: a one-sided confidence interval
  5. Populations, samples and sampling error
  6. Stratified and cluster sampling
  7. Good estimators, and the CLT across sampling designs
  8. Convenience and judgmental sampling, and choosing a method
  9. The four steps and stating the hypotheses
  10. Type I and Type II errors, significance and powerVideo lesson
  11. Critical values, p-values and the decision ruleVideo lesson
  12. p-values and p-hacking
  13. Testing a single mean
  14. Two means: independent vs paired samplesVideo lesson
  15. Testing variances: chi-square and FVideo lesson
  16. The t-test for a Pearson correlation
  17. Sample size, p-values and many correlations at once
  18. Parametric vs nonparametric tests
  19. The Spearman rank correlation testVideo lesson
  20. Contingency tables and the chi-square test of independenceVideo lesson
  21. Interpreting the result: standardized residuals and mosaics
  22. Choosing the test: four steps and a map of every test
  23. Integrated cases: testing whether a new strategy beats the old one

This lesson is part of the 2027 curriculum. You are following the 2026 curriculum, where it is not taught in this form. Switch if you are sitting the exam under the 2027 curriculum.

Lesson 6 of 23 · 14 min

Stratified and cluster sampling

Stratified sampling draws randomly from every subgroup to guarantee representation and precision; cluster sampling picks whole subgroups to save time and money.

In short

  • Stratified random sampling: divide the population into strata by one or more criteria, draw a simple random sample from each stratum in proportion to its size, then pool the subsamples.
  • It guarantees that every subgroup of interest is represented and gives more precise estimates (smaller variance) than simple random sampling of the same size.
  • Classic use: bond indexing. Cells are formed by duration, cash-flow pattern, sector, credit quality and call exposure. Number of cells = product of the categories, and each cell needs at least one bond.
  • Cluster sampling: divide the population into clusters that are each a mini-version of the population, then randomly choose whole clusters. One-stage: include every member of the chosen clusters. Two-stage: randomly subsample within them.
  • Cluster sampling is the most time- and cost-efficient probability method for a vast population, but for the same sample size it is usually less accurate.

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