Lesson 8 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 8 of 23 · 12 min

Convenience and judgmental sampling, and choosing a method

Non-probability samples are chosen by convenience or expert judgment: quick and cheap, but at real risk of not representing the population.

In short

  • Non-probability methods rely on the researcher's selection, not on a fixed random process, so not every member has an equal chance of being chosen.
  • Convenience sampling: use what is easiest to access. Fast and low cost, ideal for a pilot study, but the sample may not represent the population, so accuracy can be limited.
  • Judgmental sampling: an expert handpicks members. Useful under time pressure or when expertise is critical (e.g. auditing), but exposed to the researcher's bias.
  • Probability methods let chance alone drive sampling error; non-probability methods add the risk of a systematically unrepresentative sample.
  • Match the method to the goal: inference about a population → probability sampling; index replication → stratified; vast population on a budget → cluster; quick first look → convenience.

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