Statistics Foundation · Lesson 1.5
Sampling methods.
Sampling methods decide whether the data can fairly represent the population. This lesson teaches students how to choose a sampling method, compare probability and non-probability designs, recognise bias, and write cautious conclusions about how far a sample can be generalised.
90–100 minute lesson plan
Learn how sampling controls the strength of evidence.
In earlier lessons, you learned what populations, samples and variables are. This lesson explains how the sample is selected. The method matters because it affects bias, uncertainty and the confidence we can place in a conclusion.
0–10 min
Why sampling methods matter
Understand why the way a sample is selected can affect bias, uncertainty and the strength of a conclusion.
10–30 min
Probability sampling
Learn simple random, systematic, stratified and cluster sampling.
30–45 min
Non-probability sampling
Understand convenience, voluntary response, quota and snowball sampling, including their limitations.
45–65 min
Bias and representativeness
Explore undercoverage, selection bias, non-response and why large samples can still mislead.
65–85 min
Interactive sampling studio
Adjust population structure, sample size and sampling method to see how the selected sample changes.
85–100 min
Worked examples and quiz
Choose sampling methods for realistic education, health and survey scenarios.
Mastery checklist
By the end, you should be able to choose and critique a sampling plan.
Explain why sampling method matters.
Distinguish probability and non-probability sampling.
Compare simple random, systematic, stratified and cluster sampling.
Explain convenience and voluntary response limitations.
Identify undercoverage, selection bias and non-response bias.
Write a cautious conclusion about sample quality.