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Learning objective overview
Master Teaching Guide coverage for this objective.
S6-1
NZC26-P4-STA-DKD-K-Y10-03NZC26-P4-STA-DKD-K-Y10-02
Resources
- CorbettMaths on Random SamplingQuestions PDF · Answers
- CorbettMaths on Stratified SamplingQuestions PDF · Answers
Terminology
- Sample
- Population
- Sampling methodology
- Simple random sampling
- Systematic sampling
- Stratified sampling
- Cluster sampling
- Convenience sampling
- Bias
- Representative sample
Task goals
- Describe simple random, systematic, stratified, cluster, and convenience sampling, and identify which method a described scenario or sampling-frame diagram uses.
- Explain at least one advantage and one disadvantage of each of the five sampling methods.
- Calculate the correct number to sample from each stratum in a stratified sample, in proportion to stratum size.
- Explain why convenience sampling is quick and cheap but likely to produce a biased, unrepresentative sample.
- Explain why cluster sampling is practical for spread-out populations but risks being unrepresentative if the chosen clusters differ from the rest of the population.
- Judge which sampling method is most appropriate for a given real-world scenario, and justify the choice.
Where this fits
What leads into this objective, and where it goes next.
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Sampling methodology
When sampling from a population, the distribution for a variable varies from sample to sample. To make a reliable inference about what is happening in the population, sample sizes need to be: about 1,000 for categorical variables (with the sample obtained using technology); at least 30 for numerical variables.
Statement▸
Samples must be taken randomly from the population, otherwise there will be bias in the data, leading to inaccurate and misleading statistics. Samples are ideally chosen using simple random sampling, in which each item of the population has an equal probability of being chosen.
Statement▸
Also under S6-1:
For teachers
Need the fuller teaching notes for this objective? Open the Teacher guidance PDF.