Statistical Sampling (AS Unit 2: Applied Mathematics A) — WJEC A-Level Mathematics
Test yourself on Statistical Sampling (AS Unit 2: Applied Mathematics A) with WJEC A-Level practice questions.
7 days Premium · Then free forever · No card, no charge
Statistical Sampling (AS Unit 2: Applied Mathematics A) explained
Calculated sample statistics serve as point estimates for unknown population parameters.
Read the full explanation
Informal inference involves examining sample characteristics—such as the sample proportion, central tendency, or dispersion—and making reasoned deductions about the wider population while acknowledging the presence of sampling uncertainty. For example, finding that 35% of a random sample of voters support an initiative allows an informal estimate that roughly 35% of the electorate does so. Candidates must recognize how sample size governs precision: larger samples reduce sampling variability, producing more reliable informal estimates.
Your focus
- Estimate unknown population parameters using sample proportions and sample means.
- Explain how sample size influences the variability and reliability of informal inferences.
- Formulate cautious, evidence-based statements about a population from sample data.
Statistical Sampling (AS Unit 2: Applied Mathematics A) exam tips
Marking Points
- calculating the relevant sample statistic such as a sample mean or sample proportion
- stating an informal inference about the corresponding population parameter
- explicitly acknowledging sampling variability or uncertainty in the inferred conclusion
Examiner Tips
- 💡Use cautious phrasing such as 'the evidence suggests' or 'it is estimated that' rather than claiming proof.
- 💡Check whether sample selection was unbiased before generalising findings to the wider population.
Common Mistakes
- stating sample conclusions as absolute certainties about the population rather than tentative estimates
- ignoring the role of sample size when evaluating the reliability of an informal inference