Statistical sampling — Edexcel A-Level Mathematics
Test yourself on Statistical sampling with PEARSON EDEXCEL A-Level practice questions.
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Statistical sampling explained
A population is the whole set of individuals or items of interest, while a sample is a subset selected from it.
Read the full explanation
Because measuring a whole population is often impractical, we use a sample to make informal inferences about the population, accepting uncertainty. Simple random sampling gives every member a known, equal chance of selection, often via random numbers or a random generator, reducing bias. Opportunity sampling uses whoever is conveniently available, which is quick but prone to bias. Selecting or critiquing a technique means judging whether it suits the context, and recognising that different samples can produce different conclusions, so sample size and method affect reliability.
Your focus
- Define population and sample correctly in a given context.
- Describe and apply simple random sampling and opportunity sampling.
- Evaluate a sampling technique and explain how different samples can lead to different conclusions.
Statistical sampling exam tips
Marking Points
- Defines population as the entire group of interest and sample as a subset drawn from it, using the context correctly.
- Describes simple random sampling accurately, including that every member has an equal chance of selection and a random mechanism is used.
- Describes opportunity sampling as selecting readily available individuals and identifies its convenience and bias risk.
- Makes an informal inference about the population from sample data while acknowledging uncertainty.
- Critiques or selects a sampling method for a given context, explaining how bias or sample variation could change conclusions.
Examiner Tips
- 💡Name the population and the sampling frame explicitly in the context of the question.
- 💡Justify a chosen method by linking it to reduced bias or practicality, not just by naming it.
- 💡When critiquing, state the direction of likely bias and how it could affect the conclusion.
Common Mistakes
- Confusing the sample with the population; correct this by naming the full group as the population and the selected subset as the sample.
- Describing opportunity sampling as random; correct this by stating it selects convenient individuals and is not random.
- Treating a sample result as an exact population value; correct this by presenting it as an estimate with acknowledged uncertainty.