Pearson Edexcel · A-Level · Mathematics
Course version: 9MA0 · Version from Mar 2026
Understand and use the terms ‘population’ and ‘sample’. Use samples to…
Full specification wording
Statistical sampling: 1.1 Understand and use the terms ‘population’ and ‘sample’. Use samples to make informal inferences about the population. Understand and use sampling techniques, including simple random sampling and opportunity sampling. Select or critique sampling techniques in the context of solving a statistical problem, including understanding that different samples can lead to different conclusions about the population.
A population is the whole set of individuals or items of interest, while a sample is a subset selected from it.
Checked against the Pearson Edexcel 9MA0 specification (opens in a new tab) on .
- 4 key terms
Study notes
From the checked teaching notes for this specification point.
The idea
A population is the whole set of individuals or items of interest, while a sample is a subset selected from it. 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.
What earns the marks
- 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.
Common mistakes and how to put them right
Confusing the sample with the population.
Put it right by naming the full group as the population and the selected subset as the sample.
Describing opportunity sampling as random.
Put it right by stating it selects convenient individuals and is not random.
Treating a sample result as an exact population value.
Put it right by presenting it as an estimate with acknowledged uncertainty.
Exam 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.
Try the lesson
The start of the Statistical sampling: 1.1 Understand and use the terms ‘population’ and ‘sample’. Use samples to make informal inferences about the population. Understand and use sampling techniques, including simple random sampling and opportunity sampling. Select or critique sampling techniques in the context of solving a statistical problem, including understanding that different samples can lead to different conclusions about the population. lesson, made from checked teaching notes for this specification point.
Have a go first
A researcher wants to estimate the mean height of all 800 students in a college. She measures the heights of 50 students chosen at random from the college register. In this study, which group is the population?
Listen
A population is the whole set of individuals or items of interest, while a sample is a subset selected from it. 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. A strong answer earns marks for these 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. A common slip: Confusing the sample with the population. Instead, naming the full group as the population and the selected subset as the sample. Exam tip: Name the population and the sampling frame explicitly in the context of the question.
Key words
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Population
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Sample
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Simple random sampling
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Opportunity sampling
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See every definition
- Population
- The whole set of individuals or items of interest, named in the context of the problem.
- Sample
- A subset selected from the population, used to make informal inferences about it.
- Simple random sampling
- A technique giving every member a known, equal chance of selection, often via random numbers or a random generator, reducing bias.
- Opportunity sampling
- Selecting whoever is conveniently available, which is quick but prone to bias.
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Test your understanding of Statistical sampling: 1.1 Understand and use the terms ‘population’ and ‘sample’. Use samples to make informal inferences about the population. Understand and use sampling techniques, including simple random sampling and opportunity sampling. Select or critique sampling techniques in the context of solving a statistical problem, including understanding that different samples can lead to different conclusions about the population.
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