Use samples carefully
Explain why a business samples a population and evaluate representativeness, bias, sample size and practical limits.
Cambridge International AS & A Level Business 9609, 2026–2028 syllabus and 2029 syllabus, section 3.2.3. Cambridge states that the 2029 syllabus has no significant changes affecting teaching. This lesson maps only its named section; other sections and full paper practice remain separate. Written practice is self-checked
Before you begin
- No previous business course is assumed. Use the lesson's definitions before applying them to the business context.
Work on paper. Try each question before revealing help, and explain why your method works.
Quick readiness check
Why do businesses use samples?
What you will learn
- Explain why sampling is needed
- Identify limitations and sources of bias
- Improve a sample design for a decision
A sample makes research manageable
A population is the full group a business wants to understand. Sampling selects some members to collect evidence without contacting everyone. It can save time and cost and make research feasible.
A sample is useful only if it provides evidence relevant to the population and question. A large sample may still be biased if it excludes important groups or over-represents people who are easiest to contact.
Design matters more than size alone
Convenience samples are quick but can misrepresent the target group. A random sample gives eligible members a chance of selection; stratified sampling can ensure relevant subgroups are represented. These methods still require a sound population list and suitable response.
Non-response, unclear questions, small subgroups and changing customer behaviour can limit results. State who was eligible, how participants were selected and who did not respond. Use cautious language and seek additional evidence when the decision is high-cost.
Pause and explain
A survey only reaches customers who follow the firm's social account. What is a likely problem?
Worked example
A gym asks the first 20 morning visitors whether opening hours are convenient. Explain one limitation and improve the sample.
Show the worked solution
- Morning visitors exclude people who attend at other times or do not attend.
- The responses may overstate satisfaction with morning hours.
- Sample members across times and include potential customers.
Answer The sample is a convenience group of morning members, so it may not represent evening members or non-members. The gym could sample across different days and visit times and include local potential customers, while reporting who responded.
Try a topic test
Choose Easy, Normal or Difficult. Save your answers and open worked review after finishing.
Test this topic →From guided practice to a new situation
Write your answer and reasoning first. Use a hint only if you are stuck. The model solution is for self-checking; your written work is not automatically marked.
A college surveys 40 students from one course about a new campus café. Explain why this may not represent the student population and suggest a better design.
Give me a hint
Compare the chosen group with the population whose needs matter.
Compare my reasoning
- One course may have distinctive schedules or preferences.
- Include students from relevant courses and year groups.
- Select across time or groups and record who responds.
Students on one course may share schedules or characteristics that differ from the rest of the college. The college could sample across courses and year groups, invite responses at different times and compare response patterns, while avoiding a claim that non-respondents agree.
Look for these in your work
- Population mismatch is explained
- Broader design is concrete
- Non-response is acknowledged
A retailer surveys 800 app users about a new mobile feature. Explain why the large sample may still mislead a decision about all customers.
Give me a hint
Sample size cannot correct systematic exclusion.
Compare my reasoning
- App users may differ from customers who shop in-store only.
- The survey measures users who can access the feature.
- Add evidence from other customer groups and actual trial use.
The 800 respondents may be numerous but still exclude customers who do not use the app, so the sample is not necessarily representative of all customers. The retailer should include other shopping channels and test actual use, not rely only on stated interest.
Look for these in your work
- Size and representation are distinguished
- Excluded group is named
- Complementary evidence is proposed
A city authority samples households about a new bus route. Recommend two design choices and one limitation to report.
Give me a hint
Specify the affected population and make groups with different access visible.
Compare my reasoning
- Sample areas near and farther from the route.
- Include different travel times or household types using a clear selection method.
- Report non-response and that stated travel intentions may differ from actual use.
It could sample households in affected and comparison areas, using a clear method across relevant travel times and household groups. It should report response rates and note that stated intention may differ from actual ridership once the route opens.
Look for these in your work
- Relevant subgroups are covered
- Selection method is addressed
- A limitation is reported
Common mistakes
- Assuming a bigger sample automatically removes bias
- Generalising from existing customers to all potential customers
- Leaving the population and selection method undefined
What can you explain now?
Why do businesses use samples?
Compare with the explanation
To gather manageable evidence about a larger population
Sampling can reduce the time and cost of contacting everyone.
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Make it stick
Why is sampling useful, and what evidence helps judge whether its findings represent the intended population?
If you needed the solution, close it and solve the problem again from a blank page. A correct answer is stronger when you can explain the reason for each step.