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Management Accounting · Sampling methods

Non-Random Sampling Methods: Quota, Cluster and Convenience

Updated 11 October 2026 · Fact-checked

Non-random sampling means items are not all given a known chance of selection, so the sample may be biased. Quota sampling fills set numbers from groups, convenience sampling uses whoever is easy to reach, and cluster sampling picks whole groups. Know how each works, why it is used and where bias enters.

Understand Non-Random Sampling Methods

A population is everything you want to learn about, such as all customers or all invoices. A sample is the part of it you actually examine. Sampling saves time and cost, because checking every item is often impractical.

In random sampling every item has a known chance of being chosen. This lets you measure how reliable the result is. In non-random sampling selection depends on judgement or convenience. Some items may have no chance of being picked, so the sample may not represent the population. That risk is bias.

Quota sampling splits the population into groups, such as age bands or gender. The researcher is told how many people to find in each group. The interviewer then picks anyone who fits until each quota is full. It is quick and cheap and gives a sample with the right mix. But the interviewer chooses who to ask, so people who are easy to approach are over-represented.

Convenience sampling means choosing the items that are easiest to reach, such as customers in the shop today or the first 20 files in the cabinet. It is the cheapest and fastest method. It is also the most likely to be biased, so it suits only quick, rough or pilot work.

Cluster sampling divides the population into natural groups, called clusters, such as branches, streets or regions. You select some clusters and then study all items, or a sample of items, within them. It cuts travel and cost when the population is spread out. It can be biased if the chosen clusters are not typical of the whole. Be careful: in practice, clusters can be chosen at random, and then the method has a random element. ACCA-style questions usually treat it as a method of convenience and cost, so read the wording of the question.

Key formulas to remember

Random vs non-random
Random: every item has a known chance of selection. Non-random: selection by judgement or convenience
Only random methods let you measure sampling error with statistics.
Quota sampling
Quota for a group = group share of population × total sample size
Example: 40% of the population and a sample of 200 gives a quota of 80.
Cluster sampling
Select clusters, then study items within the selected clusters
Saves cost when the population is spread out; the clusters should be typical.
Convenience sampling
Select the items easiest to reach
Cheapest and quickest, highest risk of bias.

How to solve Non-Random Sampling Methods questions

    Quickest way: Keyword matching

    When to use it: Use this for one- or two-mark objective questions where you must name or describe a method.

    1. Look for the key phrase: quota or categories means quota; easiest or nearest means convenience; areas, branches or groups means cluster.
    2. Remember that all three are cheap and quick, and the cost saving is the usual reason for using them.
    3. Remember the main weakness: bias, and no statistical measure of error for the non-random ones.
    4. Eliminate options that describe random methods such as simple random or systematic selection.

    Common mistakes in Non-Random Sampling Methods

    • Confusing quota sampling with stratified random sampling.

      Both split the population into groups.

      Fix: In stratified sampling items are picked at random within each group. In quota sampling the interviewer picks freely until the quota is full, so it is non-random.

    • Saying cluster sampling studies a few items from every group.

      It is mixed up with stratified sampling.

      Fix: Cluster sampling selects some groups and studies items within those groups only. Stratified sampling uses every group.

    • Claiming convenience sampling is representative.

      Students assume any large sample is fair.

      Fix: Size does not remove bias. Convenience sampling reflects who was easy to reach, not the population.

    • Saying non-random sampling is never useful.

      Bias is overemphasised.

      Fix: Say it is useful when cost and speed matter, or when no sampling frame exists, but results may be unreliable.

    • Getting quota numbers wrong.

      Using the sample size where the population share is needed.

      Fix: Quota = group percentage × total sample size. Check that the quotas add up to the sample size.

    Worked examples

    Example 1

    A market researcher must interview 250 shoppers. The shoppers using the store are 60% female and 40% male. The researcher uses quota sampling by gender. How many of each should be interviewed, and why might the result still be biased?

    Show the solution
    1. Female quota = 60% × 250 = 150.
    2. Male quota = 40% × 250 = 100.
    3. Check: 150 + 100 = 250.
    4. Bias: the interviewer chooses who to approach within each quota, so approachable people may be over-represented.

    Answer: Interview 150 females and 100 males. The result may be biased because selection within each quota is not random.

    Example 2

    A company has 40 branches across a country. To review staff morale, the manager picks the 4 branches nearest head office for convenience and surveys all staff in those branches. Name the method, say whether the selection is random, and give one advantage and one disadvantage.

    Show the solution
    1. The population is grouped into branches, and whole groups are selected, so this is cluster sampling.
    2. The manager chose the branches for convenience, not at random, so here the selection is non-random. If the 4 branches had been chosen at random, it would be a random method.
    3. Advantage: surveying only 4 branches cuts travel and administration cost.
    4. Disadvantage: the 4 branches may not be typical of all 40, which can bias the result. The risk is greatest when clusters are picked for convenience.

    Answer: Cluster sampling, with the clusters chosen by convenience, so non-random. Advantage: lower cost. Disadvantage: the chosen branches may not be typical, so the result may be biased.

    Exam tips

    • Use the scenario clue to name the method before reading the options.
    • Know that quota and convenience are always non-random. Cluster sampling is random if the clusters are chosen randomly, and non-random only if they are picked by judgement or convenience. Read how the clusters were chosen.
    • In 'which is a disadvantage' questions, bias is the safest answer for non-random methods.
    • Do the quota calculation quickly and check that the quotas add up to the total sample.

    Practice questions from Sampling methods

    Non-Random Sampling Methods in other exams

    The same ground in other exams, if you are preparing for more than one or want another angle on it.

    Non-Random Sampling Methods: frequently asked questions

    What is the difference between random and non-random sampling?

    In random sampling every item has a known chance of being chosen. In non-random sampling selection is by judgement or convenience. Non-random samples are quicker and cheaper but more likely to be biased.

    What are the advantages and disadvantages of cluster sampling?

    It reduces cost and travel when the population is spread out. It can be biased if the chosen clusters are not typical of the whole population. It is a random method if the clusters are chosen randomly.

    What is a convenience sampling example?

    Surveying the first customers who walk into a shop on one afternoon is convenience sampling. So is checking the files that are nearest to hand.

    How does quota sampling differ from stratified sampling?

    Both split the population into groups. Stratified sampling selects items at random in each group, while quota sampling lets the interviewer choose until each quota is filled.