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Study Notes

Sampling Techniques

AS, A-Level
AQA, Edexcel, OCR, IB

Last updated 22 Mar 2021

A population is an entire group with specified characteristics. The target group/population is the desired population subgroup to be studied, and therefore want research findings to generalise to. A target group is usually too large to study in its entirety, so sampling methods are used to choose a representative sample from the target group.

A representative sample is a subset of the target group with a similar distribution of relevant characteristics, in turn allowing us to generalise from the sample to the target group with some justification. An unrepresentative sample is one that does not reflect the distribution of characteristics of the target group, cannot be generalised to the target population, and is therefore biased.

There are a number of different sampling methods. Let's take a look at each briefly.

Random sampling

This method gives every member of the target group an equal chance of being selected for the sample (e.g. by assigning a number to each member, and then selecting from the pool at using a random number generator).


  • It is widely accepted that since each member has the same probability of being selected, there is a reasonable chance of achieving a representative sample.


  • Small minority groups within your target group may distort results, even with a random sampling technique.
  • It can be impractical (or not possible) to use a completely random technique, e.g. the target group may be too large to assign numbers to.

Systematic sampling

A systematic method is chosen for selecting from a target group, e.g. every fourth person in a list could be used in the sample. It differs from random sampling in that it does not give an equal chance of selection to each individual in the target group.


  • Assuming the list order has been randomised, this method offers an unbiased chance of gaining a representative sample.


  • If the list has been assembled in any other way, bias may be present. For example if every fourth person in the list was male, you would have only males in your sample.

Stratified Sampling

Here the sampler divides or 'stratifies' the target group into sections, each showing a key characteristic which should be present in the final sample. Then each of those sections is sampled individually. The sample thus created should contain members from each key characteristic in a proportion representative of the target population.


  • It avoids the problem of misrepresentation sometimes caused by purely random sampling.


  • It takes more time and resources to plan.
  • Care must be taken to ensure each key characteristic present in the population is selected across strata, otherwise this will design a biased sample.

Opportunity Sampling

Participants who are both accessible and willing to take part are targeted, e.g. employees from a conveniently located employer near the laboratory could be selected for the sample group.


  • This method is easy and inexpensive to carry out.


  • The consequent sample may not be representative as it could be subject to bias (e.g. the conveniently located employer may undertake a selection process for job applicants, making it likely that employees possess certain similar characteristics that are unrepresentative of the wider target group).

Volunteer Sampling

Here the sample consists of people who have volunteered to be in the study.


  • This often achieves a large sample size through reaching a wide audience, for example with online advertisements.


  • Those who respond to the call for volunteers may all display similar characteristics (such as being more trusting or cooperative than those who did not apply) thus increasing the chances of yielding an unrepresentative sample.

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