Sampling: the sample group and sample size, random, stratified and convenience sampling, and how sampling decisions affect the reliability, validity and generalisability of findings

What this dot point is asking

You need to understand what a sample is, the main ways to select one, how sample size matters, and how every sampling decision feeds back into the reliability and validity of the research. Sampling is examined directly in short-answer questions and is a key decision in the Independent Research Project.

Population and sample

The population is the whole group the research is about, for example all Year 12 students in a school. Studying every member is usually impossible because of time and cost, so the researcher selects a sample, a smaller subset chosen to represent the population. The quality of that representation decides how far the findings can be trusted and applied beyond the people actually studied.

Sample size

Sample size is the number of respondents. A larger sample generally produces more reliable, more generalisable results because it smooths out individual quirks and reduces the chance that a few unusual responses distort the picture. However, size must be balanced against the resources available and the method used. A questionnaire can handle hundreds of respondents, while in-depth interviews of the same number would be unmanageable for a student. The goal is a sample large enough to be credible yet realistic for the project.

Random sampling

In random sampling every member of the population has an equal chance of being selected, for example by drawing names or using a random number generator. Its strength is that it minimises selection bias and improves the chance the sample mirrors the population. Its weakness is that it requires a complete list of the population and can still, by chance, miss important subgroups in a small sample.

Stratified sampling

Stratified sampling divides the population into subgroups, or strata, such as age bands, year groups or genders, then samples from each so all are represented in proportion. This is useful when the research question depends on comparing groups, for example differences between male and female respondents. It produces a more representative sample than simple random sampling when subgroups matter, but it requires the researcher to know the relevant strata in advance.

Convenience sampling

Convenience sampling uses whoever is easiest to reach, such as the researcher's own class or friends. It is fast and cheap, which is why many student projects rely on it, but it is prone to sampling bias because the easy-to-reach group may not represent the wider population. If a project uses convenience sampling, the limitation must be acknowledged honestly when discussing the validity of the findings.

How sampling affects reliability and validity

Sampling decisions feed directly into the trustworthiness of research. A representative sample of adequate size improves reliability, because repeating the study would likely give similar results, and improves generalisability, because the findings can be applied to the wider population. A biased or tiny sample undermines validity, because the data may not reflect the population at all. In CAFS answers, strong students name their sampling method, justify it against their research question and resources, and state honestly how it limits the conclusions they can draw.