In research study, statistics, quality assurance, or survey methodology, sampling is the selection of a subset of individuals from within a statistical population to estimate characteristics of the whole population. In other words, sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 customers. Sampling allows you to test a hypothesis about the characteristics of a population.
So what are the difference between a population and a sample?
- The population is the entire group that you want to draw conclusions about.
- The sample is the specific group of individuals that you will collect data from.
In practice, when you conduct research about a group of people, it’s rarely possible to collect data from every person in that group. Instead, you select a sample. The sample is the group of individuals who will actually participate in the research.
To draw valid conclusions from your research results, you have to carefully decide how you will select a sample that is representative of the group as a whole. There are two types of sampling methods:
- Probability sampling involves random selection, allowing you to make strong statistical inferences about the whole group. It minimises the risk of selection bias.
- Non-probability sampling involves non-random selection based on convenience or other criteria, allowing you to easily collect data.
You should always clearly explain how you selected your sample in the methodology section of your research.
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