Sampling in statistics explained
Statistics studies a sample, a smaller group drawn from a population, to draw conclusions about the whole. This guide explains why we sample, the common sampling methods, and how to avoid bias.
Why we sample
Studying an entire population is often impossible or impractical, so statisticians study a sample and use the results to draw conclusions about the whole. A good sample stands in for the population; a biased one leads to wrong conclusions no matter how large it is.
Common sampling methods
| Method | How it works |
|---|---|
| Simple random | Every member has an equal chance of selection |
| Systematic | Pick every nth member from a list |
| Stratified | Split into groups, then sample each in proportion |
| Cluster | Divide into clusters, then sample whole clusters |
| Convenience | Use whoever is easiest to reach (prone to bias) |
Avoiding bias
Sampling bias occurs when some members are more likely to be chosen than others, so the sample does not reflect the population. Convenience samples and self-selected surveys are common culprits. Random selection, an adequate sample size, and a high response rate all help keep a sample representative.
- A sample is a subset used to learn about a whole population.
- A representative sample reflects the population fairly.
- Random, systematic, stratified, and cluster sampling reduce bias.
- Convenience sampling is easy but often biased.
- Bias, not just size, determines whether a sample is trustworthy.
Related guides
See the Confidence Interval Guide and Statistics for Beginners.
FAQ
Why use a sample instead of the whole population?
Studying everyone is often too costly or impractical, so a representative sample is used to draw conclusions about the whole.
What is sampling bias?
When some members of the population are more likely to be selected, making the sample unrepresentative.
What is stratified sampling?
Splitting the population into groups and sampling each in proportion to its size, ensuring all groups are represented.