Populations and Samples
Statistics starts with a simple choice: study everyone, or study a sample. Knowing the difference between a population and a sample — and the trade-offs of a census versus a sample — underpins the whole module.
What you'll be able to do
- Define population, sample and sampling unit
- Distinguish a census from a sample
- Explain the advantages and disadvantages of each
- Understand what a sampling frame is
Population vs sample
The is the whole set of items or people you are interested in. A is a smaller selection taken from it. The individual members are , and a list of them is the .
Tip — Population = everyone/everything of interest; sample = the subset you actually measure.
Census vs sample
A measures every member of the population. It is completely accurate but is often expensive, slow, and sometimes impossible (e.g. testing every item destroys them). A is cheaper and faster but may not perfectly represent the population.
Why sample?
Samples save time and money and are essential when testing is destructive. The downside is potential bias and the fact that conclusions are estimates. A good sample is large enough and chosen to fairly represent the population.
Formula recap
Common mistakes to avoid
Key takeaways
- Population = whole group of interest; sample = subset measured.
- A census surveys everyone (accurate but costly); a sample is cheaper/faster.
- Sampling units are the members; the sampling frame lists them.
Test yourself
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