PC Slower Than It Used to Be?
A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Crashes, No Sound, or Screen Glitches?
Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteIn statistics, a population is the complete group a study aims to understand; a sample is the subset of that group actually observed. A school measuring 60 students to estimate the average height of all its students is studying a sample, not the whole population. Whether that sample supports a conclusion about everyone depends on how the population is defined and how the sample is selected.
What do population and sample mean?
A population is the full set of units relevant to a statistical question. Its units can be people, households, businesses, institutions, or other things—not only people. The sample is the subset of those units selected for observation. Statistics Canada defines a sample as “a subset of the units of a population” in its statistical glossary.
The population is the group a researcher wants to draw conclusions about; the sample is the group from which data are collected. A result calculated from sample observations is a statistic. It may be used to estimate a characteristic of the population, such as its average or proportion.
A simple example
Suppose a school wants to estimate the average height of its students during a particular school year. The population is all students enrolled in the school during that period. If researchers measure 60 selected students, those students are the sample. Their measured average is a sample statistic used to estimate the population average; it is not automatically the exact average height of every student.
#1 Best Overall
Population vs. sample vs. census
A census seeks information from every unit in a defined population. A sample survey gathers information from some units and uses those observations to estimate characteristics of the larger group. Statistics Canada describes sampling as a way to estimate population characteristics by observing a portion of the population in its sample-selection guidance.
| Dimension | Sample survey | Census |
|---|---|---|
| Units measured | Some units from the defined population | All units in the defined population |
| Cost and effort | Often lower because fewer units are contacted | Often higher because information is sought from every unit |
| Detail | Can collect detailed data efficiently, depending on the design and sample size | Can support direct counts and small-subgroup analysis if suitable data are collected |
| Error | Can have sampling error and nonsampling error | Avoids sampling error in the intended all-unit measurement, but can still have nonsampling error |
| Often a fit when | Estimates of adequate quality meet the need and a full enumeration is impractical | Direct counts or broad coverage are needed and resources and operations permit |
These are tradeoffs, not guarantees. A sample can be biased if relevant units are missed or selection is poor. A census can still be incomplete or inaccurate because of coverage problems, nonresponse, or reporting errors. The choice depends on the information needed, budget, population size, desired detail, and timing.
Rank #2
- This guide is a perfect overview for the topics covered in introductory statistics courses.
Define the population before choosing a sample
A population definition should make clear exactly which units the result is meant to describe. Statistics Canada distinguishes the target population, for which information is wanted, from the survey population, which the survey can actually cover. If operational limits exclude part of the target population, results should be interpreted as applying to the covered survey population rather than silently extended to everyone.
- Units: Who or what is included, such as people, households, or businesses.
- Geography: The location covered.
- Reference period: When the definition applies.
- Eligibility: Any other inclusion rules, such as age group or industry.
For example, “students at the school” is less precise than “students enrolled at the school during the 2026–27 school year.” The latter specifies a reference period and reduces ambiguity about who belongs in the group.
What’s actually slowing this PC down?
Pick the symptom - the matching free tool is one click away.
Rank #3
How to judge whether a sample supports a conclusion
- Match the population to the question. Check the units, geography, time period, and eligibility rules. A study of one school cannot, on that fact alone, establish the average for students across a country.
- Check coverage. Ask how the researcher identified eligible units and whether that frame omits groups that matter. A sample drawn from an incomplete list may leave part of the target population unrepresented. Statistics Canada discusses frame coverage and sampling methods in its survey questions and answers.
- Check selection. Find out whether units were selected using a probability-based or non-probability-based method, and whether that method supports the kind of inference being made. A large group of volunteers, for example, does not become representative solely because many people responded.
- Consider sample size together with design. More observations can improve precision in suitable designs, but size alone does not correct biased selection, poor coverage, or nonresponse. Sample size also reflects the precision required, budget, and operational limits.
- Keep the conclusion within scope. Generalize only to the population the design and coverage can support; do not extend findings to groups outside it without evidence.
Sampling error and other sources of error
Sampling error arises because a sample rather than the entire population is measured: different samples can produce different estimates. A census avoids sampling error for its intended all-unit measurement, but it is not automatically error-free. Both sample surveys and censuses can be affected by nonsampling errors, such as incomplete coverage, nonresponse, or inaccurate answers. Statistics Canada distinguishes these error types in its survey-methods guidance on errors.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Common misunderstandings
- “Population” always means people. In statistics, it can mean any clearly defined set of units, including households or businesses.
- A sample is the same as the population. It is only the observed subset; conclusions about the full population are estimates unless every unit is measured.
- A bigger sample is automatically representative. Size does not fix an unrepresentative selection process or a frame that misses relevant units.
- A census has no errors. It avoids sampling error in the intended complete enumeration, but other errors can remain.
For an introductory learning resource on data and sample surveys, Statistics Canada provides educational materials on data and statistics.
Quick Recap
Best Value
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
