Census and sample

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Studying a population

A population may be studied using one of two approaches: taking a census, or selecting a sample. 

It is important to note that whether a census or a sample is used, both provide information that can be used to draw conclusions about the whole population. 

Census: complete enumeration

A census is a study of every unit, everyone or everything, in a population. It is known as a complete enumeration, which means a complete count.

Sample: partial enumeration

A sample is a subset of units in a population, selected to represent all units in a population of interest. It is a partial enumeration because it is a count from part of the population.

Information from the sampled units is used to estimate the characteristics for the entire population of interest. 

Choosing between a census or a sample

Once a population has been identified a decision needs to be made about whether taking a census or selecting a sample will be the more suitable option.  There are advantages and disadvantages to using a census or sample to study a population:

Advantages and disadvantages of taking a census or sample
Advantages of a censusDisadvantages of a census
  • provides a true measure of the population (no sampling error)
  • benchmark data may be obtained for future studies
  • detailed information about small sub-groups within the population is more likely to be available
  • may be difficult to enumerate all units of the population within the available time
  • higher costs, both in staff and monetary terms, than for a sample
  • generally takes longer to collect, process, and release data than from a sample
Advantages of a sampleDisadvantages of a sample
  • costs would generally be lower than for a census
  • results may be available in less time
  • if good sampling techniques are used, the results can be very representative of the actual population
  • data may not be representative of the total population, particularly where the sample size is small
  • often not suitable for producing benchmark data
  • as data are collected from a subset of units and inferences made about the whole population, the data are subject to 'sampling' error
  • decreased number of units will reduce the detailed information available about sub-groups within a population

Samples selection

A sample must be robust in its design and large enough to provide a reliable representation of the whole population.  Aspects to be considered when designing a sample include the level of accuracy required, cost, and the timing. Sampling can be random or non-random.

Random (or probability) sample

In a random (or probability) sample each unit in the population has a chance of being selected, and this probability can be accurately determined.

Probability or random sampling includes, but is not limited to, simple random sampling, systematic sampling, and stratified sampling.  Random sampling makes it possible to produce population estimates from the data obtained from the units included in the sample.

Simple random sample

All members of the sample are chosen at random and have the same chance of being in the sample. A lottery draw is a good example of simple random sampling where the numbers are randomly generated from a defined range of numbers (i.e. 1 through to 45) with each number having an equal chance of being selected.

Systematic random sample

The first member of the sample is chosen at random then the other members of the sample are taken at intervals (i.e. every 4th unit).

Stratified random sample

Relevant subgroups from within the population are identified and random samples are selected from within each strata.

Non-random (or non probability) sample

In a non-random (or non-probability) sample some units of the population have no chance of selection, the selection is non-random, or the probability of their selection can not be determined.

In this method the sampling error cannot be estimated, making it difficult to infer population estimates from the sample.  Non-random sampling includes convenience sampling, purposive sampling, quota sampling, and volunteer sampling 

Convenience sampling

Units are chosen based on their ease of access;

Purposive sampling

The sample is chosen based on what the researcher thinks is appropriate for the study;

Quota sampling

The researcher can select units as they choose, as long as they reach a defined quota; and

Volunteer sampling

Participants volunteer to be a part of the survey (a common method used for internet based opinion surveys where there is no control over how many or who votes).

Collecting data about a population

Step 2 - Census or sample

Collecting data about a population flowchart
Flowchart of the 3 steps when collecting data about a population. Step 1 - What is a population? Step 2 - Census and sample (This step talked about on this page) Step 3 - Data sources

Collecting data about a population flowchart

Flowchart outlining the steps and decisions needed when collecting data
What or who is the target population? Define target population. Will the data be collected from all or part of the population? If doing a complete enumeration (all), a census is conducted. If only doing a partial enumeration (part), a sample is taken (This step talked about on this page). How will the data be sourced? If collecting primary data in a direct data collection, a survey is conducted. This includes questionnaires, interviews, focus groups and observations. If collecting secondary data in an indirect data collection, an administrative data source can be used. This data exists as a by-product of administrative processes.

Further information

Data sources
An introduction to sample surveys: A user's guide
Sample size calculator