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.

What is a sample?

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 of the entire population of interest.

How to select a Sample?

The first most important step in selecting a sample is to determine the population. Once the population is identified, a sample must be selected. A good sample is one which is:

  • Small in size.
  • It provides adequate information about the whole population.
  • It takes less time to collect and is less costly.

In the case of our previous example, you could choose students from your class to be the representative sample out of the population (all students in the school). However, there must be some rationale behind choosing the sample.

If you think your class comprises a set of students who will give unbiased opinions/feedback or if you think your class contains students from different backgrounds and their responses would be relevant to your student, you must choose them as your sample. Otherwise, it is ideal to choose another sample that might be more relevant.

What are the merits and demerits of the sampling method?

Merits of Sampling Method

  • It is an economically viable method as it is less costly, saves time, and requires less manpower to collect data.
  • The result of the census method may be checked with the help of the sampling method.
  • In cases where the population size is too large, the sampling method is easy and more practical.
  • We can use it to make estimations about population characteristics without even surveying all units of the population.

Demerits of Sampling Method

  • If the sampling is not properly conducted, it might lead to erroneous and unrepresentative results.
  • Sampling normally generates an error due to leaving out of units from the population. If a crucial unit is left out of the sample, the resulting error will be large.
  • If skilled personnel are not available to interpret the data, the results drawn will be unreliable.

When to use 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:

Pros of a CENSUS Cons 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
Pros of a SAMPLE Cons 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

How are samples selected?

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 timing. Sampling can be random or non-random. 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 stratum. 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).