Sampling and Population in Research


Sampling and population are two important and often confused subjects in research. They can be defined as;

Population: Theoretically specified group of studied elements or the large group results are generalized to

Sample: Study participants, usually a smaller group or a subset of the population

Sampling: process of selective study participants

A graphic of a diagram showing sampling and population.

There are two types of sampling, probability and it's opposite. Within there these general types, there are many different examples and kinds. A few of them are listed below.

Probability Sampling

  • Every member of the population has a known & equal chance of being selected for the study
  • Random selection is a key element

  • Simple Random Sampling: Each member of population is assigned a number, then random drawing of numbers is done.

  • Systematic Random Sampling: Every kB element is selected (i.e. every 4th name is chosen)
  • Stratified Random Sampling:

    • Group of participants are in layers / levels
    • Break into groups, then randomly select from the groups

Non-Probability Sampling

  • Not random
  • Unequal chance of selection

  • Snowball Sampling

    • Participants refer others to the study
    • Works well with hidden populations
  • Convenience or Available Participants

    • Just choosing participants because the people are available
    • Often if a survey in the middle of the pub
  • Quota

    • The non-random parallel to stratified sampling
    • Characteristic qualifies for study (i.e. demographic, age…etc)
  • Purposive Sampling

    • Criteria or diagnostic category qualifies for participation (not random)
    • For example, finding information on people who have been molested and their cases have been brought to court