Probability sampling with application,advantages and disadvantages

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Probability sampling is based on the concept of random selection where each Simple Random Sampling elements have non-zero chance to be occurred as sample. Sampling techniques can be divided into two categories: probability sampling and non-probability sampling. Randomization or chance is the core of probability sampling techniques.

probability sampling
Probability sampling.statistical aid

For example, if a researcher is dealing with a population of 100 people, each person in the population would have the odds of 1 out of 100 for being chosen. This differs from non-probability sampling, in which each member of the population would not have the same odds of being selected.

Different types of probability sampling

      —  Quadrat Sampling

Application of probability sampling

  • In opinion poll, a relatively small number of persons are interviewed and their opinions on current issues are solicited in order to discover the attitude of the community as a whole.
  • At border stations, customs officers enforce the laws by checking the effects of only a small number of travelers crossing the border.
  • A departmental store wises to examine whether it is losing or gaining customers by drawing a sample from its lists of credit card holders by selecting every tenth name.
  • In a manufacturing company, a quality control officer take one sample from every lot and if any sample is damage then he reject that lot.


  • Creates samples that are highly representative of the population.
  • Sampling bias is tens to zero.
  • Higher level of reliability of research findings.
  • Increased accuracy of sampling error estimation.
  • The possibility to make inferences about the population.


  • Higher complexity compared to non-probability sampling.
  • More time consuming, especially when creating larger sample.
  • Usually more expensive than non-probability sampling.
Cluster sampling
Cluster sampling is defined as a sampling method where multiple clusters of people are created from a population where they are indicative of homogenous characteristics and have an equal chance of being a part of the sample. In this sampling method, a simple random sample is created from the different clusters in the population.
Area sampling
·        Consumes less time and cost
·        Convenient access
·        Least loss in accuracy of data
·        Ease of implementation
·        Higher sampling error, which can be expressed is the so-called “design effect”
·        Biased samples
·        Errors
An example of cluster sampling is area sampling or geographical cluster sampling. Each cluster is a zero graphical area because a geographically dispersed population can be achieved by grouping several respondents with in a local area into a cluster.
Cluster sampling is used to estimate high mortalities increase such as wars, famines and natural disasters.

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