Random Sampling

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Random Sampling, or probability sampling, is a sampling method that allows for the randomization of sample selection, i.e., each sample has the same probability as other samples to be selected to serve as a representation of an entire population.[1]

Types of Random Sampling[2]

  • Simple random sampling: Simple random sampling is the most straightforward approach to getting a random sample. It involves picking the desired sample size and selecting observations from a population in such a way that each observation has an equal chance of selection until the desired sample size is achieved. For example, a random selection of 20 students from a class of 50 students gives a probability of selection being 1/50.
  • Stratified random sampling: This technique divides the elements of the population into key subgroups or strata. The elements are randomly selected from each of these strata. For example, males under 30, females under 30, males 30 or over, and females 30 or over. Say you want to achieve a sample size of 200, then you can pick samples of 50 from each stratum. The required sample size for each stratum will be designed either to match the known population proportions or to over-represent key subgroups of interest. We need to have prior information about the population to create subgroups. The main benefit of stratified sampling over simple random sampling is making sure that you have good sample sizes in key subgroups.
  • Cluster sampling: Similar to stratified random sampling, cluster sampling divides the sample into a large number of subgroups. Then some of these subgroups are selected at random, and simple random samples are then collected within these subgroups. These subgroups are called clusters. Typically, the purpose of cluster sampling is to reduce the costs of data collection. This is achieved by defining clusters according to the ease of access (e.g., a suburb may be a cluster if door-to-door sampling or a household may be a cluster if phone interviewing).
  • Multi-stage sampling: Multi-stage sampling is a combination of one or more of the techniques described above. The population is divided into multiple clusters and then these clusters are further divided and grouped into various subgroups (strata) based on similarity. One or more clusters can be randomly selected from each stratum. This process continues until the cluster cannot be divided any further
  • Alternatives to random sampling: Convenience sampling refers to approaches where considerations of simplicity rather than randomness determine which observations are selected in a sample. Here the samples are selected based on availability. When the availability of samples is rare, convenience samples are selected. This is used generally during the initial stages of a survey and is quick and easy to deliver results.

See Also


  1. Defining Random Sampling CFI
  2. Types of Random Sampling DisplayR