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Sampling, in statistics, is the process of learning about a population based on a sample selected from the population of interest.

For sampling, the sample can be chosen in a variety of ways. The following are some of the most important sampling methods:

• Simple random sampling: It is the type of sampling in which every unit of the population has an equal chance of being included in the sample. The sample is chosen at random by the researcher using various procedures such as a random number table and the lottery method.
• Systematic sampling: In this method, the investigator selects a starting point from the population at random and then selects every n^th data point from the population's listing.
• Stratified sampling: In this method of sampling, the investigator divides the entire population into homogeneous groups (known as strata) depending on particular criteria, and then selects a sample from each of these strata at random.
• Cluster sampling: It is a technique in which the researcher divides the population into many clusters and then selects some of the clusters at random. All members of the selected clusters are investigated.
• Convenience sampling: It's a method of sampling that isn't done at random. Subjects that are readily available are chosen in this form of sampling. For example, an online poll was performed to gauge public opinion on the introduction of a new product to the market.

In the given example, the researcher splits the test subjects into twelve groups in order to answer the question, and then four groups are picked at random. As a result, cluster sampling was chosen as the sampling method.

Hence, for the given example, the sampling method used is cluster sampling.