In statistical language, sampling is choosing the portion or subset of a population. A population is the entire group of objects having characteristics of interest under study. The subset of a population that is chosen for the study is known as a sample. In the context of market research, sampling means collecting opinions from a chosen segment of a large mass, to know the characteristics about the whole group.

The chosen sample must represent all or most of the features of the population from which it is chosen. To ensure that the chosen sample appropriately represents the population, a strategy is required. This strategy is known as a “sampling strategy.” The sampling strategy is a plan or strategy created to make sure that the sample of the population on which data will be collected is accurately representative of the group identified for study.

The task of sampling is undertaken when information regarding a process or product is not readily available, and analysis of the entire population on which the critical information is required is not feasible or possible (i.e., such an undertaking would be too time-consuming and too costly). Because sampling reduces costs and employs fewer human resources (among other benefits), it is commonly employed in most industries that require critical information regarding a process or product.

Sampling is also used when the data collection is a destructive process. For example, CDF Inc. is a mineral water manufacturer that produces bottled mineral water. The quality assurance team tests the quality standard of the mineral water by randomly selecting a sample of bottles taken from each production batch. In the testing process, they open the bottles and introduce chemicals into the contents, thus destroying the sample. These bottles will no longer be hygienic enough for sale and the water will be contaminated; testing the entire population of bottles would result in no revenue for the company, and therefore a sample is tested.

Researchers can choose from a number of different types of sampling strategies. The type of strategy chosen should appropriately suit the research objectives.

Sampling strategies are classified as either probability sampling or non-probability sampling.

Probability Sampling Strategies—Probability sampling strategies are the most reliable sampling strategies because the margin of error is minimal due to the statistical procedures used. In these strategies, every component in the population has an equal and independent opportunity to be chosen.

The four main methods of probability sampling are simple random sampling, systematic sampling, stratified sampling, and cluster sampling.

Non-probability Sampling Strategies—Non-probability sampling strategies are not as reliable as probability sampling strategies. The selection procedures in these strategies involve non-random methods. As a result, the subjects in the population do not have an equal chance of being selected as part of a sample. These types of sampling strategies are less likely to produce representative samples than probability sampling strategies. Regardless of this factor, many researchers have successfully used and continue to use these strategies. The three main strategies of non-probability sampling are Convenience, Quota, and Purposive.

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