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Each sample in a given study has an equal chance of being selected using the statistical technique known as random sampling. When a sample is drawn at random for a study, it ought to be a fair reflection of the overall population being studied. A sampling error occurs when the sample used in a study does not accurately reflect the study's entire population. The method of random sampling makes it easier to acquire data from populations that are occasionally too large to be used in a study. Sampling during data gathering facilitates statisticians' work on statistical analysis (Rao, 2015). The sampling techniques have great importance especially in the studies involving large populations.
Random sampling is important in making conclusions about the entire population under investigation. For instance, when a sample of 20 employees is drawn from a total population of 200 employees in an organization, there often exists a probability that a researcher may resort to considering 15 men even if the entire population consists of 150 male and 50 females. Random sampling technique simplifies the process of research. The use of the entire population during the study often proves to be a complex and expensive process that involves the use of large amount of resources (Rao, 2015). Random sampling technique reduces the number of applicable resources and enhances high accuracy in drawing conclusions. Random sampling reduces bias by presenting an equal chance for individuals to be chosen. In most cases, random sampling eliminates systematic bias thereby leading to high accuracy in the statistical analysis.
The systematic variation involves in the study may prevent the process of random sampling. Reducing variation that exists in a population by reducing the categories could prevent the errors in a random sampling technique. In some cases, it is necessary to make the sample as small as possible to eliminate errors that may arise during data collection.
Rao, J. N. (2015). Small‐Area Estimation. John Wiley & Sons, Ltd.
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