Posted on Saturday, June 12, 2021 1:48:06 PM Posted by Zarina P. - 12.06.2021

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Published: 12.06.2021

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Systematic sampling is a type of probability sampling that takes members for a larger population from a random starting point. It uses fixed, periodic intervals to create a sampling group that generates data for researchers to evaluate. Each interval gets calculated by dividing the population size by the desired scope of the sample. The first person would be randomized, which creates a selection series that reduces bias because the starting point becomes unpredictable.

In statistics, sampling is when researchers choose a smaller set of items or individuals within a larger group to study. Researchers then predict the characteristics of a whole population based on that sample. Sampling is advantageous to researchers because it allows them to study large groups even when their time and resources are limited. Systematic sampling allows researchers to take a smaller sample according to a set scheme or system. Systematic sampling by definition is systematic, but there are still systematic sampling advantages and disadvantages. One systematic sampling definition is that it is used in probability, especially in economics and sociology.

While reaching to conclusion about a large volume of data, we prefer to take samples from the whole population and then we analyze them and reach to a conclusion. We expect that the samples taken represents the whole population sufficiently or at least reasonably. We want to use our judgment as less as possible as the judgment sometimes can lead towards biasness. As the Simple Random Sampling involves more judgment and Stratified Random Sampling needs complex process of classification of the data into different classes, we use Systematic Random Sampling. We can also say that this method is the hybrid of two other methods viz. The figure above shows us how we conduct the process of choosing the samples from the given population. The first sample is chosen at random and then the remaining are chosen by leaving two items after the previous sample.

When to use it. Ensures a high degree of representativeness, and no need to use a table of random numbers. When the population is heterogeneous and contains several different groups, some of which are related to the topic of the study. Ensures a high degree of representativeness of all the strata or layers in the population. Possibly, members of units are different from one another, decreasing the techniques effectiveness.