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### Systematic sampling is a technique that allows you to select sample members from a larger population according to a random starting point and a fixed periodic interval. This interval, called the sampling interval, is calculated by dividing the population size by the desired sample size.

Compared to other probability sampling techniques, including simple random sampling, systematic sampling has a number of advantages. First off, since you do not need to assign numbers or use random number generators for every member of the population, it is simpler and quicker to execute. Second, it can prevent the phenomena of cluster selection, which happens when some populations' members are more likely to be chosen than others because of their closeness or similarity. Third, it has a minimal likelihood of introducing bias or mistake into the data, provided that the population order is random or random-like (e.g., alphabetical) and not cyclical or periodic.

Depending on your study goals and the availability of data, you can utilize one of three primary forms of systematic samples: random systematic samples, linear systematic samples, or circular systematic samples. By choosing a random starting point and then applying the sampling interval across the population list, random systematic samples are produced. The first or final item of the population list is chosen as the fixed starting point for linear systematic samples, and the sampling interval is then applied to the whole population list. By choosing a random starting point, applying the sampling interval throughout the population list, and then returning to the starting point, circular systematic samples are created.

Let's say that you wish to poll 10,000 bank customers about their level of customer satisfaction in order to demonstrate how systematic sampling works. You choose to conduct your survey with a sample size of 100 customers. You must first determine your sampling interval by dividing 10,000 by 100, which gives you 100, in order to create a random systematic sample. Next, you decide on a beginning point at random between 1 and 100. (let us say 37). Then, beginning with client number 37, you choose every 100th client from your population list (e.g., client number 37, 137, 237,...9937).

You can simply start with client number 1 or client number 10,000 and then apply the same sampling interval of 100 to the entire population list to produce a linear systematic sample. The same procedures as for getting a random systematic sample must be followed, except you must choose a customer every 100th time until you return to client number 37 rather than stopping at client number 9937.

Systematic sampling is a simple and effective way to conduct probability sampling that can help you obtain representative findings on a large group of people without having to reach out to each and every one of them. To avoid biasing your sample selection, you should be careful how your population list is arranged and refrain from applying this technique if there are any patterns.