Systematic sampling is a type of probability sampling that is based on the principle of random selection. Unlike other forms of sampling, systematic sampling involves selecting a random sample from a larger population with a fixed periodic interval. This is done by first determining the population size and the desired sample size, and then calculating the sampling interval - the periodic interval between each sample.

An example of systematic sampling could be when selecting 500 people from a population of 10,000 people. The sampling interval would then be 20 (10,000 divided by 500). This means every 20th person in the population would be included in the sample.

Systematic sampling has several advantages such as it eliminates the phenomenon of clustered selection and reduces the risk of data manipulation. It also helps to ensure that the sample is representative of the population. Furthermore, since the sample is randomly selected, with each entry having the same probability of being included, the process is relatively simple to understand.

Despite its advantages, systematic sampling has its drawbacks. One is the risk of overrepresentation or undersampling of population patterns. The periodic interval chosen may inadvertently result in some demographic groups being overrepresented in the sample, such as people living in certain areas or within certain age groups. There is also the possibility of data manipulation. As the sample is pre-determined, it is easier for a researcher to manipulate the data if they have an agenda for it.

In terms of variables, systematic sampling can be divided into three main types: random systematic samples, linear systematic samples, and circular systematic samples. Random systematic samples involve selecting a random sample from the population, while linear systematic samples involve selecting a sample with a linear pattern in the population. Circular systematic sampling involves selecting a sample following a geometric pattern.

In conclusion, systematic sampling is a type of scientific sampling method that is used to select a random sample from a larger population. It has several advantages, such as a lower chance of clustered selection, reduced risk of data manipulation, and a simple process for understanding. However, it also has its drawbacks, such as the potential for overrepresentation or underrepresentation of population patterns and the possibility of data manipulation. Systematic sampling can be divided into three main types: random systematic samples, linear systematic samples, and circular systematic samples.