Description: Systematic sampling is a statistical method that involves selecting elements from an ordered sampling frame. This approach is based on the idea that by selecting elements at regular intervals along a list or sequence, a representative sample of the total population can be obtained. Unlike random sampling, where each element has an equal chance of being selected, systematic sampling uses a random starting point and then chooses elements at fixed intervals. This method is particularly useful in situations where the population is large and an efficient sampling method is required. Key characteristics of systematic sampling include its simplicity and speed, making it an attractive option for researchers and analysts. Additionally, it is less prone to bias than other sampling methods, provided that the list of elements does not exhibit patterns that could influence the results. In the context of data preprocessing, systematic sampling can help reduce the size of datasets without losing representativeness, which is crucial for data analysis and the construction of models. Furthermore, in data anonymization, this method can be used to select subsets of data that maintain individual privacy while preserving the integrity of the information.
