What is the purpose of filtering in a dataset?

Filtering in a dataset serves the primary function of removing specific records based on defined criteria. This allows users to focus on a subset of data that is relevant to their analysis or reporting needs. By applying filters, you can eliminate unwanted or unrelated data points, which helps to streamline the dataset and enhance the clarity of the information being analyzed.

For instance, if you're working with a large dataset containing information from multiple years, you may want to filter out records from years that are not relevant to your current analysis. This targeted approach not only makes data management more efficient but also ensures that the insights drawn from the data are accurate and meaningful.

Other options relate to different data manipulation or analysis techniques. For example, adding additional records is concerned with expanding the dataset, summarizing data pertains to aggregating information for high-level insights, and changing data types involves modifying the format of existing data fields. None of these actions directly represent the filtering process, which is explicitly about selecting and excluding data records based on specified criteria.