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Data sampling in analytics reporting refers to the practice of analyzing a subset of data to draw conclusions about the entire dataset. This technique is often used when processing large volumes of data, where it may be impractical or inefficient to analyze the complete dataset. By selecting a representative sample, analysts can generate insights and identify trends that can be generalized to the broader population, all while reducing the time and resources needed for analysis.

Sampling can help improve reporting efficiency and can also mitigate challenges linked to data processing, such as computational limits or system performance issues. The goals of sampling are to achieve a balance between accuracy and efficiency, enabling organizations to make informed decisions based on achievable data insights.

In contrast, the other provided options do not accurately represent the concept of data sampling. Creating new datasets implies data transformation rather than reduction, collecting data from multiple sources relates more to data integration, and segmenting data focuses on dividing data into manageable parts for easier analysis rather than drawing conclusions from a smaller representative group.