Why is sampling commonly used in data analysis, especially when dealing with large datasets?

Sampling is a critical technique in data analysis, especially when dealing with large datasets, as it reduces the complexity, time, and costs associated with data collection and processing. Instead of analyzing an entire population, which can be resource-intensive, a sample that represents the population well can be analyzed to make inferences about the entire dataset. Sampling also maintains data quality by ensuring that the selected subset is representative. Option B is the most accurate because it directly highlights the efficiency and cost-effectiveness of sampling without compromising the reliability of the analysis. Option A is incorrect because sampling involves selecting a subset of the population, not the entire population. Option C is incorrect as sampling applies to both structured and unstructured data, though the methods may vary. Option D is incorrect because accuracy is dependent on the quality of the sample, not the fact that only specific subsets are analyzed. Option E is incorrect because, despite advances in technology, analyzing the entire dataset can still be resource-intensive, especially with Big Data.