Why is sampling crucial in data analysis, especially when handling large datasets?

Sampling is vital in data analysis because it allows analysts to work with a manageable subset of data that still reflects the characteristics of the larger population. By selecting a representative sample, analysts can draw insights without processing the entire dataset, reducing computational complexity and resource usage. In large datasets, analyzing every data point can be impractical or even impossible due to storage or processing limitations. Sampling enables efficient data analysis by focusing on a smaller, statistically representative portion, which speeds up the analysis process and makes it more cost-effective. Option A is incorrect because sampling decreases the data volume rather than increasing it. Option B is incorrect as sampling involves selecting a subset, not including all data points. Option D is incorrect since sampling does not replace preprocessing steps like data cleaning. Option E is incorrect because while sampling can reduce bias, it does not guarantee a completely unbiased dataset.