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    Question

    Why is sampling commonly used in data analysis,

    especially when dealing with large datasets?
    A Sampling allows analysts to work with the entire population. Correct Answer Incorrect Answer
    B Sampling helps to reduce the time and cost of data collection without compromising data quality. Correct Answer Incorrect Answer
    C Sampling only works when the data is highly structured and does not apply to unstructured data. Correct Answer Incorrect Answer
    D Sampling leads to better accuracy in predictions by analyzing only specific subsets of data. Correct Answer Incorrect Answer
    E Sampling is irrelevant in large datasets as technology can handle the entire dataset efficiently. Correct Answer Incorrect Answer

    Solution

    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.

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