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    Question

    In data analysis, why is sampling often preferred over

    analyzing an entire population?
    A Sampling provides more detailed insights than analyzing the full population. Correct Answer Incorrect Answer
    B Sampling avoids the risk of introducing bias that would occur in a full population analysis. Correct Answer Incorrect Answer
    C Sampling is typically less expensive, faster, and easier, making data analysis manageable. Correct Answer Incorrect Answer
    D Sampling ensures that rare events are more accurately represented. Correct Answer Incorrect Answer
    E Sampling results in 100% accurate conclusions about the entire population. Correct Answer Incorrect Answer

    Solution

    Sampling is a practical approach in data analysis due to its efficiency in terms of time, cost, and complexity. Analyzing an entire population, especially in large datasets, is often impractical or impossible due to resource constraints. By using a representative sample, analysts can gather meaningful insights and make accurate inferences about the larger population without the exhaustive effort required to analyze every individual item. This efficiency is particularly crucial when dealing with extensive databases or real-time data where analyzing the full dataset is unnecessary. Sampling thus allows analysts to balance accuracy with feasibility, reducing the scope of data while retaining reliable results. The other options are incorrect because: • Option 1 is misleading, as sampling does not inherently provide more detail than full population analysis; rather, it offers an approximation. • Option 2 is incorrect since bias can occur in sampling; careful sample design helps mitigate it, but sampling does not inherently prevent bias. • Option 4 is inaccurate; rare events might actually be underrepresented in sampling, depending on the sample type and size. • Option 5 is false; sampling introduces sampling error, and conclusions are approximate, not absolute, for the entire population.

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