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Sampling is used in data analysis primarily to make the data collection process more manageable, especially when dealing with large datasets. Collecting data from an entire population can be expensive, time-consuming, and complex. Sampling allows analysts to select a subset of the population that represents the whole. By carefully choosing a sample that reflects the diversity and characteristics of the population, analysts can make accurate inferences without the need for complete data collection. Proper sampling techniques help reduce the complexity of data analysis while maintaining its reliability and validity. Why Other Options Are Incorrect: • A: Collecting all possible data points (census) is not always practical or necessary. Sampling provides a good approximation without needing to collect every data point. • C: Sampling still requires statistical testing to ensure that the sample is representative and that conclusions are valid. • D: A sample cannot guarantee 100% accuracy, but it can provide insights that are statistically significant. • E: Sampling does not inherently exclude irrelevant data; it focuses on a representative subset of the data.
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