Real-world data requires thorough cleaning due to its inherent messiness and the presence of errors from collection to input stages, making it necessary for accurate insights.
Data cleaning is crucial for ensuring that analysis results can be generalized to a larger population, aiding in making valid conclusions beyond the sample used.
Most datasets serve as a sample of a broader population, emphasizing the importance of representative data to extrapolate findings accurately.
Data cleaning ensures that conclusions made are valid across a defined population, distinguishing it from data transformation tasks such as format conversion and normalization.
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