Missing data refers to the absence of values or observations in a dataset, which can occur for various reasons such as non-response in surveys, data entry errors, or equipment malfunctions. This phenomenon is important in data cleaning and preprocessing because it can significantly affect the accuracy and validity of statistical analyses, leading to biased results if not properly addressed. Understanding how to handle missing data is crucial for ensuring that conclusions drawn from analyses are reliable and reflective of the underlying population.
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