Removing records that contain missing or inconsistent data from a dataset before analysis is an example of ________.

• A. Reduction
• B. Sampling
• C. Normalization
• D. Transformation

Answer: (A)

Data reduction is about shrinking the dataset to make analysis faster and simpler. Dropping records with missing or inconsistent values reduces the amount of data you work with, which directly lowers data size and complexity for subsequent steps. This is different from normalization or transformation, which change the values or representation of the data, and from sampling, which selects a subset for estimation rather than cleaning the data. So removing problematic records is a straightforward way to reduce the dataset before analysis.