Which data-processing technique is used to scale data values to a common range for comparison?

Which data-processing technique is used to scale data values to a common range for comparison?

• A. Normalization
• B. Aggregation
• C. Filtering
• D. Transformation

Answer: (A)

Scaling data values to a common range to enable fair comparisons is normalization. It rescales each feature so its values fall within a standard interval, such as 0 to 1. This prevents features with larger ranges from disproportionately influencing analyses and makes it easier to compare measurements across different units or magnitudes. Transformation covers a broader set of data changes, not just scaling; aggregation combines values into a summary statistic; filtering removes data points. Because the aim is to put different data scales on the same footing for comparison, normalization is the appropriate technique.

Scaling data values to a common range to enable fair comparisons is normalization. It rescales each feature so its values fall within a standard interval, such as 0 to 1. This prevents features with larger ranges from disproportionately influencing analyses and makes it easier to compare measurements across different units or magnitudes. Transformation covers a broader set of data changes, not just scaling; aggregation combines values into a summary statistic; filtering removes data points. Because the aim is to put different data scales on the same footing for comparison, normalization is the appropriate technique.