Which technique groups continuous numeric values into discrete ranges to aid distribution analysis?

• A. Deriving
• B. Subquery
• C. Binning
• D. Appending

Answer: (C)

Grouping continuous numeric values into discrete ranges is binning. Binning turns a range of numeric data into labeled intervals or “bins,” making it easier to see how data are distributed across different levels. This approach is especially helpful for creating histograms, spotting patterns, and comparing frequencies within each range. You can use equal-width bins (such as 0–9, 10–19) or equal-frequency bins, which helps manage outliers and highlight trends that might be hard to discern from raw numbers. By assigning each value to a bin, you convert a continuous variable into a categorical-like one that supports distribution analysis and segment comparisons. The other options don’t perform this grouping: deriving creates new computed values from existing data, subqueries are nested SQL queries used to fetch data, and appending stacks datasets together rather than organizing values into ranges.