What is the purpose of standardization in data analysis?

• A. Converts values to standard deviation units to enable comparison across different units.
• B. Reorganizes data to be in alphabetical order.
• C. Replaces missing values with the mean of the series.
• D. Scales data to a 0-1 range.

Answer: (A)

Standardization expresses data in terms of standard deviation units, turning each value into a z-score by subtracting the mean and dividing by the standard deviation. This centers the data and scales it so variables with different units or ranges can be compared directly, which is especially important when combining features or comparing effects in analyses like regression, clustering, or PCA. Reorganizing data alphabetically is simply sorting, not standardization. Replacing missing values with the mean is imputation. Scaling to a 0–1 range is normalization (min–max scaling), not standardization. So, converting values to standard deviation units to enable comparison across different units is the purpose of standardization.