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CFA Level I · CFA Level I Exam · Introduction to Financial Data Science

A data scientist finds that 4% of the entries in the 'annual income' column of a client dataset are blank. The blanks appear unrelated to any other variable, and the remaining 96% of entries are complete. Which approach to the missing values is most appropriate if the analyst wants to keep all observations?

Replacing each blank with a representative value such as the column median is most appropriate. It keeps every observation and suits a small share of randomly missing entries. Deleting columns throws away information, and recoding income as a nominal category destroys its numerical meaning.

  1. ADelete every column in the dataset that contains a blank
  2. BReplace each blank with a value such as the column medianCorrect
  3. CConvert the income column into a nominal category named 'blank'

Explanation

Imputation with a central value such as the median retains all observations when few values are missing and the gaps look random. Deleting whole columns discards information and conflicts with the goal. Turning income into a nominal category loses its numerical content.

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