Question

Which of the following are common techniques for handling missing data in a dataset?
1. Remove the entire column of data.
2. Remove the row of data with missing values.
3. Insert a value close to the mean or mode of the variable with missing data.
4. Leave the missing data as it is.
5. Insert a random value.

• A. 1 and 2
• B. 2 and 3
• C. 3 and 4
• D. 4 and 5

Hint:

Best practice: Remove or impute missing data — never ignore it or fill randomly.

Answer 1

The Correct Option is B

Handling missing data is a crucial aspect of data preparation.
Two prevalent strategies involve: discarding records with absent entries (selection 2) and filling gaps with values approximating the mean or mode (selection 3).
These approaches safeguard the dataset's reliability and precision.
Eliminating entire data columns (selection 1) is infrequent due to the potential for substantial information depletion.
Retaining missing data without intervention (selection 4) is suboptimal for analytical purposes, as it can skew outcomes.
Introducing random values (selection 5) is discouraged, as it can inject variability and prejudice.
Consequently, selections 2 and 3 represent the standard, effective methods for addressing incomplete data.