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 an important step in data cleaning.
Two of the most common techniques are: removing rows that contain missing values (option 2) and imputing missing values by inserting a value close to the mean or mode (option 3).
These methods help maintain the integrity and accuracy of the dataset.
Removing entire columns (option 1) is less common because it may result in significant data loss.
Leaving missing data as it is (option 4) is not ideal for analysis as it may affect results.
Inserting a random value (option 5) is not recommended because it can introduce noise and bias.
Therefore, options 2 and 3 are the correct common techniques for dealing with missing data.