Mastering Missing Data: Choosing Between Mean, Median, Mode, or Drop — Questions & Answers

This quiz contains 10 questions. Below is a complete reference of all questions, answer choices, and correct answers. You can use this section to review after taking the interactive quiz above.

1. Question 1: Missing Data Basics

   When you encounter missing values in a dataset, which strategy involves removing entire rows that contain missing values?

   • A. Dropping
   • B. Averaging
   • C. Interpolating
   • D. Filling with minimum
   • E. Standardizing
   Show correct answer

   Correct answer: A. Dropping

2. Question 2: Mean Imputation

   If a column of numbers has missing values, which method replaces the missing values with the arithmetic average of the existing data?

   • A. Mode imputation
   • B. Mean imputation
   • C. Median replacement
   • D. Maximum imputation
   • E. Random sampling
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   Correct answer: B. Mean imputation

3. Question 3: Median Replacement

   For a dataset containing outliers, which method is most robust: replacing missing values with the mean, median, or mode?

   • A. Mean
   • B. Minimum
   • C. Median
   • D. Mode
   • E. All give same result
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   Correct answer: C. Median

4. Question 4: Mode for Categorical Data

   When handling missing values in a categorical column (e.g., color: red, blue, green), which imputation method is most appropriate?

   • A. Median
   • B. Mean
   • C. Mode
   • D. Drop the column
   • E. Use next value
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   Correct answer: C. Mode

5. Question 5: Imputation vs. Deletion

   If a dataset has only a few missing values, which action is generally safer to preserve data: imputing or dropping?

   • A. Dropping
   • B. Imputing
   • C. Replacing all
   • D. Ignoring missing
   • E. Normalizing
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   Correct answer: B. Imputing

6. Question 6: Mean Weakness

   Why might replacing missing values with the mean not be the best choice in a skewed dataset?

   • A. Mean always equals median
   • B. Mean is sensitive to outliers
   • C. Mean is always higher
   • D. Mean ignores missing values
   • E. Mean is for category data
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   Correct answer: B. Mean is sensitive to outliers

7. Question 7: Unique Situations

   If an entire column has all values missing, what is the most logical action?

   • A. Fill with mode
   • B. Replace with zeros
   • C. Drop the column
   • D. Forward fill
   • E. Fill with random values
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   Correct answer: C. Drop the column

8. Question 8: Continuous vs. Categorical

   Which method is least appropriate for dealing with missing data in a continuous numerical variable?

   • A. Mean imputation
   • B. Median infill
   • C. Zero replacement
   • D. Mode imputation
   • E. Interpolate
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   Correct answer: D. Mode imputation

9. Question 9: Consequence of Dropping

   What is a potential downside of dropping all rows with missing data from your dataset?

   • A. Increased accuracy
   • B. Reduced sample size
   • C. Less missing data
   • D. More outliers
   • E. Extra variables
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   Correct answer: B. Reduced sample size

10. Question 10: Real-life Example

    Suppose a dataset records student scores, and some scores are missing. Which method would distort the highest if one student scored much higher than the rest?

    • A. Drop missing scores
    • B. Fill with mode
    • C. Fill with mean
    • D. Fill with median
    • E. Fill with minimum
    Show correct answer

    Correct answer: C. Fill with mean