1d.06 Know why data may need to be ‘cleaned’ before further processing, including issues... Quiz: Pearson Edexcel Statistics, Unit 1

The 20 questions

1. What is the main purpose of cleaning data before processing?

   • Remove errors and inconsistencies
   • Change population mean
   • Format as pie chart
   • Increase sample size

2. Which item represents an extraneous symbol in a numerical column?

   • Leading zero
   • Decimal point
   • Minus sign
   • Stray currency sign

3. How is an outlier best defined in a data set?

   • Value far from others
   • Missing spreadsheet entry
   • Exact median value
   • Most frequent value

4. Why might an apparent outlier be kept in the data set?

   • Genuine unusual observation
   • Missing response
   • It is a typo
   • Formatting error

5. What term describes a value breaking the expected data rules?

   • Median
   • Anomaly
   • Statistic
   • Parameter

6. Which spreadsheet tool helps locate extraneous characters across a column?

   • AutoSum formula
   • Sort ascending
   • Find and replace
   • Pie chart tool

7. Which error is typically detected during data cleaning?

   • Accurate date entry
   • Correct mean value
   • Text in number column
   • Valid tally mark

8. A height recorded as 1720 cm instead of 172 cm indicates what?

   • Missing value code
   • Data entry error
   • Genuine outlier
   • Extraneous symbol

9. What is the best action for an obvious typing error in a dataset?

   • Ignore completely
   • Multiply by ten
   • Delete entire column
   • Check against source

10. Mixed formats like '45%' and '45' in one column require what action?

    • Delete percentages
    • Leave unchanged
    • Add them together
    • Standardise the format

11. How should a missing numeric value marked with a symbol be handled?

    • Treat as zero
    • Ignore the symbol
    • Use missing value code
    • Delete the row

12. What is the correct action for a negative value in an age column?

    • Correct or remove
    • Keep as genuine
    • Square the value
    • Calculate average

13. What suggests an extreme value is a genuine outlier rather than an error?

    • Negative sign present
    • Verified source match
    • Wrong data type
    • Typing format error

14. How should footnote symbols next to numbers in data cells be treated?

    • Include in sum
    • Convert to text
    • Treat as zero
    • Remove for calculations

15. What is the primary action when cleaning raw data before summarising?

    • Doubling sample size
    • Deleting all zeros
    • Scanning for errors
    • Averaging every row

16. In the dataset 20, 22, 21, 19, 23, 95, what is 95 classified as?

    • An outlier
    • The mode
    • A frequency
    • The median

17. Which boundary rule flags a possible outlier using the interquartile range?

    • UQ plus 2 IQR
    • LQ minus 0.5 IQR
    • Median plus IQR
    • UQ plus 1.5 IQR

18. What is the consequence of removing a genuine outlier during data cleaning?

    • Increasing sample size
    • Understating data variation
    • Shifting the mode
    • Creating missing data

19. Why should every data cleaning change be recorded?

    • To increase speed
    • To reduce rounding
    • To ensure transparency
    • To lower costs

20. What must a researcher do first when encountering extreme values in a dataset?

    • Ignore them completely
    • Delete them all
    • Investigate each one
    • Multiply by ten

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