Lesson 1d.06

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

20 questions

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Lesson 1d.06, Know why data may need to be ‘cleaned’ before further processing, including issues...: 20 multiple choice questions for the Pearson Edexcel GCSE Statistics (1ST0), Unit 1: The collection of data, written with Revision Ninja.

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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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