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MathidiaMathematics Academy
Data collection and representation
Lesson 43 of 75
Standard practice

Large data sets

Objective. Clean, interpret and critique authentic multivariable data.

  1. 1. Learn
  2. 2. Worked example
  3. 3. Practice
  4. 4. Feedback
  5. 5. Continue

Learn: the key idea

A large data set is a substantial real-world collection of multiple linked variables, used in statistics courses to practise cleaning, interpreting and critiquing authentic data rather than small artificial examples. Before analysis, data often needs cleaning to handle missing values, obvious errors, or inconsistent units, and careful interpretation is needed because real data can include outliers or anomalies with genuine explanations. For example, a weather large data set might record daily temperature, rainfall and wind speed across several UK locations over a decade, and spotting a recorded temperature of 95°C in England would flag an error needing correction or removal. Working confidently with such data builds skills in critically evaluating statistical claims drawn from genuine sources.

Worked example

A large data set of daily rainfall in millimetres for a UK city shows one entry of −5 mm. Explain what this entry in the large data set reveals and determine whether it should be used in calculations of total rainfall.

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Illustrated card showing 5
  1. 1Recognise that rainfall cannot physically be negative, since it measures a non-negative quantity.
  2. 2Conclude the −5 mm entry is a recording or data entry error, not a genuine measurement.
  3. 3Decide it should not be included in calculations such as the mean or total rainfall.
  4. 4State the data should be cleaned by removing or correcting this erroneous entry before analysis.

Answer: The −5 mm entry is an error and should be excluded or corrected before analysis

Concept mastery

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Practice

Practice happens on its own screen, one question at a time. Answers stay hidden until you submit yours, and you can stop and pick up at the same question later.

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Common mistake. Students often treat every unusual value in a large data set as definitely an error to be deleted, when some extreme values are genuine and meaningful. Always investigate context before removing an apparent outlier, since real data can legitimately contain extremes.

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