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S1.4StatisticsFoundation

Types of Data

Before analysing data you must know what kind it is. Data splits into qualitative and quantitative, and quantitative data further into discrete and continuous — choices that decide which diagrams and calculations are appropriate.

20 min Video by Zeeshan Zamurred Data Collection
Edexcel Statistics Y1 — Data Collection playlist (Zeeshan Zamurred)Watch the full walkthrough before the notes below.
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What you'll be able to do

  • Distinguish qualitative and quantitative data
  • Distinguish discrete and continuous data
  • Classify a given variable
  • Understand why the data type matters
1

Qualitative vs quantitative

(categorical) data describes qualities — colours, names, types. data is numerical — heights, scores, counts.

Tip — Qualitative = words/categories; quantitative = numbers.

2

Discrete vs continuous

Quantitative data is if it can only take specific values (usually counts, like number of cars), and if it can take any value in a range (measurements, like height or time).

Counts are discrete; measurements are continuous.
1It is numerical (quantitative) and can take any value (measurement).
Answerquantitative, continuous
3

Why it matters

The data type determines which graphs and statistics are valid — for instance, histograms suit continuous data, while bar charts suit categorical data. Getting the type right is the first step in any analysis.

Formula recap

Non-numerical data.
e.g. number of pets.
e.g. height, time.

Common mistakes to avoid

Calling shoe size continuous because it is a number.
Shoe size takes set values, so it is discrete.
Treating all numerical data the same.
Discrete and continuous data need different diagrams and methods.

Key takeaways

  • Qualitative = categories; quantitative = numbers.
  • Discrete = countable values; continuous = any value in a range.
  • The data type decides which graphs/statistics are appropriate.

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