Cambridge IGCSE Statistics: Representation of Data (0479)

This guide covers Topic 2 Representation of Data, the second of 12 topics in Cambridge IGCSE Statistics (0479), for examination 2027. Where Topic 1 covered how data is collected and sampled, Topic 2 covers how that raw data is turned into charts, tables and diagrams that can actually be read and compared.

Both papers are 2 hours 15 minutes, 100 marks and equally weighted at 50% of the qualification each, so a topic like this one – short on raw content but broad in application – can appear in either paper, mixed alongside questions from any other topic.

Where this fits in 0479

Topic 2’s diagrams are foundational to almost everything that follows in the syllabus: frequency tables reappear when Topic 3 groups data into frequency distributions, and the comparative skills practised here – reading and interpreting a chart rather than just drawing one – are tested throughout both Paper 1 and Paper 2, since every question on this syllabus may draw on content from any topic.

Syllabus coverage

CAMBRIDGE IGCSE STATISTICS (0479) — TOPIC 2 REPRESENTATION OF DATA

The syllabus presents this topic as a flat list of learning outcomes rather than named sub-headings, so it is set out here exactly as published:

• Represent and interpret data in frequency tables and two-way tables
• Represent and interpret data in pictograms, bar charts (simple, sectional, percentage sectional and multiple bar charts), pie charts (including comparative pie charts), Venn diagrams, box-and-whisker diagrams, and stem-and-leaf diagrams (including back-to-back diagrams) – note that drawing pictograms is not required, and identifying outliers is not required for box-and-whisker diagrams
• Understand the advantages and disadvantages of different ways of representing data

A worked example makes the choice between diagram types concrete. Suppose a class records how each of 30 students travels to school (walk, bus, car, bicycle). A simple bar chart or pictogram shows how many students use each method at a glance. If the same data is broken down by gender as well as travel method, a sectional or multiple bar chart – or a comparative pie chart – lets a reader see both the overall pattern and the split within it. If the class instead records each student’s exact travel time in minutes, a stem-and-leaf diagram keeps every individual value visible (useful for a small data set where no detail should be lost), while a box-and-whisker diagram is more useful once the class wants to summarise the middle 50% of travel times and compare that spread against another class’s data. A Venn diagram would suit a different question again – for example, showing how many students both cycle to school and also play a sport after school, where the two categories overlap rather than being mutually exclusive.

Two-way tables deserve a specific mention, since they are easy to under-revise relative to the more visually memorable diagrams. A two-way table cross-tabulates two variables at once – travel method against gender, for instance – and reading values out of a two-way table correctly (a specific row-and-column intersection, a row total, or a column total) is tested directly and independently of any of the diagram-drawing skills above.

How to approach it

This topic is short on paper but wide in practice, since seven different diagram types are named and each has its own drawing conventions and its own natural use case. A useful revision habit is to build a one-line “when to use it” note for each diagram type as you meet it: pictograms and simple bar charts for quick visual comparison of categories; sectional and percentage sectional bar charts, or pie charts, when the parts of a whole matter as much as the total; comparative pie charts (conventionally two pie charts whose radii are scaled to the square roots of their totals, not simply two same-size charts) or multiple bar charts when comparing two data sets side by side; Venn diagrams for data that falls into overlapping categories; box-and-whisker diagrams for summarising spread and identifying the middle 50% of a data set; and stem-and-leaf diagrams (including back-to-back versions) when the individual data values themselves need to stay visible rather than being grouped away. Cambridge’s assessment objective AO2 (weighted 10-20% of both papers) specifically rewards justifying the use of a specific method of analysis in a given situation, so “advantages and disadvantages” questions on this topic are best practised by comparing two named diagram types directly – for example, explaining why a stem-and-leaf diagram preserves more detail than a grouped bar chart, but a bar chart is easier to read at a glance for a large data set, or why a pie chart shows proportion clearly but is harder than a bar chart to use for comparing exact values between categories. Since drawing pictograms is explicitly excluded (though interpreting pictograms already drawn remains fully examinable) and identifying outliers from a box-and-whisker diagram is explicitly excluded, do not spend revision time on pictogram construction or outlier identification – these are simply this new syllabus’s own stated exclusions (0479’s first examination is 2027), not content removed from an earlier series.

It is worth remembering that Topic 2 is deliberately about representing data that has already been collected (Topic 1) and is still in its raw or lightly organised form – once data is organised into class intervals for a large data set, that becomes Topic 3 (Frequency distributions), which introduces histograms and frequency polygons as a separate, more advanced step. Keeping this boundary clear avoids the common confusion of trying to apply Topic 3’s grouped-data techniques to a Topic 2 question, or vice versa.

Official syllabus

Cambridge IGCSE Statistics (0479) syllabus for exams in 2027 — cambridgeinternational.org.