The IGCSE Statistics (0479) syllabus, explained

This is the whole Cambridge IGCSE Statistics (0479) syllabus in one place: every topic, how the two papers work, what is not examined, and how the marks are earned. It is written for students deciding whether to take the subject, students about to start, and the parents and teachers supporting them.

It will stay useful for a while. The same syllabus runs for the first exam in June 2027 and continues, with no significant changes, through 2030. If you are still deciding whether the subject is for you, start with why you should take IGCSE Statistics.

At a glance

How the two papers work

Paper 1 and Paper 2 follow the same format, and either paper can test any topic. There is no "probability paper" to prepare for separately: both draw on the whole syllabus, and a single question can combine several topics.

Cambridge's specimen papers show the shape of a paper. Each has about ten questions, starting short, at 3 to 8 marks, and building to long questions of 11 to 16 marks in several parts. You answer every question, on the question paper itself, and the working you show is part of what earns the marks.

There is no formula sheet. The syllabus expects you to remember the formula for standard deviation, in either of these forms:

$$\sigma = \sqrt{\frac{\sum (x - \bar{x})^2}{n}}$$ $$\sigma = \sqrt{\frac{\sum x^2}{n} - \left(\frac{\sum x}{n}\right)^2}$$

The second is usually quicker with a calculator, and it is the one to use when a question gives you $\sum x$ and $\sum x^2$ rather than the data itself.

You can see both papers now: specimen paper 1 and specimen paper 2.

The twelve topics

The syllabus lists twelve topics. They fall naturally into five groups, and each group below says what you will actually be asked to do.

Collecting data (topic 1)

Where every statistical investigation starts: the difference between a population, a sample and a census, and what makes a sample representative. You learn four ways of sampling (simple random, systematic, stratified and quota) and select samples yourself using a random number table. You also learn how a sampling method can introduce bias, how open and closed questions differ in a survey, and how to classify data as qualitative or quantitative, discrete or continuous.

Showing data (topics 2 and 3)

Drawing and reading data in the usual forms: frequency and two-way tables, bar charts (including sectional and percentage bar charts), pie charts, Venn diagrams, back-to-back stem-and-leaf diagrams, and box-and-whisker plots. You will be asked to say which representation suits a set of data, and why.

For grouped data, you work out class boundaries, midpoints and widths, and draw histograms (using frequency density when the classes are different widths), frequency polygons and cumulative frequency curves.

Summarising data (topics 4 to 6)

Reducing a data set to a few numbers, and choosing the right ones:

• the mean, median and mode, including estimates from grouped data and the median by linear interpolation
• quartiles and percentiles, the range, the interquartile range and the standard deviation
• what happens to these measures when every value is increased or multiplied by a constant, or when values are added or removed
• the mean and standard deviation of two data sets combined
• scaling data to a given mean and standard deviation, including standardising to a mean of 0 and a standard deviation of 1

Throughout, you are expected to say why one measure suits a situation better than another, and to compare two distributions using both an average and a measure of spread.

Probability (topics 7 and 8)

Probability notation, the chance of an event not happening, the addition rule, mutually exclusive and independent events, simple conditional probability, and tree diagrams, including selections with and without replacement. Then probability distributions: listing every outcome in a table, and calculating the expected value.

Statistics in the real world (topics 9 to 12)

This is the part of the syllabus that O Level Mathematics does not cover, and where the subject earns its name:

• Crude and standardised rates. Comparing, for example, death rates in two towns fairly when one has a much older population than the other.
• Index numbers. Price relatives and weighted aggregate index numbers, which is how price changes such as inflation are measured, and what their limits are.
• Bivariate data. Scatter diagrams, correlation, and a line of best fit, drawn by eye and by the method of semi-averages, with its equation in the form $y = mx + c$. You use the line to estimate, and explain why predicting outside the data is risky.
• Time series. Moving averages (centred where needed), the trend line, seasonal variation, and predictions built from the two.

Each of these topics will get its own explainer on this blog.

What is not examined

How the marks are earned

Cambridge gives 80 to 90% of the marks for knowing statistical techniques and applying them accurately, and 10 to 20% for interpreting results, justifying methods and communicating conclusions. Most marks reward method, but both papers also carry marks for explaining what a result means, and those are the ones a well-practised student can still lose.

Those marks hang on the command words. These are the ones students most often misread, with Cambridge's own definitions:

The phrase to remember is in relation to the context. "The median is higher" is a statement; "the median journey time is longer for bus users" is an interpretation.

Cambridge also sets out conventions that cost marks when they are ignored:

• Show your working. Where a question asks for it, a correct answer without a clear method cannot earn full marks.
• Never mix fractions and decimals in one answer.
• Keep more figures in the working than you give in the final answer, so rounding does not creep in.
• Plot points to within half a small square, and read values from graphs to the same accuracy.
• Rule straight lines, such as a line of best fit or a trend line, and draw curves smoothly by hand.

Where to start

The two specimen papers linked above, and the syllabus itself on Cambridge's 0479 page, are the official picture of the exam.

Because 0479 is first examined in June 2027, it has no past papers yet. O Level Statistics (4040) past papers cover almost the same ground, and they are the best practice available until then. A full guide to using them is coming to this blog.

Our IGCSE Statistics course teaches the whole syllabus, from the first sample to the last time series, ready for the June 2027 exam.