Data Handling and Representation
MYP Mathematics — Statistics & Probability strand (Year 3)
This is the unit where marks get lost for silly reasons, not hard ones — a miscounted tally, a pie chart that doesn't add to , a chart chosen because it 'looks nice' instead of because it fits the data. The maths here is genuinely simple; the discipline of doing it carefully and in the right order is what examiners are actually testing.
Overview — The Data Handling Journey
Every data-handling question follows the same pipeline: collect raw data (often via a survey), organise it (tally and frequency tables), classify it (discrete, continuous, categorical), represent it (bar chart, line graph, pie chart, stem-and-leaf, dot plot), then interpret it (mode, range, trend). Examiners test each stage separately — and they test whether you can justify why you picked a particular representation, not just whether you can draw one.
- Raw data is the unsorted list you're handed — it's almost never usable on its own.
- A frequency table is the first transformation: it answers 'how many of each?' and nothing more.
- Choosing the right chart depends on the type of data, not on which chart you find easiest to draw.
- Command terms like 'construct', 'complete', 'explain' and 'analyse' each demand a different depth of answer — mixing them up is the single biggest source of dropped marks in this unit.
The shape of the chapter
Command terms that control the mark scheme in this unit
| Command term | What it demands | AO | Mark-earning move |
|---|---|---|---|
| Construct | Build a table or chart from raw data, following all conventions (labels, scale, title). | Criterion A | Marks split between correct data AND correct presentation — a perfectly plotted chart with no title or axis labels still drops a mark. |
| Complete | Fill in missing tally/frequency values in a table that's already been started for you. | Criterion A | Every value must match the raw data exactly — one miscounted row forfeits that row's mark even if the rest is flawless. |
| Identify | Name one specific feature — a category, the mode, the modal class. | Criterion A | A single word/phrase is enough — but it must be the actual name (e.g. 'Blue'), not a number or a description. |
| Explain | Give a reason with a stated cause-and-effect link. | Criterion D | A bare assertion earns 0 — you must connect a feature of the data to a consequence, e.g. 'because... which means...' |
| Interpret | Draw a conclusion about the real-world data using the completed table or chart. | Criterion D | Must reference actual figures from the table — a vague comment with no numbers loses the mark. |
| Analyse | Break down a method or dataset and discuss a limitation or its reliability. | Criterion D | Must name the limitation AND state its effect on the validity of the conclusion — naming it alone gets half marks at best. |
| Apply | Use the frequency table to make a real decision (e.g. how much stock to order). | Criterion D | Must scale the real quantity proportionally to the frequencies, not just restate the table. |
| Justify | Support a choice (e.g. of representation) with evidence. | Criterion D | Must name a specific property of the data (type, number of categories, spread) and tie it directly to the choice made. |
Key point
Overview