Studio checkpoint: prototype the evidence display
Teams build two plausible displays for their project data, then test which one makes the pattern visible without distorting scale or context. Feedback must change at least one title, label, scale, or graph choice.
- Studio move: Display prototypes, peer usability test, and revision note
- Portfolio evidence: An annotated display set selected for the final story
🎯 Learning Intentions
- Choose the correct graph type for different data types
- Create accurate bar graphs, pie charts, or dot plots
- Ensure all graphs have titles, labels, and keys
🎥 Media Anchor (8 mins)
Video: Poster Design Principles
- Which graph type communicates your data most clearly and why?
- What design choice could accidentally mislead your audience?
1. Graph Matching (10 mins)
Match the data type to the graph:
- Category Data (e.g., fav colour) → Bar Graph (counts) or Pie Chart (percentages)
- Numerical Data (e.g., height) → Dot Plot or Histogram/Stem & Leaf
- Time Data (e.g., temperature over week) → Line Graph
2. Bad Graphs (10 mins)
Show examples of misleading graphs (e.g., scale not starting at zero, missing labels).
Rules for Good Graphs:
- Title - What is this about?
- Axes - Label X and Y clearly.
- Intervals - Consistent counting steps.
- Labels - What do the bars represent?
Acronym: TAIL
3. Task: Create Your Display (30 mins)
Students create at least one graph for their investigation.
Options:
- Draw by hand on graph paper (focus on precision).
- Use Google Sheets/Excel to generate a chart.
Challenge: Write one sentence below the graph describing what the "tallest bar" or "biggest slice" means.
📋 Teacher Planning Snapshot
Ngā Whāinga Ako — Learning Intentions
Ākonga choose a display that fits the question, and see how a display can mislead.
Ngā Paearu Angitū — Success Criteria
- ✅ I can choose a display that suits my data and label it properly.
- ✅ I can explain how a display could mislead someone, using a real example.
Differentiation & Inclusion
Scaffold support: Three display types on one page with the same data, so the choice is visible. Extension: make a display that misleads, then say exactly how it does it.
ELL / ESOL: Label the axes aloud together before ākonga build anything.
Inclusion: Hand-drawn displays count fully; the reasoning is what is assessed, not the software.
Mātauranga Māori lens: A display is an argument about what matters. Ngā tohu o te taiao work the same way — the signs chosen for attention are the ones held to matter.
Prior knowledge: Students should have basic familiarity with data displays (bar graphs, dot plots). No prior statistical investigation experience required — the PPDAC inquiry cycle provides accessible scaffolding for first-time investigators.
Curriculum alignment
- Te Mātaiaho (2025) · Mathematics and Statistics · Phase 3 (Years 7–8) · Statistics (Practices): “- For a given set of data, choosing and constructing an appropriate data visualisation according to the data type (e.g. a dot plot, bar graph, time-series graph) - Noticing and explaining outliers in a given set of data”
- NZC (2007) · Mathematics and Statistics · Level 4: “Plan and conduct investigations using the statistical enquiry cycle: – determining appropriate variables and data collection methods; – gathering, sorting, and displaying multivariate category, measurement, and time-series data to detect patterns, variations, relationships, and trends; – comparing distributions visually; – communicating findings, using appropriate displays.”
This lesson develops the Level 4 statistical-investigation objective through a real stage of the PPDAC cycle. See the unit curriculum companion for the exact source statement and lesson-to-evidence map.