Lesson 6: Displaying Data
Choosing the right graph to tell your data's 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 color) → 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
Students will engage with this resource to develop statistical investigation skills — planning inquiries, collecting and analysing data, interpreting distributions, and communicating findings. Tūhuratanga (investigation) is framed as a tool for understanding our communities and environment in Aotearoa New Zealand.
Ngā Paearu Angitū — Success Criteria
- ✅ Students can identify an investigative question, collect relevant data, and display it clearly.
- ✅ Students can interpret statistical findings and discuss what they might mean for a real-world community or environmental context.
Differentiation & Inclusion
Scaffold support: Provide structured investigation frameworks (PPDAC cycle templates) for entry-level access. Offer partially completed data tables for students who need additional support. Extend capable learners by asking them to critique a statistical claim from a news article, or to design their own community data investigation.
ELL / ESOL: Pre-teach statistical vocabulary (median, mode, range, distribution, sample, population). Pair visual representations (graphs, tables) with plain-language explanations. Allow students to discuss statistical ideas orally before writing. Encourage use of home language for initial sensemaking.
Inclusion: Statistical investigation offers natural differentiation — all students can engage with the same real-world question at different levels of mathematical complexity. Neurodiverse learners benefit from structured, step-by-step investigation processes. Use collaborative group investigation formats that distribute roles (data collector, recorder, analyst, presenter).
Mātauranga Māori lens: Tūhuratanga — the practice of careful investigation — resonates deeply with mātauranga Māori. The maramataka is a sophisticated data system: tracking environmental patterns, seasonal cycles, and ecological indicators over generations. Iwi environmental monitoring — counting kaimoana populations, tracking water quality, observing bird migrations — is applied statistical thinking. Framing statistics within community and environmental inquiry connects data to mana whenua responsibilities.
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
- Statistics — Statistical Investigation: 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 display.
- Statistics — Probability: Investigate situations that involve elements of chance by comparing experimental distributions with expectations from models of the possible outcomes, acknowledging uncertainty.