Lesson 9: Presenting Findings
Communicating your statistical story clearly and creatively.
🎯 Learning Intentions
- Design a poster or slide deck summarizing the investigation
- Communicate key findings visually
- Present findings to an audience
🎥 Media Anchor (8 mins)
Video: Effective Presentations
- What should an audience understand in the first 30 seconds of your findings?
- How can you explain uncertainty clearly without weakening your message?
1. Layout Design (10 mins)
A good statistical poster flows logically:
- Title: Catchy question.
- Plan: Who did you ask? (Sample size)
- Data: Your best graph(s). Bigger is better!
- Conclusion: The answer and "So what?"
2. Task: Create Final Product (30 mins)
Students assemble their investigation into a final format:
- A3 Poster (hand-drawn or printed elements)
- Google Slides presentation
- Infographic (using Canva etc.)
Focus: Make the Data the hero. The graph should be the first thing people see.
3. Gallery Walk Rehearsal (10 mins)
Stick up posters (or open laptops). Walk around and leave 1 Post-It note on another student's work:
- "I like how you..."
- "Your graph clearly shows..."
📋 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.