Whakataukī | Proverb
"Ehara taku toa i te toa takitahi, engari he toa takitini"
Success is not the work of one, but the work of many.
Data tells stories when we work together to interpret patterns and share insights. Like our tīpuna who used collective observations to understand their world, we gather data points from many sources to create meaningful graphs that reveal truths about our communities.
📋 Level 4 Achievement Objectives:
- • Collect data using simple frequency tables
- • Display data using bar graphs with appropriate scales
- • Interpret data by reading values and comparing categories
- • Make statements about patterns and relationships in the data
- • Ask questions that can be answered using the data
🍕 Graph 1: Mangakōtukutuku College Students' Favourite Lunch Options
1. What is the most popular lunch choice at our school?
2. How many more students prefer sandwiches than sushi?
3. What percentage of students chose pizza? (Show your working)
🏃 Graph 2: Mangakōtukutuku College Sports Teams (Year 8-10)
1. Which sport has the most students participating?
2. How many students are in the rugby team?
3. What is the total number of students in all sports teams?
📚 Graph 3: Mangakōtukutuku College Library Book Borrowing (This Term)
1. Which book genre is borrowed most often?
2. How many more fiction books were borrowed than non-fiction?
3. What is the total number of books borrowed this term?
✏️ Level 4 Challenge
Write a question about our school data that could be answered by collecting more information:
📋 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.