Studio checkpoint: clean the working dataset
Teams preserve the raw data, create a working copy, and document every correction, exclusion, category decision, or missing value. A partner must be able to reproduce the cleaned table from the log.
- Studio move: Dataset versioning, cleaning log, and reproducibility check
- Portfolio evidence: Clean frequency table with a transparent decision record
🎯 Learning Intentions
- Clean data by removing errors or unclear responses
- Sort data into categories
- Use frequency tables to count responses
- Introduction to digital spreadsheets (optional)
🎥 Media Anchor (8 mins)
Video: Research Skills for Students
- Which organisation method makes pattern-finding easiest?
- How can poor data organisation distort your conclusions?
1. The Messy Desk Metaphor (5 mins)
Discuss: "Why is it hard to find a specific paper on a messy desk?"
Raw data is like a messy desk. Organising it helps us find the answers.
2. Activity: Cleaning Data (10 mins)
Look at your data set. Are there any issues?
- Did someone write "dog" when you asked for a number?
- Did someone answer twice?
- Are there blank spaces?
Task: Fix clear errors or decide to remove "spoiled" data entries.
3. Skill: Frequency Tables (20 mins)
Turn a list into a count.
Raw List:
Red, Blue, Red, Green, Blue, Red...
Frequency Table:
- Red: ||| (3)
- Blue: || (2)
- Green: | (1)
Task: Create frequency tables for your own survey questions.
4. Introduction to Google Sheets/Excel (15 mins - Optional)
Demonstrate how to enter data into a spreadsheet:
- One row per person
- One column per question
- Using "Sort" to group answers
📋 Teacher Planning Snapshot
Ngā Whāinga Ako — Learning Intentions
Ākonga organise raw data and discover that how you group it decides what you can see.
Ngā Paearu Angitū — Success Criteria
- ✅ I can sort and group data in a way that suits my question.
- ✅ I can say what my grouping hides as well as what it shows.
Differentiation & Inclusion
Scaffold support: A sorted and an unsorted version of the same small dataset, so ākonga see what organising buys them. Extension: organise the same data two ways and say which reveals more.
ELL / ESOL: Teach 'sort', 'group', 'category' by physically moving cards before touching a spreadsheet.
Inclusion: Physically sorting cards or objects is a legitimate route to the same understanding as a spreadsheet.
Mātauranga Māori lens: Grouping is an act of interpretation, not a neutral step — how you categorise decides what you can see. Ask ākonga what their grouping hides.
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
- 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.