The materials were developed in conjunction with and trialled by Auckland Girls’ Grammar School, Lynfield College, and Northcote College mathematics and statistics departments.����

The summary investigation lessons are based on students undertaking a statistical enquiry to find out about the class or year level.�� Lessons 1, 2, 5, 6, 9, 10, 11 broadly follow a statistical enquiry using the PPDAC cycle; this is noted in each lesson.�� Lessons 3, 4, 7, and 8 are concept development lessons, timed to allow for data collection and data entry across a year level cohort for the statistical enquiry.

This teaching sequence covers the following statistical concepts:��

1. 1. investigate multivariate data situations for observational studies by
      
      1. exploring areas of interest (Lesson 1)
      2. posing summary investigative questions (Lesson 7, 9)
      3. make conjectures or assertions about expected findings (Lesson 4, 7, 9)
   2. plan how to collect or source data to answer investigative questions, including��
      
      1. identifying the variables needed to answer the investigative question�� (Lesson 2)
      2. planning how to make valid and reliable measures for the variables (when collecting) or finding out how they were collected (when sourcing) (Lesson 2, 7)
      3. identifying the group of interest or who the data was collected from (Lesson 2, 7, 9)
      4. using a set of interrogative questions that check the different ethical practices that should be considered through the entire statistical enquiry cycle, including checking data collection and survey questions before testing with peers (Lesson 2)
   3. collect or source data including (Lesson 6)
      1. making decisions about the validity of data and making simple edits (cleaning data) if appropriate (Lesson 5)
      2. creating a data dictionary (collected data) or finding the metadata (sourced data) (Lesson 2, 5, 7)
   4. create, describe and reason from data visualisations to support answering the investigative question, including
      1. using multiple visualisations to provide global and local views of the data (Lesson 3, 4, 6, 7, 9, 10)
      2. identifying relevant features in distributions (Lesson 3, 4, 6, 7, 8, 9, 10) – interweaving the context in the description of the distribution��
   5. communicate findings, using evidence from analysis, provide possible explanations for findings, reflect on�� conjectures or assertions, and evaluate the approach for the different phases of the statistical enquiry (Lesson 11)
   6. examine the data-collection methods and findings of others’ statistical investigations to see if their claims are reasonable, and critically consider data visualisations to see if they support or misrepresent the data (Lesson 12)

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Data cards are a way of storing data about a person, object or non-physical entity. When using data cards each individual data card represents one person, object or non-physical entity. Data information for each person, object or non-physical entity is recorded in the same way to make future analysis more straightforward. Information from data cards can also be recorded into a spreadsheet for analysis to be made using statistical software.

Data cards can provide secondary data, as many of the examples given here show.�� They can also be used for collecting data, CensusAtSchool data collection information usually has a data card for students to record their measurements on. The data card shown here is from the 2025-2026 CensusAtSchool questionnaire primary teachers guide page 5.

Year 1-3 Data Cards

Three sets of data cards with 7 or 8 variables. The 25 “students/children” are from the CensusAtSchool 2023 database. They are a random selection of year 3-6 students/children from across Aotearoa New Zealand, and the same students/children are used in the three sets of data cards.

Teaching and learning activities associated with the year 1-3 data cards are .

Year 4-6 data cards

The year have 14 variables. The 74 “students/children” are from the CensusAtSchool 2025-2026 database. They are a random selection of year 4-6 students/children from across Aotearoa New Zealand.

Variables included:

• Height
• Gender �� �� �� �� �� �� �� �� �� �� ��
• Hair colour�� �� �� �� �� �� �� �� ��
• Eye colour
• Languages spoken
• Favourite colour��
• Year level
• Plays a musical instrument
• Has pets
• Has broken a bone
• Travel method to school
• Time taken to get to school
• Left foot length
• Right foot length

Teaching and learning activities associated with the year 4-6 data cards are .

Year 7-8 data cards – to come

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These lessons are created for students grade 4-ish and above in partnership with Stanford Women’s Basketball. One of the goals of these lessons is to show students that data is everywhere – you don’t have to be an athlete to be involved in sports. Sports Data Analytics is a huge field. Teams have statisticians and coaches that analyze data to support recruiting, practice decisions, and strategies for players and ways to be ready for opposing teams. Even the players need to understand their and their opponents data. In this unit of lessons, students ask questions, explore, visualize and make decisions with data inside basketball.

Note: Any teacher can teach these lessons – you DO NOT have to know basketball. All the information needed is in the videos made by Stanford basketball players. Students will happily lead the discussions, and explain the rules to each other as needed.

There are three parts to this sports data analytics unit of work:

• In Part 1 students are introduced to basketball data through video made for them by Stanford women’s basketball team, through a data recording activity and through playing a version of the game at their tables.
• In Part 2 students are introduced to CODAP, a cool data visualization tool, to consider the height of players using dot plots, histograms and mean, median and mode.
• In Part 3 students conduct a data investigation with the goal of choosing their dream team! Students pick a five player team from all ACC & PAC-12 players from last year, justifying their choices using statistics and graphs. They will work in CODAP to choose, analyze and communicate data, and then defend their choice of players.

In partnership with Stanford Women’s Basketball, CBB Analytics, and the Sports Analytics Club Program.

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Statistics Teachers’ Day takes place at Auckland University, where stats education researchers, PhD students, and practising statisticians share what they are discovering in data. Teachers also share their classroom practice and resource development, modelling excellent teaching and learning with and about data on the day.

Resources

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Dinos of patagonia data cards

Variables:

The data is also available in a CODAP document |

You can use the blank data cards to find out information about other dinosaurs. If you want to add the new dinosaurs to the dataset, then make a copy of this and add them in.

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Students use the PPDAC cycle to undertake statistical and probability investigations. This unit of work explicitly looks at making valid and reliable measurements and considers the different sources of variation that are present in data, and students design and explore probability distributions for real data about themselves.

Session 1: In this session, students explore real variation and apply the��statistical enquiry cycle (PPDAC)��to a summary situation using measurement data, sourcing data from the latest CensusAtSchool database, and collect some data from themselves.

Session 2: ��In this session students explore induced variation due to measurement and accident and dig deep into planning what to measure and how (PLAN).

Session 3: In this session, students explore induced variation from sampling, developing the concept of sampling variability using samples of size 30 (ANALYSIS).

Session 4: In this session, students design and explore probability distributions for real data about themselves. In this session they pose a chance-based investigative question (PROBLEM), PLAN to collect data (experimental estimates of probabilities) and then collect and record the DATA by undertaking the probability experiment.

Session 5: In this session, students design and explore probability distributions for real data about themselves. In this session they ANALYSE the data, answer the chance-based investigative question and communicate their findings (CONCLUSION). This session continues work carried out in session four.

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Document with all three year plans

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Vaires-sur-Marne Nautical Stadium

A set of data cards for the 2024 New Zealand Olympic Team – 32 athletes who are competing at the Vaires-sur-Marne Nautical Stadium. The data was sourced from the .

Find out about individual athletes by typing their names in the search bar. You can filter by Games Type (Olympic Summer Games), and Sport to find the athletes in a sport or group of sports.��

The will host the Olympic rowing and canoe-kayak events.

See information about using data cards for ideas on what to do with the data cards. Other ideas include:

• Make a set of data cards for another sport or group of sports.
• Make a set of data cards for a
• Add variables to the existing set of data cards, e.g., the Olympian number, where they are based, did they compete in the Youth Olympics, what year was their Olympic debut

All the data that is in the data cards is also in the CODAP document listed in the resources.

Variables in the data card set provided:

This activity explores the following key ideas:

• Using existing data to undertake a statistical enquiry

Resources

• Teaching Notes
• Olympics NZ – Rowing, Canoe Kayak Sprint, Canoe Kayak Slalom data cards
• – Olympics NZ – Rowing, Canoe Kayak Sprint, Canoe Kayak Slalom
• Blank data cards – make your own set of NZ Olympic Athlete data cards

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The following interrogative questions provide a good starting point to understand the data, what was collected, how it was collected and who it was collected from (Arnold, 2022, p. 90).

Overall for the dataset:

1. Was the data collected using an observational study or an experiment (from year 9)? (1. Method)
2. Who was the data collected from? (2. “Who”)
3. Who collected the data? (1. Method)
4. When was the data collected? (1. Method)��
5. Where was the data collected? (1. Method)
6. What was the purpose for collecting the data? (Initial investigator’s problem/purpose)

Specific to the variable (3. What and how):

1. State the variable.
2. What was the data collection or survey question asked to collect the data?��
3. How was the variable measured?
4. What are the units, if any, for the variable?
5. What are the possible outcomes for the variable?
6. What type of data is it? Categorical or numerical?

Arnold, P. (2022). Statistical Investigations | Te Tūhuratanga Tauanga. NZCER Press.��

The at North Carolina State University have created this that provides an expanded set of interrogative questions when using data from other sources.

In our modern society, data is generated all the time and in various ways. Sometimes we create our own data from experiments, surveys, etc. More often, we use data generated from other sources, available online. At this time, data is even generated automatically, as in click-log data and other metadata, collected as we go about our daily lives. But all data has context. To gain a deeper understanding of data from other sources, you must examine the context. The questions below provide guidance to make sense of a dataset. You do not need to answer each of these questions. They are intended to guide you in developing a data interrogation mindset, wherein a good understanding of data and its sources will inform your analysis and claims made with the data.

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