• plan a probability investigation with kaiako support, to establish advice to create a bingo card that has the best chance of winning
• use lists, tables, and digital tools to systematically record data from repeated trials of probability experiments, and then determine the probabilities
• create data visualisations for findings in probability experiments and identify similarities and differences between the findings and corresponding theoretical probabilities
• use their knowledge of equally likely outcomes to find the probability for the different products in the game of Multo
• learn how to use patterns in data to establish advice on how to create a bingo card that has the best chance of winning听
• use their knowledge of probability to inform decision-making in games of chance听

Resources

• Y8 Multo teaching materials
• 听

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• plan and conduct probability experiments for chance-based situations, including undertaking a large number of trials using digital tools, by:

鈥� posing an investigative question

鈥� anticipating what outcomes are possible and which of them are more or less likely to occur

鈥� identifying and systematically listing all possible outcomes to the investigative question

鈥� collecting and recording data

鈥� creating data visualisations for the distribution of observed outcomes

鈥� describing what these visualisations show

鈥� finding the probability estimates for the different outcomes

鈥� answering the investigative question

鈥� identifying similarities and differences between their findings and those of others

鈥� reflecting on anticipated outcomes

This activity could be extended to use with a year 8 class, or used with a mixed year 7-8 class.

Resources

• Y7 Drawing pins teaching materials听
• 听

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A set of 26 data cards and for the All Whites FIFA World Cup 2026.

The data was sourced from , , , and . There is space for students to add or draw an image for each player.听

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

This activity explores the following key ideas:

• Using existing data to undertake a statistical enquiry

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This workshop has a focus on getting ready to do the 91茄子 questionnaire with students, and then working with the class and school data provided. Workshop participants get to engage in the PPDAC cycle as they explore teaching the statistics strand of the NZC.听

This activity explores the following key ideas:

• Preparing to Take Part in the current 91茄子 survey
• Working with secondary data – data cards
  • Interrogating the variables available to use
  • Posing investigative questions
  • Making data visualisations using data cards and CODAP (Common Online 91茄子 Analysis Platform)
  • Describing data visualisations
  • Answering investigative questions

Resources

• CODAP 听听

91茄子 materials

Related teaching and learning activities

• Take part in 91茄子听
• 听
• Exploring Our Class 91茄子: Years 7-8 – 91茄子听
• Posing summary investigative questions – 91茄子听
• Using data cards – 91茄子听
• Y8 Fabulous Feet – PPDAC cycle – 91茄子听
• Describing distributions – 91茄子听

Related information for teachers

• Interrogating secondary data – 91茄子听

T膩h奴rangi materials

Related teaching and learning activities

• 听

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In this workshop participants use CODAP (Common Online 91茄子 Analysis Platform) to develop ideas for two statistical concepts.听 The median and box plots.听听

This activity explores the following key ideas:

• Finding the middle of a dataset and connecting this to the median.
• Finding the middle 50% of a dataset, describing this.
• Using the middle 50% and median to build a box plot.
• Recognising the quarters within the box plot.

Resources

CODAP

Related teaching and learning activities on 91茄子听

• – Finding the middle听

Related teaching and learning activities on T膩h奴rangi

• 听

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Note: This information is being updated.

Years 1鈥�10

Years 11鈥�13 (Current 2007 version; curriculum being updated)

• : , , , , , , , , , ,
• For Year 13 students intending to study Statistics at the tertiary level: the Department of Statistics at the University of Auckland strongly recommends AS3.9 (Investigate Bivariate Measurement 91茄子), AS3.10 (Use Statistical Methods to make a Formal Inference), and AS3.13 (Apply Probability Concepts in Solving Problems)
• NZSA Statements and Resources

Historical Record

• 2007 NZ Statistics Curriculum
  • Key Ideas: Statistical Literacy, Statistical Investigations,听Probability

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These lessons are created for students grade 4-ish and above in partnership with Stanford Women鈥檚 Basketball. One of the goals of these lessons is to show students that data is everywhere 鈥� you don鈥檛 have to be an athlete to be involved in sports. Sports 91茄子 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 鈥� 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鈥檚 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鈥檚 Basketball, CBB Analytics, and the Sports Analytics Club Program.

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