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It was wonderful seeing so many teachers at last year’s Statistics Teachers’ Day in Auckland. Planning is already underway for this year’s event!

Later this year, we’ll let you know the overarching theme and put out a call for workshops and talks.

In the meantime, save the date:

Statistics Teachers’ Day 2026
Friday November 27

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Note: This information is being updated.

Years 1–10

Years 11–13 (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����), 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,��Probability

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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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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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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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Participants analysed articles using the frameworks provided (). Further online data literacy resources developed for the TFC (Tertiary Foundation Program) at the University of Auckland are also included. They could be repurposed for Level 2/3 Statistics or Scholarship programs as a weekly focus on developing students’ data literacy capabilities.

Have students select and design their own online data literacy resource, then send in resources to share.

Resources

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