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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Statistics Teachers’ Day takes place at Auckland University, where stats education researchers, PhD students, and practising statisticians share what they are discovering in data. Many 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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Why? Because data is better than anecdote and “every piece of statistical information needs a representation—that is, a form. Some forms tend to cloud minds, while others foster insight” (Gigerenzer & Edwards, 2003, p. 258).

Who?�� – are an infographic and data visualization agency in Copenhagen. They add value to data and information by telling visual stories that inform and inspire. In other words you send them your data they analysis it, and tell you stuff you may not know already. This is where the saying “Statisticians get to play in other peoples back yards” comes from.

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At CensusAtSchool we get a lot of questions about this question! StatsNZ have developed a flowchart that recognises best practice for questionaire design, regarding posing survey questions and collecting data about sex and gender.�� See the and the full report and findings.

The review was driven by unmet information needs, known issues with existing statistical standards, and significant community interest. Other New Zealand government agencies, including CensusAtSchool, are also seeking guidance on how to best collect such information across broader survey and administrative settings.

StatsNZ were already aware of some issues with the existing statistical standards, particularly limitations in adequately reflecting gender minorities and intersex people. These issues relate to limited options available for respondents to describe who they are and limitations on our ability to meet information needs for different populations by ensuring the data reported can be disaggregated where appropriate. The review of the sex and gender identity statistical standards seeks to address these issues, with a view to providing appropriate guidance for different contexts and information needs.

Statistical offices around the world are facing similar challenges regarding how best to collect information on these concepts, with fast-evolving societal and cultural understandings and languages. It is critical that StatsNZ ensure the statistical standards for sex and gender identity are contemporary, flexible and enduring, and able to meet information relevant to the New Zealand context.

For more information about the review, email��identity@stats.govt.nz.

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