Teaching sequence

Work through this resource material in the following sequence:

10 minutes

Tune In: What is good data?

25 minutes

Find Out: Applying sampling techniques

20 minutes

Take Action: The impacts of ethical data collection

5 minutes

Reflection

Learning intentions

Students will:

understand how sampling and data collection methods influence representation and potential bias in datasets
analyse real-world examples and reflect on the ethical responsibilities involved in collecting and using data.

Success criteria

Students can:

explain the difference between random and non-random sampling
identify when a sample may not represent a population suggest ways to make data collection more inclusive and ethical
describe how missing data can influence decisions in areas such as health, policy and funding.

Tune In: What is good data?

Step 1

Provide students with the Student Worksheet and read the statement in question 1 aloud: ‘Everyone in our school prefers watching footy to soccer’.

Ask students to use the ‘Claim-Support-Question’ thinking routine to unpack the statement and then use the suggested responses to facilitate discussion.

Claim	What is being stated as true?	This statement claims that all students at the school prefer footy over soccer.
Support	What evidence or data would support or challenge this claim?	To support or challenge this claim, we would need data from students at the school, such as a survey on sport preferences. The data would need to include a wide range of students to be fair.
Question	What questions does this claim raise, and what might be missing?	Who was asked? How many students were surveyed? Are other sports or preferences being left out?

Step 2

Explain that good data avoids assumptions and aims to represent everyone, not just the loudest or most visible groups. Emphasise that collecting data requires careful planning to ensure that the information you collect actually answers the questions you have.

Using the Australian Census as an example, highlight the importance of collecting data in a way that accurately reflects Australia’s population, including differences in gender, culture, age, family structures, income, and socio-economic backgrounds.

Find Out: Apply sampling techniques

Step 1

Ask students to stand up. Explain that you will collect data using a sample to answer a simple question, such as “What is your favourite type of music?” Clarify that a sample involves collecting data from a subset of people to understand a larger group.

Tell students that the first sample will be non-random, and you will choose participants based on convenience rather than chance. Select the five students who are closest to you, and record their responses on the board.

Step 2

Next, explain that you will take another sample, but this time using a random method, so everyone has an equal chance of being selected. Use a number generator, wheel spinner or name sticks to choose five students. Record these responses alongside the first set of data.

Step 3

Compare the two sets of results as a class using the following prompts:

Do the samples represent the whole class? Why or why not?
Why might the two samples look different?
What makes a sample fair?

Reiterate that this activity reflects real-world data collection. A non-random ‘sample of convenience’ can overlook diverse perspectives, mirroring bystander behaviour by ignoring who is missing. An Upstander approach insists on fair methods to ensure all voices are represented. Large-scale collections like the Australian Census aim to do this, supporting more accurate and inclusive decisions.

Step 4

Explain that the dataset students will use in this part of the lesson (Question 2 of the worksheet) represents a small population, similar to how Australia’s population is represented in the Australian Census. Emphasise that Census data is self-reported, meaning people choose how they describe themselves. Highlight why this matters ethically: self-reporting respects individual identity, allows people to represent themselves accurately, and avoids assumptions about someone’s background, culture, or experiences.

Step 5

Provide students with the Data Sample Factsheet showing a fictional, self-reported diversity dataset and direct students to Question 2 of the Student Worksheet. Explain that the dataset includes information such as ethnicity, gender, religion, place of birth and language spoken at home, and that it has been designed to reflect a range of identities and experiences.

Step 6

Explain that students will work with a random sample from this dataset. Clarify that random sampling gives everyone an equal chance of being selected, making it fair, but that small samples may not fully reflect the diversity of a large population like Australia's.

Distribute pre-cut rows from the dataset so that each pair receives 10 random ‘person cards’. Ask students to sort their cards into categories of their choice, such as gender, language spoken at home or place of birth.

Step 7

Ask students to choose one category and create a bar graph to represent their data. Explain that this graph represents their group’s ‘snapshot’ of Australia based on a small sample.

Learning support: Provide students with sentence stems for each question on the Student Worksheet. We have provided these in the Teacher Content Information.

Learning extension: Invite students to investigate how different sampling methods (systematic, stratified, cluster) influence the type of data collected. See the Teacher Content Information for ways students can apply these methods to their data set, and for reflection questions to use with the students when considering the impact of the method on the results.

Step 8

Have students compare their graph with another group’s graph and consider:

how similar or different their snapshots of Australia are
which groups appear strongly represented
which groups have a low representation or are not represented at all.

Use this discussion to reinforce that small samples can tell different stories, even when taken fairly, and that large-scale data collection, like the Australian Census, is needed to better represent the whole population

Take Action: The impacts of ethical data collection

Step 1

Pose the following question to the class: ‘What happens when important viewpoints or communities are missing from data?’

Invite students to share their thinking and build on each other’s ideas. Encourage them to refer back to earlier sampling activities and Census discussions. Reinforce that collecting accurate, inclusive data is one way communities can plan more fairly and ensure everyone’s needs are recognised.

When data is incomplete, biased or collected unfairly, the decisions based on it can also be unfair. Funding, health services, education support, and community programs may not reflect the true needs of the population. As a result, some groups may be overlooked or disadvantaged, not because they are unimportant, but because they were not fully represented in the data.

Step 2

Divide the class into three groups and assign each group to one of the case studies in the Ethical Data Collection Case Studies Factsheet. Instruct students to read their background information and case study to consider the impact of sampling and inclusion on large datasets. Encourage pairs within the group to discuss and answer Question 3 together before formulating their 1-minute summary.

Step 3

Create new groups of three consisting of one student from each case study. Ask students to share their 1-minute summaries with each other and complete Question 4 together. Encourage students to identify patterns in who is included or excluded and to consider how these patterns could influence how data is planned and collected in the future.

Remind students to consider factors such as age, gender, cultural background, location, language, disability, income, digital access, and trust. Encourage them to consider what barriers might prevent different groups from participating and how those barriers could be reduced.

Step 4

Facilitate a class discussion about how the case studies demonstrate that data depends on who is counted. When certain groups are excluded, whether intentionally or unintentionally, the results can change, and so can the decisions made from that data. Encourage students to consider the practical challenges of collecting data and the broader implications of how it is gathered, represented and used.

Learning extension: First Nations Data Sovereignty (optional)

Data affects people’s lives, communities and futures. First Nations Data Sovereignty means Aboriginal and Torres Strait Islander peoples have the right to decide what data is collected about them, how it is used and who controls it. When communities have this control, data is more accurate and useful, helping improve services and decision-making. Without it, data can be misused or reinforce unfair systems.

With your students, explore the following webpage from ANTAR and answer the following questions

Why is it important for communities to have control over their own data?
How could community involvement improve the accuracy of surveys?
What might happen if data is collected about a community without their input?
How does data sovereignty connect to fairness and representation?

Reflection

Have students answer the following:

Why should we identify when someone else (or another community that we don’t belong to) is not being heard?
How can we be Upstanders and advocate for the inclusion of diverse perspectives?
How can we make sure we create space for diverse voices in our own data collection?

The Ethics of Data Collection

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Summary

Learning intentions

Success criteria

Lesson Guides and Printables

Lesson Details

Skills Required

Curriculum Mapping

Resources Required

UN Sustainable Development Goals

Additional Information

Related Professional Learning

In this lesson