SectionRecognizing Bias and Developing Ethical Data Thinking
One of the most important data dispositions is healthy skepticism. Not all data tells the complete story, and students need to develop the habit of asking: “What might this data be missing? Who collected this and why?” Additionally, when we collect information about people, we have responsibilities.
The Scenario: Jamie wants to find out what the most popular playground activity is at their school. They survey 20 students during recess and find that 15 out of 20 say “basketball” is their favorite.
What questions should we ask about this conclusion? Where did Jamie conduct the survey? When did they survey? Who did they ask? How did they ask the question? What other information might be helpful?
Location bias is the biggest issue here. If Jamie surveyed students who were playing basketball or near the basketball court, those students would be much more likely to say basketball is their favorite activity. This shows how WHERE we collect data can significantly influence the results, even with good intentions.
Random selection from class lists would give every student an equal chance to participate, regardless of which playground activity they prefer. Jamie could also survey at different times and locations, but random selection is the most systematic approach to reducing bias.
The Activity: Present any data visualization or claim to your students. Ask them to put on their “detective hats” and investigate using age-appropriate questions.
Detective Questions for Secondary: What was the source of this data? What was the sample size and method? What might be missing from this picture? Who benefits if people believe this claim? What additional information would help us evaluate this?
Understanding that bias is normal and usually unintentional helps students approach data with healthy skepticism rather than cynicism. Most data collectors aren’t trying to mislead—they’re just limited by practical constraints. Teaching students to ask good questions about data sources and methods helps them become better consumers and creators of information.
When we collect information about people, we have responsibilities. This is true whether students are surveying classmates about lunch preferences or analyzing historical census data.
Elementary Version - Our Data Promise: We will ask before collecting information about each other. We will keep personal information private. We will be kind when sharing what we learn. We will ask for help if we’re unsure about something.
Secondary Version - Data Ethics Guidelines: Obtain informed consent before data collection. Anonymize data when sharing results publicly. Consider potential harm or embarrassment to individuals. Be transparent about data collection methods and purpose. Respect the right to withdraw participation.
Your class surveys students about how they get to school. One student mentions their family doesn’t have a car. What should you do when sharing results with other classes?
The goal is to protect individual privacy while still sharing useful information about transportation patterns. You could say “Most students ride the bus, some walk, and a few get rides” without identifying specific students or their family situations. This teaches students to think about the impact of data sharing on real people.
