📋 Teacher Cheat Sheet — Session 2: Asking Good Questions

Data Science for Young Minds · Grade 3 · Ages 8–9
~60 min Ages 8–9 Session 2 of 8 ND-Friendly
⏱ Session Agenda
TimeBlockWhat's Happening
0–5🔁 Warm-UpShare take-home observations. "Did anyone find a pattern?"
5–18📖 Lesson 1–2Data questions vs. opinion questions · Open vs. closed · Leading/biased questions
18–35🎮 ActivityQuestion Sort Game — 20 cards, two piles: data can / data can't answer
35–48📖 Lesson 3–4Who you ask matters — sample, sample size, representative sample
48–55✍️ ActivityFix the Bad Questions — students rewrite 3 biased questions
55–58🔁 Recap"What makes a question data can answer?"
58–60👋 ClosePreview Session 3: "Now we'll actually collect the data!"
Key insight to land: Bad questions don't give you useful data. The question design is as important as the data itself — and this is true in ALL of data science.
📦 Materials Needed
20 question cards (printed or on index cards — see below) 2 labeled bins/areas: "Data CAN answer" · "Data CANNOT" Pencils Student worksheets
💡 Write question cards on index cards the night before. Or print the worksheet's question sort page and cut into strips.
📚 Key Vocabulary
Data question — answerable with counting, measuring, or observing
Opinion question — answered with feelings/judgments, not data
Open question — lets people answer freely (any answer)
Closed question — gives set choices (yes/no, A/B/C)
Biased/leading question — pushes toward one answer
Sample — the group of people you actually ask
Sample size — how many people; bigger = more reliable
Biased sample — only asking certain groups
💬 Discussion Questions + Teacher Notes
  • "What is the best pizza topping?" — Can data answer this?
    → No — "best" is an opinion. BUT "What is the most popular pizza topping in our class?" IS a data question. Show the transformation.
  • "Isn't our school the greatest?" — What's wrong with this question?
    → It's leading — it pressures people to say yes. A fair question: "How would you rate our school: great, okay, or needs improvement?"
  • "If I only asked the soccer players what their favorite sport is, what might happen?"
    → Biased sample! The results wouldn't represent the whole class. Who you ask matters as much as what you ask.
  • "Is asking 3 people enough? What about 30?"
    → Small samples = unreliable. If 2 out of 3 say yes, that's 67%. If 20 out of 30 say yes, that's also 67% but much more trustworthy.
  • "What question would YOU want to ask your class?"
    → Invite genuine curiosity. Then ask: "Is that a data question? How would you make it fair?"
🎮 Question Sort Game — Setup
Groups of 3–4. Each group gets 20 question cards. Sort into two piles: data CAN answer / data CANNOT answer. Then discuss disagreements.
Sample question cards — Data CAN answer ✅:
How many students in our class have a pet?
What is the most common eye color in our class?
How many minutes do students spend on homework each night?
Which fruit do most students prefer: apple, banana, or orange?
Data CANNOT answer ❌:
What is the best movie ever?
Is math fun?
Should we have longer recess?
Why is the sky beautiful?
💡 Gray-area cards (like "Is math fun?") ARE the lesson. Let groups disagree — then show how rewording turns it into a data question.
🎯 Opening Warm-Up
Ask 2–3 students to share their take-home observations from Session 1.
Then say: "Today we take that observation skill and turn it into a question — one that data can actually answer."
→ Bridges Session 1 → Session 2 explicitly.
✍️ Fix the Bad Questions
Write on board:
"Rewrite each question to make it fair, specific, and answerable with data."
Bad: "Isn't pizza the best lunch?"
Fixed: "What is your favorite lunch option: pizza, sandwich, or salad?"
Use worksheet Part 3. Pairs or individuals.
🧠 ND-Friendly Tips
  • The gray-area questions — Some students get anxious when there's no clear right answer. Reassure: "It's okay to disagree — that's how data scientists think."
  • Pair for the sort — Talking through cards is easier than deciding alone. Pairs reduce pressure.
  • Anchor with examples — For each new concept, give one crystal-clear example BEFORE asking students to generate their own.
  • Sample size — Use a concrete analogy: "If 1 person says they like broccoli, does that mean everyone does?"
  • Preview the sort — Show one "data can" and one "data can't" card before releasing the full 20.
Data Science for Young Minds · Grade 3 · Session 2 Cheat Sheet · sdabagh.github.io/learn/data-science