Data questions
Data questions must be answerable by the data you actually have. Half of analysis is narrowing the question until it is.
AI for Kids · Class 9 · Module 4 of 9
Data Analysis with AI
The data is fine. The conclusion is where things usually go wrong.
8 sessionsSessions 25–32
4 weeksTypical pace
AppliedLevel
Class 9Artificial Intelligence
The big idea
Students frame answerable questions, use AI to explore patterns and outliers, and practise stating conclusions with appropriate caution — including saying when the data simply cannot answer the question.
Why it matters
Drawing responsible conclusions is the hardest and most valuable part of data work, and the part most often skipped.
Unpacked
What you will actually learn
Three core ideas, each taught with worked examples and then practised until it feels obvious.
Patterns and outliers
Patterns and outliers both matter. An outlier can be an error, or it can be the most interesting thing in the dataset.
Responsible conclusions
Responsible conclusions state what the data supports, what it does not, and what would be needed to be more certain.
Did you know?
Anscombe’s quartet is four datasets with identical means, variances and correlations that look completely different when plotted. Always plot the data.
Common mix-up
“The AI analysed it, so the conclusion is sound.” AI describes patterns. Judging what they mean remains entirely human work.
Session by session
Your 8-session journey
Sessions 25–32 of the 72-session year, at two one-hour sessions per week.
- 1
Warm-up and big picture
Where this module fits, what you will build, and a hands-on starter that gets everyone curious about data questions.
- 2
Data questions
Guided teaching on data questions, worked through together with the teacher.
- 3
Data questions — practice lab
Independent practice, small challenges and one deliberate mistake to diagnose.
- 4
Patterns and outliers
Guided teaching on patterns and outliers, building directly on the previous two sessions.
- 5
Patterns and outliers — practice lab
Applied tasks that combine data questions and patterns and outliers in one piece of work.
- 6
Responsible conclusions
Responsible conclusions introduced and practised, completing the toolkit needed for the project.
- 7
Project build
Guided build session for the module project: School-community data story.
- 8
Test, present and reflect
Finish, test against the checklist, present the work and explain the decisions behind it.
Project lab
School-community data story
Every module ends with something the student built themselves and can demonstrate. This is the piece that goes into their portfolio and gets explained out loud at the end of session 32.
Try this at home
Write your conclusion, then write the strongest honest objection to it. If you cannot answer the objection, soften the conclusion.
It is finished when
- It works from start to finish without breaking
- You can explain every part of it in your own words
- You tested it and improved at least one thing afterwards
- Someone else used or understood it without your help
By the end of this module
Students finishing Module 4 can:
- Explain and use data questions without prompting
- Explain and use patterns and outliers without prompting
- Explain and use responsible conclusions without prompting
- Build and finish school-community data story
- Test your own work and correct what you find
- Talk an adult through what you made and why
Word bank
The vocabulary introduced here, in plain language:
- outlier
- A data point far from the rest.
- confounding
- A hidden factor influencing both variables.
- uncertainty
- The honest range of doubt around a conclusion.
Module challenge
Think you have got this?
0 XPLevel 1 · Curious Beginner
6 quick questions drawn from this module — vocabulary, the project you build, and a myth-or-fact round. Every wrong answer explains itself, so a mistake still teaches you something.
Your child's free trial is one message away.
Tell us your child’s class and what they enjoy. We will suggest the closest program fit—no pressure and no upfront payment.