Data Science for Young Minds — Grade 3
| Topic | Details |
|---|---|
| Truncated y-axis | Truncated y-axis: starting the scale at a number other than zero |
| Unequal bar widths or stretched scales | Unequal bar widths or stretched scales |
| Missing labels and titles that hide impo | Missing labels and titles that hide important information |
| Activity | Activity: spot the trick in 5 misleading graphs |
| Why 3 people is not enough to make concl | Why 3 people is not enough to make conclusions about 300 |
| How sample size affects reliability | How sample size affects reliability |
| The coin flip example | The coin flip example: small samples give weird results |
| When is a sample big enough? | When is a sample big enough? |
| What bias means | What bias means: a built-in unfairness that skews results |
| Selection bias | Selection bias: who you chose to ask |
| Question bias | Question bias: how you worded the question |
| Confirmation bias | Confirmation bias: only seeing data that supports what you already believe |
| Questions every data detective asks | Questions every data detective asks: Who collected this? Why? How? How many? |
| Red flags | Red flags: no source, tiny sample, emotional language, missing context |
| The difference between data that informs | The difference between data that informs and data that persuades |
| Activity | Activity: evaluate 3 real-world data claims |
Learn to spot graphs that look convincing but actually distort the truth.
Learn why asking just a few people can give completely wrong results.
Explore how the way data is collected can build in unfairness from the start.
Put your critical thinking skills together. Evaluate real-world data claims like a detective.