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Session 6 of 8 · Grade 3 Data Science
🔍

What Does the Data Say?

Reading charts, spotting patterns, and drawing conclusions
~60 minutes Grade 3 · Ages 8–9 Data Cycle: Interpret
Today's Plan

What We'll Do Today

The Big Idea: Reading a chart is a skill. First you say what you SEE — then what you THINK it means. Both matter, but they are different things.
Warm-Up · 5 min

Gallery Walk

Walk around and look at the charts your classmates made!
Your job
Visit at least 3 charts on the wall
For each one — write ONE thing you notice on a sticky note
Stick your note under that chart
Be specific: "the blue bar is tallest" not just "good chart!"
After the walk — discuss
What was the most interesting chart you saw?
What question would YOU want to ask about someone else's data?
Did any two charts look similar?
Lesson 1

How to Read Any Chart

1
Read the title. What is this chart about? Who was asked?
2
Read the axis labels. What do the categories mean? What does the scale count?
3
Find the tallest and shortest bars. Which category has the most? The least?
4
Look for ties or near-ties. Are any two bars almost the same height?
5
Calculate differences. How much more did the top choice get than the bottom?
6
Write what it means. Use "The data shows…" or "I notice…"
Lesson 2

Observation vs. Inference

👁️ OBSERVATION
What you can SEE directly in the chart
A fact anyone can verify
Uses exact numbers from the chart
Examples:
"Dog received 8 votes."
"Summer is the most popular season."
"Fish and 'No pet' have the same count."
🧠 INFERENCE
What you THINK the data means
Based on reasoning — could be debated
Goes beyond what the chart shows
Examples:
"Kids prefer dogs because they're fun to play with."
"This class might have more outdoor kids."
"If we asked more students, dogs might still win."
Observations = evidence  ·  Inferences = thinking  ·  You need BOTH to understand data
Quick Practice

Observation or Inference?

Favorite School Lunch
Pizza
9
Sandwich
5
Salad
3
Pasta
7
For each statement below, is it an Observation (O) or Inference (I)?
"Pizza got 9 votes." → ____
"Kids in this class don't like vegetables." → ____
"Sandwich and salad together got 8 votes." → ____
"If the cafeteria served pizza more often, kids would be happier." → ____
"Pizza is the most popular lunch choice." → ____
Activity · 20 min

🎮 Chart Detectives!

Your Mission

You will get 3 different charts (on your worksheet)
For each chart, write: 2 observations + 1 inference
Use the sentence frames on the board to help you
Be ready to share one of your statements with the class
Observation frames
"The most common _____ is _____."
"_____ people chose _____."
"The tallest bar is _____, with _____."
Inference frames
"This might mean that _____."
"Based on the data, I think _____."
"I wonder if _____ because _____."
Lesson 3

Patterns and Trends

A pattern = something that repeats or stands out
A trend = a direction the data moves (higher, lower, same over time)
Patterns to look for
One answer is clearly more popular than all others
Two answers are almost tied
Some answers are much lower than others
The data splits roughly evenly
How to describe patterns
"Most students preferred ___"
"Almost no one chose ___"
"___ and ___ were nearly tied"
"The results were spread out / clustered at ___"
Patterns in YOUR data: what stands out in the chart you made in Session 5?
Brain Break · 2 min
🕵️

Observation or Inference?

The teacher shows a simple image or object.

Thumbs UP = if the next statement is an observation

Thumbs DOWN = if it's an inference

(Go fast — no overthinking!)

Lesson 4

Talking About Data Like a Scientist

Scientists use specific language when describing data. Here are the key sentence starters:
Observation
"The data shows that ___."
"___ received the most/fewest responses."
"___ and ___ were equal."
Inference
"This might mean that ___."
"I wonder if ___."
"Based on this, I think ___."
Conclusion
"Overall, the data suggests ___."
"If I had to answer my question, I would say ___."
Activity · 6 min

✍️ Write About YOUR Chart

Use the chart you made in Session 5

Open your worksheet to Part 4
Write 2 observations — facts you can see directly in your chart
Write 1 inference — what you think the data might mean
Write 1 conclusion — if you had to answer your original question, what would you say?
Goal: By the end of Session 6, you can look at YOUR data and say something meaningful about it — using real data science language.
Going Further

Comparing Two Charts

Sometimes data scientists compare two groups to see if they're different. This is called comparison.
Class A — Favorite Season
Summer
9
Winter
3
Class B — Favorite Season
Summer
5
Winter
7
Comparison questions to ask:
Which class prefers summer more?
Do both classes agree, or do they disagree?
What might explain the difference?
Lesson — Strong Conclusions

What Makes a Strong Conclusion?

❌ Weak conclusion
"Some people like dogs."
"The chart is about pets."
"Dogs are better than cats."
Why weak: vague, doesn't use the data, or goes way beyond what the chart says
✅ Strong conclusion
"In our class of 24, dogs were the most popular pet with 8 votes."
"Fish and 'No pet' were tied at 3 each — the least popular options."
Why strong: uses specific numbers, stays close to the data, is clear and precise
Vocabulary Check

Today's Key Words

Observation
What you can see directly in the data — a fact anyone can verify from the chart
Inference
What you think the data means — uses reasoning, goes beyond what the chart shows
Pattern
Something that repeats or stands out in the data
Trend
A direction the data moves — higher, lower, or staying the same
Conclusion
What you decide based on all the evidence in the data
Compare
Looking at two or more values and noticing what's the same or different
Session 6 Wrap-Up
🎉

You Can Read Data!

Visualize → Interpret → (next: Watch out for tricks!)
What you did today
Walked a gallery of classmates' charts
Learned observation vs. inference
Analyzed 3 charts as a detective
Wrote real data science statements about your own chart
Coming up in Session 7
What if a chart is designed to fool you?
Misleading graphs — truncated axes, tiny samples
How to spot tricks and stay critical
You'll become a Data Myth Buster!
Data Science for Young Minds · Grade 3 · sdabagh.github.io/learn/data-science