Data and Graphing Study Pack

1. Introduction / Essential Question

Essential Question

How do scientists use data, tables, and graphs as evidence to understand patterns, test ideas, and make better decisions?

Introduction / Hook

Imagine two students are testing which paper towel absorbs the most water. One student says, "Brand A is best because it looks thicker." Another student measures how many milliliters of water each towel absorbs, repeats the test three times, records the results in a table, and makes a bar graph.

Which student has stronger evidence?

In science, opinions and guesses are not enough. Scientists collect data, organize it, graph it, and look for patterns. Data can help answer questions like:

• Which plant fertilizer helps plants grow tallest?
• How does temperature affect the speed of a chemical reaction?
• Does exercise change heart rate?
• Which bridge design holds the most weight?
• How is Earth's average temperature changing over time?

Data and graphing are part of the Science and Engineering Practices in the NGSS. They help scientists and engineers make sense of observations, compare results, support claims, and improve designs.

In this study pack, you will learn how to:

• identify different types of data
• choose useful tables and graphs
• read axes, scales, labels, and units
• describe patterns and trends
• connect evidence to scientific explanations
• avoid common graphing mistakes
• use data to support a claim

As you read, keep asking:

• What do I notice?
• What patterns do I see?
• What evidence supports this idea?
• What could explain the results?
• How could the investigation be improved?

2. Key Vocabulary / Definitions

Required Science and Engineering Vocabulary

Term	Student-Friendly Definition	Example
Hypothesis	A testable prediction or possible explanation based on what you already know.	"If plants get more sunlight, then they will grow taller."
Variable	Something that can change in an investigation.	Amount of sunlight, plant height, temperature, time
Independent variable	The variable the scientist changes on purpose.	Changing the amount of sunlight a plant receives
Dependent variable	The variable the scientist measures or observes.	Measuring how tall the plant grows
Controlled variable	A variable kept the same to make the test fair.	Same plant type, soil, pot size, and water amount
Evidence	Data or observations used to support a scientific claim.	A table showing plant growth over 14 days
System	A group of connected parts that interact.	A terrarium, weather system, ecosystem, or circuit
Energy	The ability to cause change or do work.	Light energy helps plants make food
Matter	Anything that has mass and takes up space.	Air, water, rocks, soil, plants, and people

Data and Graphing Vocabulary

Term	Definition	Why It Matters
Data	Information collected through observations or measurements.	Data helps scientists answer questions using evidence.
Quantitative data	Data involving numbers or measurements.	"The plant is 12 cm tall."
Qualitative data	Data describing qualities or characteristics.	"The leaf is dark green."
Observation	Information gathered using senses or tools.	Seeing bubbles form or measuring temperature
Measurement	A number with a unit collected using a tool.	25 degrees C, 8 cm, 40 g
Unit	A label showing what a measurement means.	meters, grams, seconds, degrees Celsius
Table	An organized grid of data using rows and columns.	A table of temperature each hour
Graph	A visual display of data.	A line graph of temperature over time
Axis	A reference line on a graph.	x-axis and y-axis
x-axis	The horizontal axis, usually showing the independent variable.	Time in minutes
y-axis	The vertical axis, usually showing the dependent variable.	Temperature in degrees C
Scale	The number pattern used on an axis.	Counting by 1s, 5s, 10s, or 100s
Title	A short description of what a graph or table shows.	"Plant Growth Over 4 Weeks"
Trend	A general pattern in data.	Increasing, decreasing, or staying about the same
Outlier	A data point very different from the rest.	One plant measuring 2 cm when others are 12-14 cm
Average	A typical value found by adding values and dividing by how many values there are.	Average height of three plants
Range	The difference between the highest and lowest values.	18 cm - 10 cm = 8 cm
Bar graph	A graph used to compare categories.	Comparing battery brands
Line graph	A graph used to show change over time or continuous data.	Temperature over a day
Pie chart	A circular graph showing parts of a whole.	Percent of classroom waste types
Scatter plot	A graph showing pairs of numerical data to look for relationships.	Shoe size and height
Model	A representation of an idea, object, system, or process.	A graph can model how data changes.
Claim	A statement that answers a question.	"The plant grew faster with more light."
Reasoning	The explanation that connects evidence to a claim.	"Light provides energy for photosynthesis, so more light can increase growth up to a point."

3. Core Science Concepts

3.1 Why Scientists Collect Data

Science is based on evidence. Data gives scientists a way to move from "I think" to "The evidence shows."

Scientists collect data to:

• test hypotheses
• compare different conditions
• identify patterns
• make predictions
• improve models
• evaluate engineering designs
• communicate discoveries clearly

A scientist might ask, "How does water temperature affect how fast sugar dissolves?" Instead of guessing, the scientist can measure dissolving time at different temperatures and look for a pattern.

3.2 Qualitative and Quantitative Data

There are two main types of data.

Qualitative data describes qualities:

• The solution turned cloudy.
• The soil felt dry.
• The rock had a rough texture.
• The flame was orange.

Quantitative data uses numbers and units:

• The solution temperature was 40 degrees C.
• The plant was 18 cm tall.
• The rock had a mass of 63 g.
• The reaction took 25 seconds.

Both types can be useful. Quantitative data is usually easier to graph, compare, and analyze. Qualitative data can help describe changes that numbers may not show.

3.3 Variables in an Investigation

A variable is anything that can change. Good investigations identify variables clearly.

Example investigation:

Question: How does the amount of light affect plant growth?

• Independent variable: amount of light
• Dependent variable: plant height
• Controlled variables: plant type, soil, pot size, water amount, room temperature, length of investigation

If too many variables change at once, it becomes hard to know what caused the result. A fair test changes one main variable and keeps other important conditions the same.

3.4 Tables Organize Data

A data table helps keep information neat before it becomes a graph.

Good tables include:

• a clear title
• column headings
• units in headings
• data arranged logically
• repeated trials when possible
• averages when useful

A table is not just a place to store numbers. It helps you notice patterns and decide which graph will communicate the data best.

3.5 Graphs Show Patterns

Graphs turn data into pictures. This makes patterns easier to see.

Different graph types answer different questions:

Graph Type	Best Used For	Example Science Question
Bar graph	Comparing categories	Which material insulates best?
Line graph	Showing change over time or continuous change	How does temperature change during the day?
Pie chart	Showing parts of a whole	What percent of trash is paper, plastic, metal, and food?
Scatter plot	Looking for relationships between two numerical variables	Is arm span related to height?

Choosing the wrong graph can make data harder to understand. For example, a pie chart is not useful for showing plant height over several weeks because plant height changes over time. A line graph would be better.

3.6 Graph Parts

A complete graph should include:

• title
• x-axis label
• y-axis label
• units
• even scale
• correctly plotted points or bars
• key or legend if more than one data set is shown

If a graph is missing labels or units, it may be difficult to understand. A graph that says "Height" is less helpful than one that says "Plant Height (cm)."

3.7 Scale Matters

The scale is the number pattern on an axis. A good scale:

• is evenly spaced
• includes all data values
• is easy to read
• does not make the graph misleading

For example, if the y-axis jumps from 0 to 10 to 25 to 30, the scale is uneven. That can distort the pattern. A better scale might count by 5s or 10s.

3.8 Patterns, Trends, and Relationships

When analyzing data, scientists look for patterns such as:

• increase: values go up
• decrease: values go down
• no clear change: values stay about the same
• cycle: values repeat in a pattern
• relationship: one variable changes as another variable changes

A line graph might show that temperature rises during the morning, reaches a peak in the afternoon, then falls in the evening. A scatter plot might show that taller students often have longer arm spans, but not exactly every time.

3.9 Outliers and Repeated Trials

An outlier is a data point that is very different from the others. Outliers can happen because:

• a measurement tool was used incorrectly
• data was recorded wrong
• conditions changed during the test
• the result was unusual but real

Scientists do not automatically erase outliers. They investigate them. Repeated trials help scientists see whether a result is reliable.

Example:

Trial	Time for Tablet to Dissolve (s)
1	42
2	44
3	15

The 15-second result is much lower than the others. A scientist should ask:

• Was the water hotter in Trial 3?
• Was the tablet already broken?
• Was the timer started late?
• Should the test be repeated?

3.10 Evidence and Claim-Evidence-Reasoning

Scientists use data as evidence in explanations. A strong scientific explanation often uses Claim-Evidence-Reasoning, or CER.

Claim: The answer to the question.

Evidence: Data or observations that support the claim.

Reasoning: The science idea that explains why the evidence supports the claim.

Example:

Question: Does warmer water make sugar dissolve faster?

Claim: Sugar dissolves faster in warmer water.

Evidence: In the investigation, sugar dissolved in 18 seconds at 60 degrees C but took 75 seconds at 10 degrees C.

Reasoning: Warmer water particles move faster, so they collide with sugar particles more often and help separate them faster.

3.11 Data in Engineering

Engineers also use data. They test designs, compare results, and improve solutions.

Example engineering challenge:

Build a paper bridge that holds the greatest mass.

Engineers might collect:

• bridge shape
• number of paper sheets
• length of bridge
• mass held before collapsing
• cost of materials

They can graph the data to compare designs and decide which design works best under the constraints.

3.12 Data Does Not Explain Itself

Data needs interpretation. Two students may look at the same graph and make different claims. The stronger claim is the one supported by evidence and good reasoning.

When interpreting data, ask:

• What is the graph showing?
• What are the variables?
• What units are used?
• What pattern is visible?
• Are there outliers?
• What evidence supports the claim?
• What questions remain?

4. Examples, Case Studies, and Real-World Applications

Case Study 1: Tracking Weather

Meteorologists collect weather data every day, including:

• air temperature
• wind speed
• humidity
• air pressure
• precipitation

They use graphs and models to predict weather. A line graph can show how temperature changes during the day. A map can show where storms are moving. A table can compare rainfall totals in different cities.

Weather predictions are not perfect because weather is a complex system with many interacting parts. Still, data helps scientists make useful forecasts.

Case Study 2: Sports Science

Coaches and athletes use data to improve performance. A runner might record:

• distance
• time
• heart rate
• temperature
• sleep
• hydration

A line graph of running time over several weeks can show whether the athlete is improving. A scatter plot might show whether higher temperatures are related to slower race times.

Science thinking question:

If a runner's time gets slower on hot days, what other variables should be controlled or measured before making a conclusion?

Possible variables include hydration, sleep, route, wind, shoes, and training intensity.

Case Study 3: Environmental Science

Environmental scientists use data to study ecosystems. They might collect:

• water temperature
• pH
• dissolved oxygen
• number of fish
• nitrate level
• plant growth

If dissolved oxygen in a pond decreases, fish populations may also decrease. A scientist can graph oxygen levels and fish counts over time to look for a relationship.

Important reminder: A relationship in data does not always prove one thing caused another. Scientists need careful investigation and repeated evidence before making strong cause-and-effect claims.

Case Study 4: Medical and Public Health Data

Public health scientists use data to understand patterns in human health. They may study:

• disease spread
• vaccination rates
• air pollution exposure
• nutrition
• exercise habits

Graphs help communities make decisions. For example, a line graph might show flu cases increasing in winter. A bar graph might compare asthma rates in areas with different air quality.

Scientists must handle health data carefully and respectfully. Personal information should be protected.

Case Study 5: Engineering Safer Helmets

Engineers design helmets to protect the brain. They test different materials and shapes using sensors that measure force during impacts.

Data can help answer:

• Which material reduces impact force the most?
• How does helmet thickness affect protection?
• Does the design still feel comfortable?
• Does the helmet meet safety requirements?

Engineers use test data to revise their designs. A first design is rarely the final design.

5. Tables and Data

Data Table 1: Plant Growth Investigation

Question: How does daily light exposure affect bean plant height after 14 days?

Daily Light Exposure	Trial 1 Height (cm)	Trial 2 Height (cm)	Trial 3 Height (cm)	Average Height (cm)
2 hours	5	6	5	5.3
4 hours	9	8	10	9.0
6 hours	13	14	13	13.3
8 hours	16	15	17	16.0

What do you notice?

• Plant height increased as light exposure increased.
• The 8-hour plants had the highest average height.
• Repeated trials made the results more reliable than one plant per condition.

Possible claim:

Bean plants grew taller when they received more daily light, at least from 2 to 8 hours in this investigation.

Data Table 2: Temperature and Dissolving Time

Question: How does water temperature affect how long a sugar cube takes to dissolve?

Water Temperature (degrees C)	Dissolving Time (s)
10	95
20	74
30	55
40	39
50	28
60	19

Pattern:

As temperature increased, dissolving time decreased.

Scientific reasoning:

Higher temperature means water particles move faster. Faster-moving water particles collide with sugar particles more often, helping the sugar cube break apart and dissolve more quickly.

Data Table 3: Material Insulation Test

Question: Which cup covering keeps water warm the longest?

Starting water temperature: 80 degrees C

Water temperature after 20 minutes:

Covering Material	Final Temperature (degrees C)
No covering	42
Paper towel	48
Aluminum foil	51
Cotton cloth	57
Foam sheet	64

Best graph choice:

A bar graph is a good choice because the independent variable is a set of categories: different covering materials.

Possible claim:

The foam sheet was the best insulator in this investigation because the water covered with foam had the highest final temperature after 20 minutes.

Data Table 4: Pond Water Quality

Week	Water Temperature (degrees C)	Dissolved Oxygen (mg/L)	Fish Count Observed
1	18	9.2	31
2	20	8.7	29
3	23	7.8	25
4	26	6.5	18
5	28	5.9	14

What patterns do you see?

• Water temperature increased from Week 1 to Week 5.
• Dissolved oxygen decreased as water temperature increased.
• Fish counts decreased during the same time period.

Careful interpretation:

The data suggests a relationship among warmer water, lower dissolved oxygen, and fewer fish observed. More evidence would be needed to prove the exact cause of the fish decrease.

Data Table 5: Paper Bridge Engineering Test

Design constraint: Use only 3 sheets of paper and 20 cm of tape.

Bridge Design	Shape	Mass Held Before Collapse (g)	Notes
A	Flat strip	120	Bent quickly in the center
B	Folded accordion	410	Held shape well
C	Rolled tubes	530	Strong but took longer to build
D	Triangle truss	620	Most stable during testing

Engineering conclusion:

Design D held the most mass. Its triangle shapes helped spread forces through the structure.

6. Text / ASCII Diagrams and Visual Aids

Scientific Diagram: Parts of a Graph

Title: Plant Growth Over 4 Weeks

Plant Height (cm)
20 |                         *
18 |
16 |                   *
14 |
12 |             *
10 |
 8 |       *
 6 |
 4 |
 2 |
 0 +--------------------------------
     Week 1   Week 2   Week 3   Week 4

         Time (weeks)

Labels to notice:

• The title explains what the graph shows.
• The x-axis shows time in weeks.
• The y-axis shows plant height in centimeters.
• The points show plant height measurements.
• The upward pattern shows growth over time.

Graph: Bar Graph Example

Question: Which material kept water warmest?

Final Temperature (degrees C)
70 |
65 |                              ####
60 |                              ####
55 |                    ####      ####
50 |          ####      ####      ####
45 | ####     ####      ####      ####
40 | ####     ####      ####      ####
   +-------------------------------------
     None    Foil     Cloth     Foam

The tallest bar is foam, so foam kept the water warmest in this test.

Flow Diagram: From Question to Evidence

Ask a question
      |
      v
Make a hypothesis
      |
      v
Plan a fair test
      |
      v
Collect data
      |
      v
Organize in a table
      |
      v
Make a graph
      |
      v
Look for patterns
      |
      v
Make a claim with evidence

Experiment Setup: Dissolving Sugar

Cup A: 10 degrees C water       Cup B: 60 degrees C water
-------------------------       -------------------------
|                       |       |                       |
|   sugar cube          |       |   sugar cube          |
|      []               |       |      []               |
|                       |       |                       |
-------------------------       -------------------------

Same in both cups:
- same amount of water
- same sugar cube size
- same stirring method
- same timer method

Changed on purpose:
- water temperature

Measured:
- time for sugar to dissolve

Infographic: Choosing a Graph

What kind of data do you have?

Categories to compare?
      |
      v
   Bar graph

Change over time?
      |
      v
   Line graph

Parts of one whole?
      |
      v
   Pie chart

Two numerical variables?
      |
      v
   Scatter plot

Comparison Grid: Graph Types

Feature	Bar Graph	Line Graph	Pie Chart	Scatter Plot
Shows categories	Yes	Sometimes	Yes	No
Shows change over time	Not best	Yes	No	Sometimes
Shows parts of a whole	No	No	Yes	No
Shows relationships	Sometimes	Yes	No	Yes
Uses x- and y-axes	Yes	Yes	Usually no	Yes
Common science use	Compare materials	Track temperature	Show percentages	Look for correlation

Scenario Card: Mystery Graph

Scenario:

A class tested how ramp height affects the distance a toy car travels. The graph shows that as ramp height increased, travel distance also increased.

Think about it:

• What is the independent variable?
• What is the dependent variable?
• What pattern is shown?
• What might explain the pattern?
• What controlled variables would make the test fair?

Possible reasoning:

A higher ramp gives the car more gravitational potential energy at the start. As the car rolls down, more of that energy changes into motion, so the car may travel farther.

7. Interactive Thinking Tasks

Task 1: Notice and Wonder

Look at this data:

Day	Seedling Height (cm)
1	2
3	3
5	5
7	8
9	11

What do you notice?

What do you wonder?

Possible notices:

• The seedling got taller over time.
• Growth was not exactly the same every two days.
• The largest increase was from Day 5 to Day 7 or Day 7 to Day 9.

Possible wonders:

• Would the seedling keep growing at the same rate?
• How much light did it receive?
• Was it watered the same amount each day?

Task 2: Choose the Best Graph

Choose the best graph type for each data set.

Data Set	Best Graph Type	Why?
Number of each bird species seen in a park	Bar graph	Compares categories
Temperature every hour for one day	Line graph	Shows change over time
Percent of school waste that is paper, plastic, food, and metal	Pie chart	Shows parts of a whole
Height and arm span of 25 students	Scatter plot	Shows relationship between two numerical variables

Task 3: Spot the Graphing Error

A student makes a graph titled "Temperature." The x-axis says "Time" but has no units. The y-axis has numbers 0, 5, 20, 25, 30 with uneven spacing.

What should be fixed?

• The title should be more specific, such as "Water Temperature Over 30 Minutes."
• The x-axis should include units, such as "Time (min)."
• The y-axis should include units, such as "Temperature (degrees C)."
• The y-axis scale should increase evenly.

Task 4: Build a CER Response

Question: Which bridge design was strongest?

Data:

Design	Mass Held (g)
Flat	120
Accordion	410
Tube	530
Triangle truss	620

Claim:

The triangle truss bridge was the strongest.

Evidence:

It held 620 g before collapsing, which was more than the flat, accordion, and tube designs.

Reasoning:

Triangle shapes help distribute forces through a structure, making the bridge more stable under load.

8. Common Misconceptions

Misconception 1: "A graph proves cause and effect."

Better thinking:

A graph can show a pattern or relationship, but it does not always prove that one variable caused another. Scientists need fair tests, repeated evidence, and careful reasoning to support cause and effect.

Example:

Ice cream sales and sunburns may both increase in summer. Buying ice cream does not cause sunburn. Hot sunny weather can influence both.

Misconception 2: "The tallest bar is always the best result."

Better thinking:

The tallest bar means the greatest amount, but that is not always "best." If a graph shows pollution level, a shorter bar may be better. Always read the title, labels, and question.

Misconception 3: "Data and evidence mean the same thing."

Better thinking:

Data is collected information. Evidence is data used to support a claim. Not all data automatically counts as useful evidence for every claim.

Misconception 4: "Qualitative data is not scientific."

Better thinking:

Qualitative observations can be scientific when collected carefully. Color change, odor, texture, and behavior can be important observations. However, numbers often make comparisons easier.

Misconception 5: "A line graph should connect any points."

Better thinking:

Line graphs are best for continuous data, especially change over time. You would not usually connect points for unrelated categories like rock types or favorite lunch foods.

Misconception 6: "An outlier should always be deleted."

Better thinking:

An outlier should be investigated. It might be a mistake, or it might reveal something important. Scientists repeat tests and check methods before deciding what to do.

Misconception 7: "A bigger sample always guarantees correct results."

Better thinking:

A larger sample can improve reliability, but the investigation still needs good methods. Biased samples, poor tools, or uncontrolled variables can still lead to weak conclusions.

Misconception 8: "The independent variable always goes on the y-axis."

Better thinking:

Usually, the independent variable goes on the x-axis and the dependent variable goes on the y-axis.

Misconception 9: "Graphs are only used in math class."

Better thinking:

Graphs are used across science, engineering, medicine, sports, economics, weather forecasting, environmental studies, and many other fields.

Misconception 10: "If two trials are different, the experiment failed."

Better thinking:

Some variation is normal. Scientists use repeated trials and averages to understand natural variation and reduce the effect of random errors.

9. Science Thinking Tips

Tip 1: Read the Whole Graph Before Answering

Before using a graph, check:

• title
• x-axis label
• y-axis label
• units
• scale
• key or legend
• highest and lowest values
• general pattern

Do not jump straight to the bars or points without reading the labels.

Tip 2: Use Precise Data in Answers

Weak answer:

The plant grew more.

Stronger answer:

The plant grew from 2 cm on Day 1 to 11 cm on Day 9, an increase of 9 cm.

Precise numbers make your evidence stronger.

Tip 3: Describe Trends Clearly

Useful trend phrases:

• increased steadily
• decreased sharply
• stayed about the same
• rose at first, then leveled off
• changed in a repeating cycle
• showed no clear pattern

Avoid vague phrases like:

• it changed
• it was weird
• it went different

Tip 4: Use Claim-Evidence-Reasoning

When writing a scientific explanation:

1. State your claim clearly.
2. Give specific data as evidence.
3. Explain the science idea that connects the evidence to the claim.

Sentence frame:

My claim is ___. The evidence is ___. This supports the claim because ___.

Tip 5: Compare With Numbers

Instead of saying "Design D was stronger," say:

Design D held 620 g, while Design C held 530 g. Design D held 90 g more than Design C.

Comparing with numbers makes your answer more convincing.

Tip 6: Think About Fair Tests

Ask:

• What changed on purpose?
• What was measured?
• What stayed the same?
• Were there repeated trials?
• Was the sample size large enough?
• Could there be another explanation?

Tip 7: Be Careful With Predictions

A prediction should be based on a pattern, not a random guess.

Example:

If a plant grew 2 cm, then 3 cm, then 5 cm, then 8 cm, it is reasonable to predict it may keep growing. But the exact next height depends on light, water, nutrients, space, and plant health.

Tip 8: Know the Difference Between Correlation and Causation

Correlation means two variables seem related.

Causation means one variable directly causes a change in another.

Scientists need controlled investigations to support causation.

Tip 9: Use Units Every Time

Numbers without units can be confusing.

8 could mean:

• 8 seconds
• 8 grams
• 8 centimeters
• 8 degrees C
• 8 trials

Always include units when reporting measurements.

Tip 10: Ask Better Inquiry Questions

Strong science questions are testable and specific.

Less useful:

Do plants like light?

Better:

How does the number of hours of daily light affect the height of bean plants after 14 days?

10. Practice Questions

A. Quick Recall Questions

1. What is data?
2. What is the difference between qualitative and quantitative data?
3. Which variable is changed on purpose in an investigation?
4. Which variable is measured in an investigation?
5. Why should a graph have units on its axes?
6. What type of graph is usually best for showing change over time?
7. What type of graph is usually best for comparing categories?
8. What is an outlier?
9. What does CER stand for?
10. Why are repeated trials useful?
11. What is a hypothesis?
12. What is evidence?
13. What is a controlled variable?
14. Why is scale important on a graph?
15. What does a trend show?

B. Multiple Choice Questions

Choose the best answer.

1. Which statement is quantitative data? A. The liquid is blue. B. The rock feels rough. C. The plant is 14 cm tall. D. The soil smells earthy.

2. A student changes the amount of fertilizer given to plants and measures plant height. What is the independent variable? A. Plant height B. Amount of fertilizer C. Type of ruler D. Color of the leaves

3. In the same fertilizer investigation, what is the dependent variable? A. Amount of fertilizer B. Plant height C. Pot color D. Number of students

4. Which graph is best for showing the temperature of water every minute for 20 minutes? A. Line graph B. Pie chart C. Bar graph D. Picture graph only

5. Which graph is best for comparing the mass held by four bridge designs? A. Pie chart B. Bar graph C. Line graph D. Map

6. What should usually go on the x-axis? A. The independent variable B. The dependent variable C. The conclusion D. The answer key

7. What should usually go on the y-axis? A. The independent variable B. The dependent variable C. The title D. The hypothesis only

8. A graph title should tell the reader: A. who drew the graph B. what the graph shows C. the answer to every question D. only the dependent variable

9. Which is the best example of evidence? A. "I think foam is warm." B. "Foam looks thicker than paper." C. "The foam-covered cup was 64 degrees C after 20 minutes." D. "My friend likes foam."

10. What is an outlier? A. A graph with no title B. A data point very different from the rest C. A controlled variable D. A type of pie chart

11. Why do scientists repeat trials? A. To make the investigation take longer B. To check whether results are reliable C. To avoid using data D. To change all variables

12. Which question is most testable? A. Are plants cool? B. Why is science interesting? C. How does daily light exposure affect bean plant height after 14 days? D. Which plant is the best?

13. A pie chart is best for showing: A. parts of a whole B. change over time C. a circuit diagram D. repeated trials only

14. A scatter plot is useful for: A. comparing lunch choices only B. looking for a relationship between two numerical variables C. showing parts of one whole D. labeling a microscope

15. If water temperature increases and dissolving time decreases, the graph shows: A. no pattern B. a relationship between variables C. that the timer broke D. that sugar is matter but water is not

16. Which is a controlled variable in a sugar dissolving test? A. Water temperature, if it is changed on purpose B. Time to dissolve, if it is measured C. Same sugar cube size in every trial D. The final claim

17. What is the purpose of a data table? A. To organize data clearly B. To replace all graphs C. To hide unusual results D. To make results less precise

18. A student says, "The tallest bar is always the best." What is the problem with this idea? A. Bar graphs never have tall bars. B. The highest amount is not always the best result. C. The x-axis always shows height. D. Graphs cannot show pollution.

19. What does reasoning do in a CER explanation? A. Connects evidence to the claim using science ideas B. Lists random numbers C. Repeats the title D. Deletes outliers

20. Which axis label is most complete? A. Time B. Stuff C. Plant Height (cm) D. Results

21. Which is qualitative data? A. The water temperature was 23 degrees C. B. The object had a mass of 80 g. C. The leaf was yellow with brown spots. D. The ramp height was 12 cm.

22. Which statement is a claim? A. "The foam sheet was the best insulator." B. "64 degrees C" C. "Trial 1, Trial 2, Trial 3" D. "Temperature in degrees C"

23. Which graphing choice could be misleading? A. Using clear labels B. Using units C. Using uneven scale spacing D. Including a title

24. In a ramp investigation, a toy car travels farther from a higher ramp. Which science idea may help explain this? A. Higher ramp height can give the car more gravitational potential energy. B. Matter disappears on high ramps. C. Graphs make cars move faster. D. The dependent variable causes the independent variable.

25. Which statement about data is most accurate? A. Data always explains itself. B. Data must be interpreted carefully. C. Data is never used in engineering. D. Data is only qualitative.

26. A student measures plant height in centimeters. What does "centimeters" represent? A. A unit B. A trend C. An outlier D. A hypothesis

27. Which is the best reason to average repeated trials? A. To reduce the effect of random variation B. To remove all data C. To make the independent variable disappear D. To avoid making a graph

28. Which situation best shows correlation but not necessarily causation? A. A controlled test shows hotter water dissolves sugar faster. B. Ice cream sales and sunburns both increase in summer. C. A ruler measures length in centimeters. D. A student labels the x-axis.

29. What is the range of these values: 5 cm, 8 cm, 12 cm, 13 cm? A. 5 cm B. 8 cm C. 13 cm D. 8 cm

30. Which statement best describes a system? A. A group of connected parts that interact B. A single number with no unit C. A graph with missing labels D. A random guess

31. Which graph type would best show how a student's heart rate changes before, during, and after exercise? A. Line graph B. Pie chart C. Bar graph D. Venn diagram only

32. Which answer best describes matter? A. Anything with mass and volume B. A type of graph title C. A prediction without evidence D. The ability to cause change

33. Which answer best describes energy? A. Anything with mass B. The ability to cause change or do work C. A table heading D. A controlled variable only

34. What should a scientist do first when seeing an unexpected outlier? A. Automatically delete it B. Investigate possible reasons and consider repeating the trial C. Ignore all the other data D. Change the hypothesis to match only the outlier

35. Which is the strongest scientific explanation? A. "The bridge was best because I liked it." B. "The triangle truss held 620 g, the most of all designs, because triangles spread forces through the structure." C. "The bridge worked." D. "The graph was tall."

C. Short Answer Questions

1. A class records the number of leaves on plants each week for six weeks. What graph type should they use, and why?
2. Explain why units are important in data tables and graphs.
3. A student changes both water temperature and sugar cube size in the same dissolving investigation. Why is this a problem?
4. Look at the plant growth data in Data Table 1. What claim can you make using the averages?
5. In Data Table 2, what happens to dissolving time as temperature increases?
6. Why might a scientist use repeated trials instead of one trial?
7. What is the difference between data and evidence?
8. A graph shows that two variables increase at the same time. Why should you be careful before saying one caused the other?
9. What information should be included in a complete graph title?
10. Why are controlled variables important in a fair test?
11. A student measures three trials: 22 s, 24 s, and 80 s. What should the student do about the 80 s result?
12. Explain how a graph can be a model.

D. Longer Written / Reasoning Questions

1. A student tests how ramp height affects the distance a toy car travels. The student uses ramp heights of 10 cm, 20 cm, 30 cm, and 40 cm. The car travels 45 cm, 82 cm, 121 cm, and 159 cm. Write a CER explanation about the relationship between ramp height and travel distance.

2. A school wants to reduce cafeteria waste. Students collect data on waste types for one week: paper 25%, plastic 30%, food 35%, metal 10%. What graph type should they use to show this data? Explain why and describe one action the school could take based on the data.

3. An engineering team tests four water filters. They measure how clear the water is after filtering and how long each filter takes. Filter A is fastest but leaves cloudy water. Filter B is slower but produces very clear water. Filter C is medium speed and medium clarity. Filter D breaks during testing. Explain why engineers may need more than one kind of data before choosing the best design.

4. A graph shows pond temperature rising from 18 degrees C to 28 degrees C over five weeks while dissolved oxygen falls from 9.2 mg/L to 5.9 mg/L. Fish counts also fall. Explain the pattern and describe one additional piece of data that would help scientists understand the system better.

5. Design a fair investigation to test which paper towel absorbs the most water. Identify the independent variable, dependent variable, and at least three controlled variables. Explain what data table you would create.

E. Data Analysis Tasks

Use this data for Questions 1-5.

Question: How does exercise time affect heart rate?

Exercise Time (min)	Heart Rate (beats/min)
0	72
2	94
4	116
6	132
8	144
10	151

1. What is the independent variable?
2. What is the dependent variable?
3. Describe the trend.
4. What graph type would best show this data?
5. Predict the heart rate at 12 minutes. Explain why your prediction should be cautious.

Use this data for Questions 6-10.

Question: Which surface creates the most friction for a sliding block?

Surface	Distance Block Slid (cm)
Wax paper	92
Smooth tile	78
Cardboard	51
Carpet	24

6. Which surface seems to create the most friction? Use evidence.
7. Which surface seems to create the least friction? Use evidence.
8. What graph type would work best?
9. Why does a shorter sliding distance suggest more friction?
10. Name one controlled variable for this investigation.

11. Answer Key

A. Quick Recall Answers

1. Data is information collected through observations or measurements.
2. Qualitative data describes qualities; quantitative data uses numbers and units.
3. The independent variable.
4. The dependent variable.
5. Units show what the numbers mean.
6. A line graph.
7. A bar graph.
8. A data point very different from the rest.
9. Claim-Evidence-Reasoning.
10. They help check reliability and reduce the effect of random variation.
11. A testable prediction or possible explanation.
12. Data or observations used to support a claim.
13. A variable kept the same in a fair test.
14. Scale affects how accurately and clearly data patterns are shown.
15. A trend shows a general pattern in data.

B. Multiple Choice Answers

1. C
2. B
3. B
4. A
5. B
6. A
7. B
8. B
9. C
10. B
11. B
12. C
13. A
14. B
15. B
16. C
17. A
18. B
19. A
20. C
21. C
22. A
23. C
24. A
25. B
26. A
27. A
28. B
29. D
30. A
31. A
32. A
33. B
34. B
35. B

C. Short Answer Suggested Answers

1. A line graph, because the data shows change over time.
2. Units tell what measurements mean. Without units, a number could represent seconds, grams, centimeters, degrees, or something else.
3. It is a problem because two variables changed at once, so the student cannot tell whether temperature or sugar cube size caused the result.
4. Bean plants grew taller as daily light exposure increased. The average height increased from 5.3 cm at 2 hours of light to 16.0 cm at 8 hours of light.
5. As temperature increases, dissolving time decreases.
6. Repeated trials make results more reliable and help reveal unusual results or measurement errors.
7. Data is collected information. Evidence is data used to support a specific claim.
8. Two variables changing together does not always mean one caused the other. Another variable could affect both.
9. A complete graph title should describe the variables or what relationship the graph shows, such as "Plant Height Over 4 Weeks."
10. Controlled variables help make sure the test is fair and that changes in the dependent variable are likely due to the independent variable.
11. The student should investigate whether the 80 s result was caused by an error or unusual condition, then consider repeating the trial.
12. A graph can be a model because it represents a real pattern or relationship in a simpler visual form.

D. Longer Written / Reasoning Suggested Responses

1. Sample CER: The toy car traveled farther when ramp height increased. The evidence is that the car traveled 45 cm from a 10 cm ramp, 82 cm from a 20 cm ramp, 121 cm from a 30 cm ramp, and 159 cm from a 40 cm ramp. This supports the claim because a higher ramp gives the car more gravitational potential energy. As the car rolls down, more energy changes into motion, which can help the car travel farther.

2. A pie chart would work well because the data is given as percentages that make up one whole: all cafeteria waste. The largest part is food waste at 35%, followed by plastic at 30%. The school could reduce waste by starting a composting program for food waste or by encouraging reusable containers to reduce plastic.

3. Engineers need more than one kind of data because a design may be strong in one way but weak in another. Filter A is fast, but cloudy water means it may not clean well. Filter B is slower, but the clear water suggests better filtering. Filter D breaking is also important because reliability matters. Engineers would need to compare speed, clarity, cost, durability, and safety before choosing the best design.

4. The data shows that as pond temperature increased, dissolved oxygen decreased, and fish counts also decreased. Warmer water often holds less dissolved oxygen, and fish need dissolved oxygen to survive. One additional useful piece of data would be pollution level, algae growth, rainfall, water depth, or the number of fish predators. This would help scientists understand whether temperature was the main cause or part of a larger system change.

5. A fair investigation could test different paper towel brands. The independent variable is paper towel brand. The dependent variable is the amount of water absorbed, measured in milliliters. Controlled variables should include towel size, amount of water available, soaking time, draining time, and testing method. A useful data table would list each brand, Trial 1 water absorbed, Trial 2 water absorbed, Trial 3 water absorbed, and average water absorbed.

E. Data Analysis Answers

1. Exercise time.
2. Heart rate.
3. Heart rate increases as exercise time increases. The increase is fast at first and then begins to slow slightly.
4. A line graph, because the data shows change over time.
5. A reasonable prediction might be around 156-158 beats/min, based on the slowing increase from 8 to 10 minutes. The prediction should be cautious because heart rate may level off, and people differ in fitness, age, and effort.
6. Carpet creates the most friction because the block slid the shortest distance, 24 cm.
7. Wax paper creates the least friction because the block slid the farthest distance, 92 cm.
8. A bar graph, because the surfaces are categories.
9. Friction is a force that opposes motion. More friction slows the block sooner, so it travels a shorter distance.
10. Controlled variables could include the same block, same starting height, same push or release method, same surface length, and same measurement method.

12. Model Answers / Suggested Responses

Model Response 1: Graph Interpretation

Question:

A line graph shows that a plant grew from 3 cm on Week 1 to 6 cm on Week 2, 10 cm on Week 3, and 15 cm on Week 4. What pattern does the graph show?

Model answer:

The graph shows an increasing trend in plant height over time. The plant grew from 3 cm to 15 cm between Week 1 and Week 4, which is an increase of 12 cm. The data supports the claim that the plant continued growing during the investigation.

Why this is strong:

• It names the trend.
• It uses specific numbers.
• It connects the data to a claim.

Model Response 2: Fair Test Explanation

Question:

Why should a scientist keep the same amount of water in each cup when testing how temperature affects dissolving time?

Model answer:

The amount of water should stay the same because it is a controlled variable. If one cup has more water than another, dissolving time might change because of water amount instead of temperature. Keeping water amount the same makes the test fairer.

Model Response 3: Evidence-Based Comparison

Question:

Which material was the best insulator: paper towel, aluminum foil, cotton cloth, or foam sheet?

Model answer:

The foam sheet was the best insulator in this investigation. The water covered with foam was 64 degrees C after 20 minutes, which was warmer than cotton cloth at 57 degrees C, aluminum foil at 51 degrees C, and paper towel at 48 degrees C. Since the foam-covered cup lost the least heat, foam performed best.

Model Response 4: Correlation and Causation

Question:

A graph shows that students who spend more time outside also tend to drink more water. Does the graph prove that being outside causes people to drink more water?

Model answer:

The graph shows a relationship, but it does not prove cause and effect by itself. Students may drink more water because they are exercising, because the weather is hotter, or because they brought larger water bottles. A controlled investigation would be needed to test the cause.

Model Response 5: Engineering Data

Question:

An engineer designs a phone case. One material protects the phone well but is expensive. Another is cheaper but cracks during testing. How should data help the engineer decide what to do next?

Model answer:

The engineer should compare data about protection, cost, weight, durability, and user comfort. A good design must meet the goal while staying within constraints. The engineer might test a third material or combine materials to improve protection while reducing cost.

13. Mini Investigation: Make and Analyze Your Own Data

Investigation Question

How does the height of a ramp affect the distance a toy car travels?

Materials

• toy car
• ruler or meter stick
• board or cardboard ramp
• books
• tape
• flat floor space
• data table

Variables

Independent variable:

• ramp height

Dependent variable:

• distance the toy car travels

Controlled variables:

• same toy car
• same ramp surface
• same release point
• same floor surface
• no pushing
• same measuring method

Procedure

1. Set the ramp height to 10 cm.
2. Place the toy car at the top of the ramp.
3. Release the car without pushing.
4. Measure the distance from the bottom of the ramp to where the car stops.
5. Repeat three trials.
6. Test ramp heights of 20 cm, 30 cm, and 40 cm.
7. Calculate the average distance for each height.
8. Make a line graph or scatter plot.
9. Write a CER explanation.

Blank Data Table

Ramp Height (cm)	Trial 1 Distance (cm)	Trial 2 Distance (cm)	Trial 3 Distance (cm)	Average Distance (cm)
10
20
30
40

Investigation Reflection

Answer these questions after collecting data:

• What pattern did you observe?
• Was there an outlier?
• What could explain the results?
• How could you improve the investigation?
• What would you test next?

14. Discussion Prompts

Use these for partner, group, or class discussion.

1. Why is a graph sometimes easier to understand than a data table?
2. Can a graph ever be misleading? How?
3. Why do scientists need both creativity and careful measurement?
4. How is data used in everyday decisions, such as sports, shopping, weather, or health?
5. What makes a claim scientific?
6. Why might two scientists disagree about the same data?
7. When should scientists collect more data before making a conclusion?
8. How can engineering designs improve through repeated testing?
9. Why does a fair test usually change only one variable at a time?
10. How can data help communities solve environmental problems?

15. Learning Web Activity Ideas

Vocabulary Activity

Match each word to its definition:

• independent variable
• dependent variable
• evidence
• trend
• outlier
• scale
• system
• energy
• matter

Fill-in-the-Blank Activity

1. The variable changed on purpose is the _____ variable.
2. The variable measured is the _____ variable.
3. A _____ graph is useful for showing change over time.
4. A _____ graph is useful for comparing categories.
5. Data used to support a claim is called _____.

Category Sort Activity

Sort each item into qualitative or quantitative data:

• The solution turned green.
• The temperature was 32 degrees C.
• The leaf felt waxy.
• The plant was 18 cm tall.
• The rock had a mass of 74 g.
• The gas had a sharp odor.

Sequence Activity

Put these investigation steps in order:

• Collect data.
• Ask a question.
• Make a graph.
• Plan a fair test.
• Write a claim with evidence.
• Make a hypothesis.

Correct order:

1. Ask a question.
2. Make a hypothesis.
3. Plan a fair test.
4. Collect data.
5. Make a graph.
6. Write a claim with evidence.

Sentence Builder Activity

Build a scientific explanation using:

• My claim is...
• The evidence shows...
• This supports the claim because...

Example:

My claim is that higher ramp height made the toy car travel farther. The evidence shows that the car traveled 45 cm from a 10 cm ramp and 159 cm from a 40 cm ramp. This supports the claim because greater ramp height can give the car more gravitational potential energy.

16. Final Revision Checklist

Use this checklist before a quiz, discussion, investigation, or written response.

• I can define data, evidence, hypothesis, variable, system, energy, and matter.
• I can identify independent, dependent, and controlled variables.
• I can tell the difference between qualitative and quantitative data.
• I can choose a useful graph type for different data sets.
• I can read graph titles, axes, labels, units, and scales.
• I can describe trends using clear science language.
• I can use numbers from a table or graph as evidence.
• I can explain why repeated trials are useful.
• I can identify possible outliers and explain how to investigate them.
• I can avoid confusing correlation with causation.
• I can write a Claim-Evidence-Reasoning explanation.
• I can explain how data helps scientists and engineers improve ideas and designs.
• I can interpret data tables and ASCII graphs.
• I can identify common graphing mistakes.
• I have attempted the practice questions.
• I have reviewed the answer key and model answers.