Scientific Inquiry and Laboratory Practices

The Nature of Science is about how people learn about the world by observing, testing, and asking questions. Science helps us explain natural phenomena, which means things that happen in nature, like rain, plant growth, shadows, and the movement of animals.

Scientists do not just guess. They look for evidence. Evidence is information we can gather by using our senses and tools. For example, a scientist might measure how tall a plant grows, how warm the air is, or how much rain falls in a week.

One important idea about science is that it is based on empirical evidence. That means science depends on things people can observe, measure, and record. If we want to know whether a plant grows better in sunlight, we do not just say what we think. We test it and collect data.

Another important idea is that scientific knowledge can change. This does not mean science is weak. It means science is careful and honest. When new evidence is found, scientists may improve an old explanation or replace it with a better one.

Science also focuses on explaining the natural world. It asks questions about things we can observe in nature. For example, science can help explain why ice melts, why the Moon seems to change shape, or why some materials sink while others float.

Introduction: What makes science special?

Science is a way of learning. People have always wondered about the world. Why do leaves fall? Why do some animals come out at night? Why do puddles disappear after it stops raining?

Science helps answer questions like these by using observations, tests, and evidence. It does not depend on opinions alone. Instead, it looks for answers that match what we can actually see and measure.

Main Teaching Point 1: Science uses evidence

In science, evidence comes first. A person may have an idea, but that idea must be checked with observations or experiments. If the evidence does not match the idea, the idea may need to change.

For example, suppose someone says, “Plants only need water to grow.” That is an idea. To test it, we could grow one plant with sunlight and water, and another plant with water but no sunlight. Then we observe what happens.

Scientists often gather evidence by:

• carefully observing
• measuring with tools
• recording data
• comparing results
• repeating tests

Evidence should be as accurate as possible. That is why scientists use rulers, thermometers, balances, timers, and notebooks. These tools help people collect information they can trust.

Main Teaching Point 2: Science explanations can change

Sometimes scientists learn something new. When that happens, they may change an explanation. This is a strong part of science because it shows that scientists are willing to improve their understanding.

Imagine you think all heavy objects sink. Then you test a large log in water and see that it floats. Now you have new evidence. You would need to change your idea. Maybe it is not just weight that matters.

Scientific knowledge grows over time. New tools can help too. A better microscope or telescope can show things people could not see before. New observations can lead to better explanations.

This means science is always learning. It keeps asking, “What does the evidence show now?”

Main Teaching Point 3: Science explains natural phenomena

Science studies things in nature. These are things we can observe happening in the world around us.

Examples of natural phenomena include:

• rain falling from clouds
• a seed sprouting
• the Sun warming the ground
• rocks breaking into smaller pieces
• day changing to night

Science tries to explain how and why these things happen. It asks questions that can be explored with evidence.

For example, if we ask, “Why do some objects cast longer shadows at different times of day?” science can study sunlight and shadow length by measuring them at morning, noon, and afternoon.

Main Teaching Point 4: Observations and inferences are different

An observation is something you notice directly. You may use your senses or a tool. For example, “The plant is 12 centimeters tall” is an observation.

An inference is a smart idea based on observations. For example, “The plant is healthy because it is getting enough sunlight” is an inference.

Both are useful, but it is important to know the difference. Observations are the evidence. Inferences are explanations based on that evidence.

Main Teaching Point 5: Scientists share and check results

Science works best when people write down what they did and what they found. Then others can review it, ask questions, and even repeat the test.

If two groups test the same question, they should be able to compare their results. This helps make science more dependable.

When scientists communicate clearly, they help others learn. Good science is not secret guessing. It is careful work that can be explained and checked.

Worked Example 1: Using evidence to answer a question

Question: Do seeds sprout faster in a warm place or a cool place?

Step 1: Make a test. Put the same kind of seeds in two places. Keep the water and light the same. One place is warmer. One place is cooler.

Step 2: Observe and record. Count how many days it takes for each seed to sprout.

Step 3: Use the evidence. If the warm-place seeds sprout in fewer days, the evidence suggests that warmth helped the seeds sprout faster.

What this shows: Science uses evidence from observations, not just a guess.

Worked Example 2: Changing an idea when new evidence appears

Question: Does every object made of metal sink in water?

A student sees a metal spoon sink and thinks, “All metal objects sink.” Then the student tests a metal toy boat, and it floats.

New evidence: Some metal objects can float.

New conclusion: The first idea was not fully correct. The student must change the explanation.

What this shows: Scientific ideas can change when we find new evidence.

Worked Example 3: Observation or inference?

Situation: You see a puddle on the playground in the morning. In the afternoon, the puddle is gone.

Observation: “The puddle was there in the morning and gone in the afternoon.”

Inference: “The water dried up because the Sun warmed it.”

What this shows: Observations are what we directly notice. Inferences are explanations based on those observations.

Worked Example 4: Explaining a natural phenomenon

Question: Why do shadows change during the day?

Test: Place a stick in the ground. Measure the shadow in the morning, at noon, and in the afternoon.

Suppose the measurements are:

• Morning: 10 inches
• Noon: 4 inches
• Afternoon: 9 inches

Evidence: The shadow length changes at different times.

Explanation: The Sun appears in different parts of the sky during the day, so the shadow changes.

What this shows: Science explains natural events by observing and measuring them.

How to think like a scientist

1. Ask a question about the natural world.
2. Make careful observations.
3. Use tools to measure when possible.
4. Record data clearly.
5. Look for patterns.
6. Explain your results using evidence.
7. Be ready to change your idea if new evidence appears.

Important reminders

• Science is based on evidence.
• Science studies the natural world.
• Scientific ideas can improve over time.
• Observations and inferences are not the same.
• Sharing results helps others check the work.

Brief Summary

The nature of science is the way we learn about the natural world through evidence. Scientists observe, measure, test, and record what they find. They use that evidence to explain natural phenomena.

Science is also open to change. If new discoveries are made, explanations may be improved. That is what makes science such a powerful way to learn about our world.

Observation vs. Inference

Scientists learn about the world by paying close attention and thinking carefully about what they notice. Two important parts of scientific thinking are observation and inference.

These two ideas are connected, but they are not the same. Knowing the difference helps students become better scientists.

An observation is something you notice directly by using your senses or a tool. You can see it, hear it, smell it, touch it, or measure it.

An inference is a smart guess or idea based on observations and what you already know. An inference helps explain what might be happening, but it is not something you directly saw or measured.

Think of it this way:

• Observation = What I notice
• Inference = What I think that means

Why does this matter in science?

Science needs facts that can be checked. Observations are important because they are based on direct evidence. Inferences are also useful because they help scientists explain observations and ask new questions.

Good scientists know when they are reporting facts and when they are making a conclusion from those facts.

What counts as an observation?

Observations come from your senses and from tools. Some observations are made without measuring, and some are made with measuring.

• Using senses: The flower is yellow. The bell sounds loud. The soup smells spicy.
• Using tools: The thermometer reads 20 degrees. The ruler shows the pencil is 15 centimeters long. The scale says the apple has a mass of 120 grams.

Observations should be clear and specific. Instead of saying, “The rock is nice,” a better observation is, “The rock is smooth, gray, and cool to the touch.”

What counts as an inference?

Inferences are ideas you make after looking at observations. They often answer questions like:

• Why did this happen?
• What may happen next?
• What does this evidence suggest?

For example, if you observe dark clouds, strong wind, and thunder, you might infer that it is going to rain soon. You did not directly observe the future rain yet. You used clues to make a logical conclusion.

Clue words can help

Observation sentences often sound like this:

• I see...
• I hear...
• I measure...
• The data show...

Inference sentences often sound like this:

• I think...
• Maybe...
• This could mean...
• I infer that...

Be careful: not every sentence that starts with “I think” is wrong. Sometimes people use those words casually. What matters most is this question: Did I directly notice it, or am I figuring it out from clues?

Observation or inference?

1. “The ice cube is cold.” This is an observation because you can feel it.
2. “The ice cube was just taken out of the freezer.” This is an inference because you did not directly see where it came from.
3. “The plant is 12 centimeters tall.” This is an observation because it can be measured.
4. “The plant is healthy.” This is usually an inference because it is a conclusion based on signs like green leaves or strong growth.

Worked Example 1: Wet sidewalk

You walk outside in the morning and notice the sidewalk is wet.

Step 1: Observation

• The sidewalk is wet.
• There are small puddles.
• The grass has drops of water on it.

Step 2: Inference

• It may have rained last night.

Why? You directly observed water on the ground. You did not directly observe the rain happening. Rain is the conclusion you made from the evidence.

Worked Example 2: Empty dog bowl

You see a dog bowl on the floor.

Observations

• The bowl is empty.
• There are a few drops of water in it.
• The dog is standing next to the bowl and looking at it.

Inference

• The dog is probably thirsty.

Why? You can see the empty bowl and the dog’s actions. But you cannot directly see thirst. Thirst is an idea based on the clues.

Worked Example 3: Classroom plant

A class is watching a plant by the window.

Observations

• Two leaves are brown.
• The soil feels dry.
• The plant is leaning toward the window.
• The height of the plant is 18 centimeters.

Inference

• The plant may need water.
• The plant may be growing toward the sunlight.

Why? Brown leaves and dry soil are facts you can notice. Saying the plant needs water is a conclusion from those facts. Seeing the plant lean is an observation. Deciding that sunlight is the reason is an inference.

Worked Example 4: Mystery footprints

You are on the playground after recess.

Observations

• There are muddy footprints near the door.
• The footprints are small.
• The floor inside the door is dirty.

Inference

• A student walked through mud before coming inside.

Why? You saw the prints and dirt. You did not directly watch the student step in the mud, so that part is inferred.

How observations and inferences work together

Observations give you the evidence. Inferences help you explain the evidence.

Scientists often follow this pattern:

1. Observe carefully.
2. Write down facts.
3. Look for patterns or clues.
4. Make an inference.
5. Check if more observations support that inference.

For example, if a thermometer reads 30 degrees Celsius, you observe the measurement. If you say, “It will be a hot day,” that is an inference based on the observation.

A helpful test

If you are not sure whether a sentence is an observation or an inference, ask:

• Could another person notice or measure the same thing right now? If yes, it is probably an observation.
• Am I explaining what I think happened or what might happen? If yes, it is probably an inference.

Let’s practice

Read each sentence and decide whether it is an observation or an inference.

1. The candle flame is bright orange. Observation
2. The candle was just lit. Inference
3. The jar contains 10 marbles. Observation
4. Someone played with the marbles earlier. Inference
5. The baby is crying loudly. Observation
6. The baby is tired. Inference

Common mistakes to avoid

• Mixing facts with ideas: “The boy is sad” may sound simple, but sadness is usually an inference unless the person tells you directly.
• Using opinion words: Words like “pretty,” “gross,” or “best” are opinions, not good scientific observations.
• Forgetting tools: Measurements from rulers, scales, and thermometers are observations too.

Mini challenge

Imagine you see this:

• A lunch box is open.
• A banana peel is on the table.
• A napkin is crumpled next to it.

What is one observation and one inference?

Possible answer:

• Observation: A banana peel is on the table.
• Inference: Someone ate a banana.

Summary

An observation is something you notice directly with your senses or measure with a tool. An inference is a conclusion you make based on those observations.

In science, both are important. First, collect careful observations. Then use those facts to make reasonable inferences. When you know the difference, you think more like a scientist.

Qualitative and Quantitative Data

When scientists do investigations, they collect data. Data is information we gather by observing, measuring, and recording what happens.

There are two important kinds of data: qualitative data and quantitative data. Knowing the difference helps scientists describe what they notice and measure what they find.

In this lesson, you will learn what each type of data means, how to tell them apart, and why both are useful in science.

What is qualitative data?

Qualitative data tells about the qualities or characteristics of something. It describes what something is like.

Qualitative data usually uses words, not numbers. It can tell about:

• color
• shape
• texture
• smell
• sound
• how something looks or feels

Examples of qualitative data:

• The flower is yellow.
• The rock feels rough.
• The liquid smells sweet.
• The bird made a high sound.

What is quantitative data?

Quantitative data tells about an amount, a number, or a measurement. It answers questions like how many, how much, how long, or how heavy.

Quantitative data uses numbers. Scientists often collect it with tools such as rulers, scales, measuring cups, clocks, and thermometers.

Examples of quantitative data:

• The plant is 12 centimeters tall.
• There are 8 worms in the soil.
• The water temperature is 20 degrees.
• The apple weighs 150 grams.

An easy way to remember

• Qualitative = quality = description
• Quantitative = quantity = number

If the data uses describing words, it is probably qualitative. If the data uses numbers or measurements, it is probably quantitative.

Why do scientists use both kinds of data?

Scientists use qualitative data to describe what they observe. This helps them notice details that numbers cannot show by themselves.

Scientists use quantitative data to measure carefully and compare results. Numbers can help scientists see changes more clearly.

Both kinds of data are important because they work well together. A scientist might describe a plant as green and healthy and also measure that it is 18 centimeters tall.

Main differences

• Qualitative data is descriptive.
• Quantitative data is numerical.
• Qualitative data often comes from the senses: sight, touch, smell, sound, and sometimes taste if it is safe.
• Quantitative data often comes from measuring tools and counting.

Worked Example 1: Sorting observations

A student observes a leaf and writes these notes:

• The leaf is dark green.
• The leaf is 9 centimeters long.
• The edges feel smooth.
• The leaf has 5 brown spots.

Step 1: Look for descriptions and numbers.

• Dark green is a description, so it is qualitative.
• 9 centimeters long is a measurement, so it is quantitative.
• Smooth is a description, so it is qualitative.
• 5 brown spots includes a count, so it is quantitative.

Answer:

• Qualitative: dark green, smooth
• Quantitative: 9 centimeters long, 5 brown spots

Worked Example 2: Observing water in science class

A class studies a cup of water. They record:

• The water looks clear.
• The cup holds 200 milliliters of water.
• The water feels cool.
• The temperature is 18 degrees.

Step 1: Ask, “Is this a description or a number?”

• Looks clear is qualitative.
• 200 milliliters is quantitative.
• Feels cool is qualitative.
• 18 degrees is quantitative.

Answer:

• Qualitative data describes the water: clear, cool.
• Quantitative data measures the water: 200 milliliters, 18 degrees.

Worked Example 3: Plant investigation

Two plants are growing by a window. A student writes:

• Plant A has bright green leaves.
• Plant A is 14 centimeters tall.
• Plant B has pale green leaves.
• Plant B is 11 centimeters tall.

Step 1: Find the qualitative data.

Bright green leaves and pale green leaves are descriptions, so they are qualitative data.

Step 2: Find the quantitative data.

14 centimeters and 11 centimeters are measurements, so they are quantitative data.

Step 3: Use both kinds of data.

The student can say Plant A is taller because $$14 > 11$$. The student can also describe the color difference between the plants.

Answer: The color observations are qualitative, and the height measurements are quantitative.

Worked Example 4: Studying rocks

A group of students studies a rock sample. They record:

• The rock is gray with white lines.
• The rock has a rough surface.
• The rock weighs 300 grams.
• The rock is 7 centimeters long.

Step 1: Descriptions are qualitative.

• Gray with white lines
• Rough surface

Step 2: Numbers and measurements are quantitative.

• 300 grams
• 7 centimeters long

Answer: Scientists can use the qualitative data to describe the rock and the quantitative data to compare it to other rocks.

Tips for telling them apart

1. Look for numbers. If you see a count or measurement, it is usually quantitative.
2. Look for describing words. If it tells what something is like, it is usually qualitative.
3. Ask yourself: “Can I measure it or count it?” If yes, it is probably quantitative.
4. Ask yourself: “Am I describing how it looks, feels, smells, or sounds?” If yes, it is probably qualitative.

Be careful!

Sometimes a sentence can have both kinds of information. For example:

The 3 flowers are red.

• 3 flowers is quantitative because it tells how many.
• red is qualitative because it describes color.

This means one observation can include both qualitative and quantitative data.

How scientists record data

Scientists try to record data clearly and carefully. They may use:

• science notebooks
• data tables
• drawings with labels
• charts and graphs for numerical data

Good scientists also use safe tools correctly and write exactly what they observe. They do not guess. They record what they really see and measure.

Quick practice

Decide if each one is qualitative or quantitative:

• The butterfly has orange wings. → Qualitative
• The jar contains 25 marbles. → Quantitative
• The sand feels gritty. → Qualitative
• The stick is 30 centimeters long. → Quantitative

Summary

Qualitative data uses words to describe characteristics, such as color, texture, smell, and sound. Quantitative data uses numbers to count or measure, such as length, weight, temperature, and amount.

Scientists need both types of data. Qualitative data helps explain what something is like, and quantitative data helps show how much, how many, or how big. When you can tell these two kinds of data apart, you are thinking like a scientist.

Lesson: Testable Hypotheses

Scientists ask questions about the world. Then they make a smart guess they can check by doing an investigation. That smart guess is called a hypothesis.

A testable hypothesis is a prediction that can be checked with an experiment, careful observation, or measurement. It should tell what you think will happen and be clear enough that someone can find out if it is right or wrong.

In science, it is okay if a hypothesis turns out to be wrong. Scientists learn from results either way. What matters is that the hypothesis can be tested.

What makes a hypothesis testable?

• It is about something you can observe. You can see it, hear it, count it, or measure it.
• It compares things. It often looks at how one thing changes another thing.
• It makes a prediction. It says what you think will happen.
• It can be shown true or false. The results can support it or not support it.

Many testable hypotheses use an If..., then... form.

Example pattern: If one thing changes, then another thing will happen.

For example: If a plant gets more sunlight, then it will grow taller.

This is testable because we can give plants different amounts of sunlight and measure their height.

Variables in a hypothesis

A variable is something that can change in an investigation.

• Changed variable: the one thing you change on purpose.
• Measured variable: the thing you watch, count, or measure to see what happens.

In the hypothesis If a plant gets more sunlight, then it will grow taller:

• The changed variable is the amount of sunlight.
• The measured variable is the plant's height.

To make a fair test, scientists try to keep other things the same, like the kind of plant, amount of water, and size of the pot.

How to write a testable hypothesis

1. Start with a science question.
2. Think about what one thing you will change.
3. Think about what you will observe or measure.
4. Write a prediction using clear words.

Question: Does the amount of water change how fast a bean plant grows?

Hypothesis: If a bean plant gets more water, then it will grow faster.

This works because we can change the amount of water and measure growth over time.

What is not a testable hypothesis?

Some ideas are interesting, but they are not testable in a science investigation.

• Opinions: "Chocolate ice cream is the best flavor."
• Too vague: "Plants are nice."
• Not measurable: "A song makes plants happy."

These do not clearly tell what to measure, or they are based on feelings instead of evidence.

Better versions

• Instead of "Plants are nice," say "If a plant gets sunlight, then it will grow more leaves."
• Instead of "A song makes plants happy," say "If plants hear music each day, then they will grow taller than plants that do not hear music."

Now the ideas can be tested because we can count leaves or measure height.

Worked Example 1: Simple testable hypothesis

Question: Do seeds sprout faster in warm places?

Step 1: Changed variable — temperature of the place

Step 2: Measured variable — how many days it takes seeds to sprout

Hypothesis: If seeds are kept in a warmer place, then they will sprout in fewer days.

Why it is testable: We can place seeds in different temperatures and count the days until they sprout.

Worked Example 2: Choosing the better hypothesis

Question: Does the size of a ramp change how far a toy car travels?

Which statement is testable?

• A. Toy cars like big ramps.
• B. If a toy car rolls down a taller ramp, then it will travel farther.

Answer: B is testable.

Why? We can change the ramp height and measure how far the car goes. Statement A is not testable because "like" is a feeling, and we cannot measure it for a toy car.

Worked Example 3: Fixing a weak hypothesis

Question: Does exercise change heart rate?

Weak hypothesis: Exercise is good for you.

This is not a good testable hypothesis for this question. It does not say what will be changed or measured.

Better hypothesis: If a person exercises for 5 minutes, then their heart rate will be higher than before exercise.

Why it is better:

• It tells what changes: exercise for 5 minutes.
• It tells what is measured: heart rate.
• It makes a clear prediction: heart rate will be higher.

Worked Example 4: Looking at variables carefully

Question: Does adding more paper clips to a paper airplane change how far it flies?

Hypothesis: If more paper clips are added to a paper airplane, then the airplane will fly a shorter distance.

Changed variable: number of paper clips

Measured variable: distance the airplane flies

Keeping the test fair:

• Use the same kind of paper.
• Fold each airplane the same way.
• Throw the airplane from the same place.
• Try to throw with the same force each time.

This helps us know whether the paper clips caused the change.

Helpful question starters

These question starters can help you create a testable hypothesis:

• Does ___ change ___?
• How does ___ affect ___?
• What happens to ___ when ___ changes?

Helpful hypothesis starters

• If ___ changes, then ___ will ___.
• If we increase ___, then ___ will ___.
• If we decrease ___, then ___ will ___.

Check your hypothesis

Ask yourself these questions:

• Did I make a prediction?
• Can I test it by observing or measuring?
• Did I include what changes?
• Did I include what I will measure?
• Could the results show I was wrong?

If you can answer yes to these questions, your hypothesis is probably testable.

Practice thinking

Here are some questions and possible hypotheses. Notice which ones are testable.

• Question: Does more study time help students remember spelling words?
  Testable hypothesis: If students study spelling words for more minutes, then they will spell more words correctly on the test.
• Question: Does the kind of soil affect plant growth?
  Testable hypothesis: If plants grow in garden soil instead of sand, then they will grow taller.
• Question: Do larger magnets pick up more paper clips?
  Testable hypothesis: If a magnet is larger, then it will pick up more paper clips.

Remember: A hypothesis is not just any guess. It is a scientific prediction that can be checked with evidence.

Brief Summary

A testable hypothesis is a clear prediction about what will happen when one thing changes. It should include what you will change and what you will observe or measure. Scientists use testable hypotheses to guide investigations and learn from real evidence.

Variables and Controls help scientists make a fair test. A fair test is an experiment where you change one thing and watch what happens.

When scientists do experiments, they want to know what caused the change. If too many things change at once, it is hard to tell why the results happened. That is why scientists use variables and controls.

In this lesson, you will learn what independent variables, dependent variables, control variables, and a control group are. You will also learn how to use them to plan a fair test.

What is a variable?

A variable is anything in an experiment that can change.

For example, in a plant experiment, these things could be variables:

• how much water the plant gets
• how much sunlight it gets
• what kind of soil it grows in
• how tall the plant becomes

Some variables are changed on purpose. Some are measured. Some must stay the same.

1. Independent Variable

The independent variable is the one thing you change on purpose.

This is what the scientist decides to test.

Ask yourself: What am I changing?

Example: If you want to know whether plants grow better with more water, then the amount of water is the independent variable.

2. Dependent Variable

The dependent variable is what you measure or observe.

It changes because of the independent variable.

Ask yourself: What am I watching or measuring?

Example: If you are changing the amount of water, you might measure plant height. Plant height is the dependent variable.

3. Control Variables

Control variables are the things you keep the same in every part of the experiment.

These help make the test fair.

Ask yourself: What must stay the same?

In a plant test, control variables might be:

• same kind of plant
• same size pot
• same type of soil
• same amount of sunlight
• same number of days

If one plant gets more sunlight and more water, then you would not know which change made the difference. That would not be a fair test.

4. Control Group

A control group is the group that does not get the change you are testing.

It is used for comparison.

For example, if one group of plants gets fertilizer and another group does not, the group with no fertilizer is the control group.

The control group helps scientists see whether the change really made a difference.

How to Build a Fair Test

1. Choose a question.
2. Change only one thing.
3. Measure what happens.
4. Keep all other important things the same.
5. If possible, compare with a control group.

Helpful Question Words

• Independent variable: What am I changing?
• Dependent variable: What am I measuring?
• Control variables: What am I keeping the same?
• Control group: Which group does not get the change?

Worked Example 1: Which paper towel holds more water?

Question: Which brand of paper towel holds the most water?

Let's identify the parts of the fair test.

• Independent variable: the brand of paper towel
• Dependent variable: how much water each towel holds
• Control variables: same towel size, same amount of time in water, same way of measuring
• Control group: This test may not need a control group because you are comparing brands to each other.

Why is this fair? Only the brand changes. Everything else stays the same.

Worked Example 2: Do plants grow taller with more water?

Question: Does giving plants more water help them grow taller?

• Independent variable: amount of water
• Dependent variable: plant height
• Control variables: same kind of plant, same soil, same pot size, same sunlight, same number of days
• Control group: the plant group getting the normal amount of water

Why is this fair? The scientist changes only the amount of water. The height is measured. The other conditions stay the same.

Suppose Plant A gets 1 cup of water each day and Plant B gets 2 cups each day. After 10 days, you measure both plants.

If Plant A is 12 cm tall and Plant B is 15 cm tall, you can compare them.

The difference is:

$$15 - 12 = 3$$

Plant B is 3 cm taller. Because the other conditions stayed the same, the water amount may have caused the change.

Worked Example 3: Which ramp makes a toy car go farther?

Question: Does the height of a ramp change how far a toy car travels?

• Independent variable: ramp height
• Dependent variable: distance the car travels
• Control variables: same toy car, same ramp surface, same floor, same starting point, same way of letting go
• Control group: the lowest ramp height could be used as the control group for comparison

Why is this fair? You change only the ramp height. Then you measure the distance.

Example distances:

• Low ramp: 80 cm
• Medium ramp: 110 cm
• High ramp: 140 cm

You can compare how the dependent variable changed as the independent variable changed.

Worked Example 4: Does fertilizer help bean plants grow?

Question: Does fertilizer help bean plants grow taller?

• Independent variable: fertilizer or no fertilizer
• Dependent variable: plant height
• Control variables: same bean seeds, same water, same pots, same soil, same sunlight, same time growing
• Control group: the plants with no fertilizer

Suppose after 2 weeks, the plants with fertilizer are 18 cm tall and the plants without fertilizer are 14 cm tall.

The difference is:

$$18 - 14 = 4$$

The fertilizer group is 4 cm taller.

Because the plants were treated the same in every other way, this is a fair test.

How to Spot Mistakes in an Experiment

Sometimes a test is not fair. Here are common mistakes:

• changing more than one thing at a time
• not measuring the results carefully
• giving groups different conditions besides the one being tested
• forgetting to use a control group when one would help

Example of an unfair test: A student gives one plant more water and puts it in more sunlight. If the plant grows taller, we do not know whether water or sunlight caused the change.

A Simple Way to Remember

• Independent variable: I change it.
• Dependent variable: I measure it.
• Control variables: I keep them the same.
• Control group: It does not get the change.

Practice Thinking

If you test whether music helps students read faster, ask:

• What am I changing? music or no music
• What am I measuring? reading speed
• What stays the same? same reading time, same type of story, same room
• What is the control group? the group with no music

Why Variables and Controls Matter

Scientists want results they can trust. When they use variables and controls the right way, they can better understand what caused the results.

This helps scientists share clear, honest information with others.

Summary

In a fair test, you change only one thing on purpose. That is the independent variable.

You measure what happens. That is the dependent variable.

You keep other important things the same. Those are the control variables.

You may also use a control group, which does not get the change you are testing.

When you use variables and controls correctly, your experiment is fair, and your results make more sense.

Scientific Modeling is a way scientists use ideas, drawings, objects, and simple numbers to help them understand the world.

A model is something that stands for something else. It helps us explain, study, or predict what might happen.

Scientists use models because some things are too big, too small, too far away, too fast, or too hard to study directly.

For example, a globe is a model of Earth. A drawing of the water cycle is a model. A chart that shows how a plant grows over time is also a model.

In this lesson, you will learn what scientific models are, the different kinds of models, how they help us, and why models are useful but not perfect.

What Is a Scientific Model?

A scientific model is a tool for thinking and learning. It helps people answer questions about how something looks, works, or changes.

Models do not have to look exactly like the real thing. They only need to show the important parts clearly.

Scientists ask, “What do I need this model to show?” Then they choose the best kind of model for the job.

Types of Scientific Models

There are three common kinds of scientific models you may use in 4th grade science.

1. Physical models

A physical model is something you can see and touch.

It is often smaller, bigger, or simpler than the real thing.

• A globe is a physical model of Earth.
• A model of the solar system is a physical model.
• A toy skeleton is a physical model of bones in a body.

Why physical models help:

• They let us see shapes and parts.
• They help us notice where things are placed.
• They make hard ideas easier to picture.

2. Conceptual models

A conceptual model is a model made from ideas, labels, arrows, or drawings that explain how something works.

You may not be able to hold it in your hands, but it helps your brain understand a process.

• A food chain drawing is a conceptual model.
• A diagram of the water cycle is a conceptual model.
• A life cycle chart of a butterfly is a conceptual model.

Why conceptual models help:

• They show steps in order.
• They show how parts are connected.
• They help explain causes and effects.

3. Mathematical models

A mathematical model uses numbers, tables, charts, graphs, or simple equations to show patterns.

It helps scientists measure change and make predictions.

• A table showing plant height each week is a mathematical model.
• A bar graph of rainfall is a mathematical model.
• A simple rule like “2 centimeters of growth each week” is a mathematical model.

Why mathematical models help:

• They show patterns clearly.
• They help compare information.
• They can help us predict what may happen next.

Why Scientists Use Models

Scientists use models to learn about things they cannot always test or see easily.

• Some things are too big, like Earth or the solar system.
• Some things are too small, like tiny parts of plants and animals.
• Some things take too long, like changes in land over many years.
• Some things happen too fast, like a sudden storm.
• Some things may be too dangerous to test directly.

Models help scientists ask questions, test ideas, and share what they learn with others.

What Makes a Good Model?

A good scientific model should do a few important jobs.

• It should show the most important parts.
• It should be clear and easy to understand.
• It should match what we observe in real life.
• It should help explain or predict something.

Sometimes scientists improve a model after they gather new evidence. That means models can change when we learn more.

Models Can Help Predict

One special job of a model is to help us make a prediction. A prediction is a careful guess about what may happen.

If a plant grows 2 centimeters each week, a mathematical model can help us predict its future height.

If the plant starts at 3 centimeters, then after 1 week it may be:

$$3 + 2 = 5$$

After 2 weeks, it may be:

$$5 + 2 = 7$$

We can also write this as:

$$3 + 2 + 2 = 7$$

The model helps us think about what might happen next.

Models Have Limits

Even good models are not perfect. A model is only a helpful copy or idea. It is not the real thing.

For example, a globe shows Earth’s shape, but it does not show every road, tree, or building.

A drawing of the water cycle shows the main steps, but it may not show every tiny detail about clouds and weather.

A graph of plant growth shows numbers, but it may not explain why one plant grew faster than another.

When using a model, it is important to ask:

• What does this model show well?
• What does this model leave out?
• How is this model different from the real thing?

How to Build or Choose a Model

When making or choosing a scientific model, you can follow these steps:

1. Ask what you want to learn.
2. Choose the type of model that fits the question.
3. Include the most important parts.
4. Label clearly.
5. Check whether the model matches observations.
6. Improve the model if needed.

Worked Example 1: Physical Model

Question: Why might a student use a globe instead of looking at the ground outside to learn about Earth?

Step 1: Think about the job of the globe.

The globe is a physical model of Earth.

Step 2: Think about what it shows.

It shows Earth’s round shape, land, water, and where places are.

Step 3: Decide why it helps.

When you stand outside, you can only see a small part of Earth. A globe helps you see the whole planet in a simple way.

Answer: A student uses a globe because it helps show the whole Earth and where places are, which is hard to see from one spot outside.

Worked Example 2: Conceptual Model

Question: A class draws the water cycle with arrows from water to clouds to rain. What kind of model is this, and why is it useful?

Step 1: Look at what the model uses.

It uses drawings and arrows to show steps.

Step 2: Name the model type.

This is a conceptual model.

Step 3: Explain why it helps.

It helps people understand the order of the steps and how water moves through the cycle.

Answer: It is a conceptual model because it uses ideas, pictures, and arrows to explain a process. It is useful because it shows the steps clearly.

Worked Example 3: Mathematical Model

Question: A seedling is 4 centimeters tall. It grows 3 centimeters each week. How tall will it be after 2 weeks?

Step 1: Start with the beginning height.

Beginning height: 4 centimeters

Step 2: Add the growth for 2 weeks.

Growth each week: 3 centimeters

In 2 weeks, growth is:

$$3 + 3 = 6$$

Step 3: Add the total growth to the starting height.

$$4 + 6 = 10$$

Answer: After 2 weeks, the seedling will be 10 centimeters tall.

This mathematical model helps us predict future growth.

Worked Example 4: Thinking About Limits

Question: A model volcano looks like a real volcano, but it does not show heat deep inside Earth. Is this model still useful?

Step 1: Think about what the model shows well.

It may show the shape of the volcano and where lava comes out.

Step 2: Think about what it leaves out.

It does not show everything happening under the ground.

Step 3: Decide if it can still help.

Yes. A model can still be useful even if it does not include every detail.

Answer: Yes, it is still useful because it helps show some important parts of a volcano, even though it leaves out other parts.

Tips for Students

• Ask, “What is this model trying to show?”
• Look for labels, arrows, sizes, shapes, numbers, or patterns.
• Remember that models help us learn, but they are not perfect copies.
• Use the model together with observations and evidence.

Let’s Review

A scientific model is something that helps us understand a real object, system, or process.

Physical models are objects you can touch, like globes. Conceptual models are drawings or diagrams that explain ideas, like the water cycle. Mathematical models use numbers, tables, graphs, or equations to show patterns and help make predictions.

Models are useful because they make science easier to study and explain. But every model has limits, so we should always think about what the model shows and what it leaves out.

Pattern Recognition and Trend Analysis means looking at information carefully to see what happens again and again, what changes, and what stands out.

In science, we collect information, or data. Data can be numbers, pictures, marks on a chart, or things we notice with our eyes.

When we study data, we ask simple questions like: What do I see? What happens most of the time? Is something different?

Learning to find patterns helps us answer our science question. It helps us make smart ideas based on what we observed.

What is a pattern?

A pattern is something that repeats in a way we can notice.

Here are some simple patterns:

• red, blue, red, blue
• sunny, rainy, sunny, rainy
• \(1, 2, 1, 2\)

In science, patterns can happen in weather, plant growth, animal behavior, and many other things.

What is a trend?

A trend tells us how something is changing.

• If something gets bigger and bigger, that is an up trend.
• If something gets smaller and smaller, that is a down trend.
• If it stays about the same, that is a same trend.

When we look for trends, we are asking: Is it going up, going down, or staying the same?

What is something different?

Sometimes most of the data follows a pattern, but one part does not. That part is something different.

For example, if a plant grows a little each day, but one day it does not grow at all, that day is different from the others. Scientists notice that and ask, Why was that day different?

How do scientists find patterns?

1. Look at the data carefully.
2. Say what you notice.
3. Check if something repeats.
4. Check if the data goes up, down, or stays the same.
5. Look for anything different.
6. Use the data to answer the science question.

Things we can use to help us see patterns

• Tally marks
• Picture charts
• Simple tables
• Bar graphs
• Drawings in a science notebook

These tools help our eyes see the data more clearly.

Main idea 1: Repeating patterns

A repeating pattern happens when the same order happens again and again.

If we see: sprout, flower, sprout, flower, sprout, flower, we can tell the pattern repeats.

Not all science data repeats exactly, but sometimes it does. When it does, that helps us predict what may come next.

Main idea 2: Up, down, or same

Many science questions are about change. A seedling may grow taller. A puddle may get smaller. The temperature outside may stay about the same for a few hours.

We can describe these changes in simple ways:

• Up: more, taller, bigger, warmer
• Down: less, shorter, smaller, cooler
• Same: no big change

Main idea 3: Find what does not match

If most of the data shows one pattern, but one part does not match, we should notice it.

This does not mean the data is wrong. It means we should think carefully. Maybe something changed. Maybe we need to look again. Maybe that point gives us a clue.

Main idea 4: Use evidence

In science, we do not just guess. We use evidence.

Evidence is what we observed and recorded. We can say, I know this because the chart shows it or I know this because I counted it.

Worked Example 1: Find a repeating pattern

A class watches the sky for 4 days. They record:

sunny, cloudy, sunny, cloudy

Step 1: Look at the order.

It goes sunny, cloudy, then sunny, cloudy again.

Step 2: Ask if it repeats.

Yes. The pattern repeats.

Answer: The repeating pattern is sunny, cloudy.

Worked Example 2: Find an up trend

A child measures a plant for 4 days.

• Day 1: \(2\) blocks tall
• Day 2: \(3\) blocks tall
• Day 3: \(4\) blocks tall
• Day 4: \(5\) blocks tall

Step 1: Compare the numbers.

They are \(2, 3, 4, 5\).

Step 2: Ask what is happening.

The plant gets taller each day.

Answer: This data shows an up trend. The plant is growing.

We can show it like this:

$$2 < 3 < 4 < 5$$

Worked Example 3: Find a down trend

A puddle is measured during a sunny day.

• Morning: \(5\) handprints wide
• Later: \(4\) handprints wide
• Later: \(3\) handprints wide
• Later: \(2\) handprints wide

Step 1: Look at the numbers.

\(5, 4, 3, 2\)

Step 2: Ask what is happening.

The puddle gets smaller.

Answer: This data shows a down trend.

We can show it like this:

$$5 > 4 > 3 > 2$$

Worked Example 4: Find what is different

A class counts birds at the feeder each morning.

• Monday: \(3\)
• Tuesday: \(3\)
• Wednesday: \(3\)
• Thursday: \(1\)

Step 1: Look for what happens most.

Most days show \(3\) birds.

Step 2: Look for what is different.

Thursday has only \(1\) bird.

Answer: Thursday is different from the other days.

A scientist might ask, Why were fewer birds there on Thursday?

How pattern finding helps answer science questions

Let us say our question is: Does a plant grow over time?

If our measurements go \(1, 2, 3, 4\), we see an up trend. That helps us answer, Yes, the plant is growing over time.

Let us say our question is: Does the weather stay the same every day?

If we record sunny, rainy, windy, sunny, then the weather does not stay the same every day. The data helps us answer the question.

Words you can use when talking about data

• I notice...
• The pattern is...
• It repeats...
• It is going up.
• It is going down.
• It stays the same.
• This part is different.
• I know because the data shows...

Try thinking like a scientist

If you see these leaf counts on a plant:

• Day 1: \(1\) leaf
• Day 2: \(2\) leaves
• Day 3: \(2\) leaves
• Day 4: \(3\) leaves

You might say:

• The number of leaves mostly goes up.
• From Day 2 to Day 3, it stayed the same.
• There is no repeating pattern, but there is a growth trend.

That is good science thinking.

Tips for doing a good job

• Look carefully.
• Count slowly and correctly.
• Record what you see.
• Compare one part to another.
• Use the data, not just a guess.

Brief Summary

Pattern recognition means finding what repeats, what changes, and what is different in data.

Trend analysis means telling if data is going up, going down, or staying the same.

When we use patterns and trends, we can answer science questions using evidence from our observations.

Claim, Evidence, and Reasoning (CER) is a way scientists explain their thinking.

When scientists do an investigation, they do not just give an answer. They also explain what they think, what they observed, and why the observations support the answer. This is called CER.

CER stands for:

• Claim — the answer to the question
• Evidence — the facts, observations, or data that support the claim
• Reasoning — the science idea that explains why the evidence supports the claim

Learning CER helps you sound like a scientist. It helps you give strong answers instead of guesses.

1. What is a Claim?

A claim is your answer to a science question.

A good claim is:

• clear
• short
• able to be supported with evidence

For example, if the question is, Which plant grew taller in sunlight or in shade?, a claim could be: The plant in sunlight grew taller.

The claim should answer the question directly. It should not include every detail. Those details belong in the evidence and reasoning.

2. What is Evidence?

Evidence is the information that supports your claim.

Evidence can come from:

• measurements
• observations
• charts or tables
• notes from an investigation

Good evidence is specific. It tells exactly what happened.

For example, instead of saying, The plant looked bigger, better evidence would be: After 3 weeks, the plant in sunlight was 14 centimeters tall, and the plant in shade was 9 centimeters tall.

Scientists often use numbers as evidence because numbers are easier to check. This is called data.

Here is a simple comparison:

• Weak evidence: The sunlight plant was better.
• Strong evidence: The sunlight plant grew to 14 cm, while the shade plant grew to 9 cm.

3. What is Reasoning?

Reasoning explains why your evidence supports your claim.

This is the part where you connect your observations to a science idea you know.

For the plant example, the reasoning could be: Plants need sunlight to make food and grow well, so the plant with more sunlight grew taller.

Reasoning is important because it shows your thinking. Without reasoning, a reader may know your answer and your data, but not understand how they fit together.

Think of CER like this:

• Claim: What do I think?
• Evidence: What did I observe?
• Reasoning: Why does that observation make sense?

4. How CER Works Together

The three parts work as a team.

• The claim answers the question.
• The evidence proves the claim.
• The reasoning explains the science behind it.

If one part is missing, the answer is weaker.

For example:

• If you only have a claim, it may sound like a guess.
• If you have a claim and evidence, but no reasoning, your thinking is not fully explained.
• If you have all three, your answer is stronger and clearer.

5. Steps for Writing a CER Answer

1. Read the question carefully. Find out exactly what you need to answer.
2. Write your claim. Answer the question in one clear sentence.
3. Choose your evidence. Use observations or data from the investigation.
4. Add your reasoning. Explain the science idea that connects the evidence to the claim.
5. Check your work. Make sure all three parts are included.

6. Helpful Question Starters

You can use sentence starters to help write CER.

Claim starters:

• I claim that...
• The results show that...
• The best answer is...

Evidence starters:

• One piece of evidence is...
• The data show...
• In the investigation, we observed...

Reasoning starters:

• This evidence supports the claim because...
• This makes sense because...
• According to what we know about science,...

7. Worked Example 1: Ice Melting

Question: Did ice melt faster in the sun or in the shade?

Investigation data:

• Ice cube in sun melted in 10 minutes.
• Ice cube in shade melted in 18 minutes.

Claim: Ice melted faster in the sun.

Evidence: The ice cube in the sun melted in 10 minutes, but the ice cube in the shade took 18 minutes.

Reasoning: The sun gives off heat energy. More heat makes ice melt faster, so the ice in the sun melted sooner.

Why this works: The claim answers the question. The evidence gives exact times. The reasoning explains that heat helps melt ice.

8. Worked Example 2: Which Ramp Made a Toy Car Go Farther?

Question: Did a toy car travel farther on a steep ramp or a gentle ramp?

Investigation data:

• Steep ramp distance: 120 cm
• Gentle ramp distance: 75 cm

Claim: The toy car traveled farther on the steep ramp.

Evidence: The car went 120 cm on the steep ramp and 75 cm on the gentle ramp. The steep ramp distance was greater.

Reasoning: A steeper ramp can make the car move faster as it rolls down. When the car starts faster, it can travel farther across the floor.

Why this works: The evidence uses measurements. The reasoning connects the ramp shape to the car's motion.

9. Worked Example 3: Which Material Absorbed More Water?

Question: Did the paper towel or the plastic wrap absorb more water?

Investigation observations:

• The paper towel soaked up the spilled water.
• The plastic wrap did not soak up water. The water stayed on top.

Claim: The paper towel absorbed more water than the plastic wrap.

Evidence: The paper towel soaked up the water, while the plastic wrap did not absorb it.

Reasoning: Some materials are absorbent, which means they can soak up liquids. Paper towel is absorbent, but plastic wrap is not, so the paper towel absorbed more water.

Why this works: This example uses observations instead of numbers. Evidence can be numbers or careful observations.

10. Worked Example 4: Which Soil Helped Seeds Sprout Better?

Question: Did seeds sprout better in garden soil or sandy soil?

Investigation data:

• Garden soil: 8 out of 10 seeds sprouted
• Sandy soil: 3 out of 10 seeds sprouted

Claim: Seeds sprouted better in garden soil.

Evidence: In garden soil, 8 of 10 seeds sprouted. In sandy soil, only 3 of 10 seeds sprouted. Since 8 is greater than 3, more seeds sprouted in garden soil.

Reasoning: Seeds need the right amount of water, air, and support to grow. Garden soil often holds water and nutrients better than sandy soil, so more seeds were able to sprout.

Why this works: This CER uses data and a science idea about what seeds need to grow.

11. How to Tell If an Answer Is Strong

A strong CER answer should:

• answer the question clearly
• use facts from the investigation
• explain why the facts support the answer

Ask yourself:

• Did I write a clear claim?
• Did I include evidence from observations or data?
• Did I explain my reasoning with science ideas?

12. Common Mistakes to Avoid

• Mistake: Writing a claim that does not answer the question.
  Fix: Read the question again and answer it directly.
• Mistake: Giving opinions instead of evidence.
  Fix: Use measurements, observations, or data.
• Mistake: Forgetting the reasoning.
  Fix: Add a sentence that explains why the evidence fits the claim.
• Mistake: Using evidence that does not match the claim.
  Fix: Make sure your facts actually support your answer.

13. Quick Practice Thinking

If a student says, I think the darker rock got hotter, that is only a claim.

To make it stronger, the student could add evidence: The darker rock measured 36°C, while the lighter rock measured 29°C.

Then the student could add reasoning: Darker colors often absorb more heat from sunlight, so the darker rock became hotter.

Now the answer has all three parts of CER.

14. A Simple CER Frame You Can Use

You can follow this pattern:

Claim: I claim that ________.

Evidence: One piece of evidence is ________.

Reasoning: This evidence supports my claim because ________.

15. Final Summary

Claim, Evidence, and Reasoning helps scientists explain answers clearly.

A claim tells the answer. Evidence gives observations or data. Reasoning explains why the evidence supports the claim.

When you use all three parts together, your science answer becomes strong, clear, and convincing.

Peer Review and Scientific Discourse

Scientists do not work alone. They share what they learn with other people. Then they listen, ask questions, and think again. This helps everyone learn more.

In first grade, we can do this too. When we tell classmates about what we noticed in an investigation, and they ask us kind questions, we are doing a simple kind of peer review and scientific talk.

Peer review means other people look at your work and help you check it. Scientific discourse means talking about science in a careful, kind way. We use what we saw, heard, or measured to explain our ideas.

This is important because sometimes we miss something. Sometimes a friend notices a new detail. Sometimes new evidence helps us change our minds. In science, it is okay to change your idea when you learn more.

What do we do when we share our science work?

• Tell what you did. Say what you tested.
• Tell what you saw. Share your observations.
• Show your evidence. Evidence is the facts you found.
• Listen to others. They may have good questions.
• Answer kindly. Use a calm, respectful voice.
• Think again if needed. New evidence can help you improve your idea.

What does kind science talk sound like?

• “I noticed...”
• “I think... because...”
• “Can you tell me more?”
• “What did you see?”
• “How did you test it?”
• “I agree because...”
• “I have a different idea because...”

These sentence starters help us talk about ideas, not hurt feelings. In science, we are checking the work, not being mean to the person.

What kinds of questions can we ask?

• What was your question?
• What did you do first, next, and last?
• What happened?
• How do you know?
• Did you try it more than one time?
• What could you do differently next time?

These questions help us understand if the test was fair and if the evidence matches the idea.

What is evidence?

Evidence is what helps show your idea might be true. Evidence can be what you saw, counted, drew, or wrote down.

For example, if you say, “Plants need water,” you should tell the evidence too. You might say, “The plant with water stayed green. The plant with no water drooped.” That is evidence.

What if someone asks a question about your work?

That is okay. Questions help science grow. You can listen, think, and answer the best you can.

You can say:

• “I saw...”
• “I counted...”
• “I am not sure yet.”
• “I want to test it again.”

Scientists do not have to know everything right away. Good scientists are careful and honest.

What if new evidence shows something different?

Then we can change our idea. This is a smart science choice. It means we are learning.

For example, maybe you thought the bigger rock would sink faster. But when you tested it, both rocks sank quickly. Then you can say, “I changed my idea because my test showed something different.”

How to be a good science listener

1. Look at the speaker.
2. Listen all the way through.
3. Think about what they said.
4. Ask a kind question.
5. Use evidence when you respond.

How to be a good science speaker

1. Say your question.
2. Tell what you did.
3. Tell what you observed.
4. Share your evidence.
5. Listen to questions.
6. Revise your idea if new evidence helps you.

Worked Example 1: Talking about a plant test

Mia says, “I wanted to know if plants need water. I gave one plant water. I did not give water to the other plant. After a few days, the watered plant was green. The other plant drooped.”

A classmate asks, “How do you know water made the difference?”

Mia can answer, “Both plants were the same kind. I kept them in the same place. The big difference was water.”

Why this is good science talk:

• Mia told what she did.
• The classmate asked a kind question.
• Mia used evidence to explain her idea.

Worked Example 2: Comparing shadows

Leo says, “I think the shadow is longer in the morning.” He shows two drawings: one morning shadow and one afternoon shadow.

A classmate says, “Can you test it again tomorrow?”

Leo thinks and says, “Yes. One day may not be enough. I will check again.”

Why this is good science talk:

• The classmate did not say, “You are wrong.”
• The classmate asked for more evidence.
• Leo was willing to test again.

Worked Example 3: Changing an idea after new evidence

Sara says, “I thought all heavy things sink.” She tested a rock and a big piece of wood. The rock sank. The wood floated.

A classmate asks, “Did both heavy things sink?”

Sara answers, “No. The wood did not sink. I need to change my idea. Not all heavy things sink.”

Why this is good science talk:

• Sara used what really happened.
• She did not ignore the new evidence.
• She revised her conclusion.

Worked Example 4: Asking about how a test was done

Ben says, “I found out that ice melts fastest in the sun.”

A classmate asks, “Did you use the same size ice cubes?”

Ben says, “Oops, one cube was bigger. I should try again with the same size cubes.”

Why this is good science talk:

• The classmate asked about the test steps.
• Ben noticed the test was not fully fair.
• Ben knows how to improve the investigation.

Remember these big ideas

• Science is about sharing ideas.
• We ask kind questions.
• We use evidence to explain.
• We listen to others.
• We can change our minds when we learn more.

When we talk with classmates about science, we help each other become better thinkers. Peer review and scientific discourse mean we share, ask, listen, and learn together.