Lesson
How to Conduct Experiments
Drop a Mentos into soda and a foamy geyser erupts. Anyone can watch it happen - but a scientist can find out why. This lesson teaches you the scientist's toolkit.
Driving Question
How do scientists turn a curious question into an answer they can trust?
🔬 Learning Science Focus
🔍 Phenomenon First
🏷️ Label After Learning
🪜 Stepwise Scaffolds
✏️ Generation Effect
✅ Retrieval Practice
Learning Goals
What You'll Be Able to Do
By the end of this lesson, you will be able to:
Write a testable hypothesis that explains the cause-and-effect relationship between variables, using the if - then - because frame.
SEP 1
Identify the independent variable, dependent variable, and control variables in any experiment.
SEP 3
Classify observations as quantitative (numbers and measurements) or qualitative (senses and descriptions).
SEP 4
Explain the difference between an observation and an inference, and draw reasonable inferences from evidence.
SEP 4
📚 Instructional Design
Why this section exists
- Name the four science-practice targets before students do any of the work.
- Frame the lesson as skills to perform, not facts to memorize.
Cognitive science
- Goal setting orients attention toward what to look for
- Advance organizer for the hypothesis, variables, and observation work ahead
Bloom's / DOK
- Understand to Apply
- DOK 1 to 2 (goals name and classify; no goal demands extended reasoning here)
Accessibility considerations
- Four short goal cards, one idea each
- Practice standards tagged for transparency
- Plain "be able to do" phrasing
Key Vocabulary
Words You'll Meet
Choose a card to see what each word means.
📚 Instructional Design
Why this section exists
- Pre-teach the eight terms students meet again in context below.
- Give a definition to return to when a word reappears.
Cognitive science
- Pre-teaching vocabulary lowers load during the explore tasks
- Collapsed cards reduce extraneous load until a word is needed
Bloom's / DOK
- Remember to Understand
- DOK 1
Accessibility considerations
- One card open at a time
- Click to reveal, no hover
- Short, plain definitions
Engage · Mystery
Three Everyday Mysteries
Science doesn't start in a lab. It starts the moment you notice something and ask, "Wait... why does that happen?" Click each card and see what questions pop into your head.
The Eruption
You drop a Mentos candy into a bottle of soda. In one second, foam blasts out of the bottle like a geyser - but only with some sodas, and not others.
What would make the eruption bigger or smaller? How could you find out?
Click to look closer
The Thirsty Plant
You have three identical plants. You water one a little, one a medium amount, and one a lot. After two weeks, they look completely different.
Which amount of water will cause the plant to grow the healthiest? How would you prove it?
Click to look closer
The Wet Grass
You step outside in the morning and the grass is soaked - but you never heard rain overnight, and the sidewalk is dry.
What happened while you were asleep? What's your best explanation?
Click to look closer
Notice something? Each mystery began the same way: you observed something, and a question appeared. Scientific inquiry always starts with a question, driven by curiosity or a desire to solve a problem.
But a question alone isn't an answer. To get an answer you can trust, you need a plan - and that plan is called an experiment.
The mission: By the end of this lesson, you'll be able to take any "why does that happen?" question and design a fair experiment to answer it. Step one: make an educated guess.
📚 Instructional Design
Why this section exists
- Open with real phenomena so a question, not a definition, starts the inquiry.
- Show that every investigation begins with noticing something puzzling.
Cognitive science
- Curiosity gap primes attention before any vocabulary appears
- Phenomenon-based learning anchors the abstract method in concrete events
Bloom's / DOK
- Understand
- DOK 2 (students generate questions from an observation)
Accessibility considerations
- Click to reveal each mystery, no hover
- Icon paired with short scenario text
- No prior knowledge required to engage
Explore · The Educated Guess
What Is a Hypothesis?
Before scientists test anything, they predict what will happen and why. That prediction has a name.
The key idea
A hypothesis is an educated guess that explains an observation based on your prior knowledge. A good hypothesis is testable and explains the cause-and-effect relationship between variables.
Testable: "Plants given more sunlight will grow taller" - you can actually run this experiment and measure the result.
Not testable: "Plants are happier in the sun" - how would you ever measure a plant's happiness?
Back to the eruption: Mentos dropped into which soda will cause the biggest foaming eruption? Make a prediction to unlock the Hypothesis Builder.
🧪 The Hypothesis Builder
A strong hypothesis has three parts. Use the drop-downs to build one for the Mentos experiment, then check it.
1If - the change you will make (the cause).
2Then - the result you predict (the effect).
3Because - your reasoning, based on what you already know.
If Mentos are dropped into
, then it will cause
, because
.
A hypothesis points at a cause and an effect. The thing you change... the thing that responds... scientists have exact names for those. Time to meet the variables.
📚 Instructional Design
Why this section exists
- Define a hypothesis as a testable, reasoned prediction, not a random guess.
- Make students build the if - then - because frame, not just read it.
Cognitive science
- Worked examples and a three-part frame scaffold the structure
- Cause-and-effect modeling links the "if" to the "then"
- Misconception checking: testable versus untestable cards counter "a hypothesis is just a guess"
Bloom's / DOK
- Understand to Apply
- DOK 2 (students assemble and check a hypothesis, not justify a full design)
Accessibility considerations
- Drop-down builder, no free typing required
- Predict gate before the builder, with immediate feedback
- Key term defined in place before use
Explore · The Variables
What Are the Variables?
Every fair experiment has three kinds of factors. Follow the color coding through the rest of the lesson: teal is what you change, orange is what responds, and green is what stays the same.
You change it
Independent Variable
The factor in the experiment that you manipulate or change. In the plant experiment: the amount of water.
The Cause
➜
It responds
Dependent Variable
The factor in the experiment that responds to the change. In the plant experiment: how healthy each plant grows.
The Effect
➕
You keep it the same
Control Variables
The other factors kept constant to ensure any change measured was due to the independent variable. Same pot, same soil, same sunlight.
The Fair Test
Think about it: In the plant experiment, why bother keeping the sunlight the same for all three plants? Make a prediction to unlock the sorting challenge.
Here's why it matters
If one plant got more sunlight and more water, you could never tell which one caused it to grow taller. Keeping everything else constant means the independent variable is the only possible cause of the change you measure. That's what makes a test fair.
🌱 Variable Sorter: The Plant Experiment
A student waters three identical plants with different amounts of water and measures their height after two weeks. Sort each factor into its role.
The amount of water each plant gets
The height of each plant after two weeks
The amount of sunlight each plant receives
The type of soil and size of pot
🏆 All sorted! You just identified every variable like a scientist planning a fair test.
Question → hypothesis → fair test. Now the experiment is running. But what exactly do you write down while it happens? Time to talk about observations.
📚 Instructional Design
Why this section exists
- Separate the three variable roles and tie each to a color used lesson-wide.
- Make students justify why holding controls constant is what makes a test fair.
Cognitive science
- Comparison and contrast distinguishes independent, dependent, and control roles
- Error analysis: the predict gate exposes why an uncontrolled factor ruins the test
- Misconception checking counters "changing more than one variable is okay"
Bloom's / DOK
- Understand to Analyze
- DOK 2 to 3 (sorting is 2; justifying why a control keeps the test fair reaches 3)
Accessibility considerations
- Consistent color coding for the three roles
- Sorter gives item-level feedback on each choice
- Short, parallel role cards
Explore · Collecting Evidence
Two Types of Observations
Scientists collect evidence in two flavors: with instruments and numbers, or with their senses and descriptions. Both are useful - but they're not the same.
Quantitative observations involve precise measurements and numerical quantities. Objective measures: length, height, temperature, time. "The eruption reached 2.3 meters."
Qualitative observations involve using sensory information to describe something's qualities. Subjective measures: colors, textures, smells, tastes. "The foam was white and smelled sweet."
Memory trick
Quantitative sounds like quantity - a number you can count or measure. Qualitative sounds like quality - a description from your senses.
🔬 Observation Sorter
A scientist recorded these notes during the plant experiment. Sort each one: quantitative (numbers) or qualitative (senses)?
"The greenhouse temperature was 24°C."
"The leaves on plant B feel waxy and look dark green."
"Plant C grew 4 centimeters in 14 days."
"The soil in pot A smells damp and earthy."
🏆 All sorted! Numbers and measurements are quantitative; sensory descriptions are qualitative.
Observations are what you actually see, measure, hear, or smell. But scientists do one more thing with their evidence: they figure out what it means. That step has its own name too.
📚 Instructional Design
Why this section exists
- Distinguish quantitative from qualitative evidence before students collect data.
- Practice classifying real lab-notebook entries, not just reading the definitions.
Cognitive science
- Comparison and contrast pairs numbers against sensory descriptions
- The quantity / quality mnemonic supports retrieval
- Generation effect: students classify entries themselves
Bloom's / DOK
- Understand to Analyze
- DOK 2 (classifying each observation by type)
Accessibility considerations
- Two-way sort keeps choices simple
- Icon and short quote for every item
- Immediate feedback per entry
Explore · Reading the Clues
What Is an Inference?
Detectives don't see the crime - they see the clues and work out what happened. Scientists do exactly the same thing with observations.
The key idea
An inference is a conclusion that you draw about something based on your observations. You didn't see it happen - your brain filled in the most likely explanation.
Try it: You observe someone wearing a raincoat and carrying an umbrella. What can you infer?
The reasonable inference
You can infer that they think it is going to rain. You never observed rain - you observed a raincoat and umbrella, and your brain connected the clues to the most likely explanation. That's an inference: a conclusion built from observations.
One more - remember the mystery from the start? You step outside in the morning and observe that the grass is wet. What can you infer?
The reasonable inference
You can infer that it rained overnight or dew formed on the grass. Notice that the dry sidewalk is also an observation - it's a clue that points toward dew rather than rain. Better observations lead to better inferences.
Observation: information you collect directly with senses or instruments. "The grass is wet."
Inference: the conclusion your brain draws from those observations. "It probably rained or dew formed."
📚 Instructional Design
Why this section exists
- Draw the line between what you observe and the conclusion you build from it.
- Show that stronger observations lead to stronger inferences.
Cognitive science
- Evidence-based reasoning connects clues to a likely explanation
- Detective analogy maps inference onto a familiar schema
- Callback to the opening wet-grass mystery supports elaboration
Bloom's / DOK
- Understand to Analyze
- DOK 2 (drawing and distinguishing inferences from given observations)
Accessibility considerations
- Predict gate before each reveal
- Observation and inference shown side by side
- Concrete everyday examples first
Quick Recall
Brain Check
Two quick questions before we put it all together. These are not graded. Pulling answers from memory now will help them stick.
Quick Recall · 1 of 2
A quick brain check. Not graded.
In the Mentos experiment, you drop the same candy into three different sodas and measure how high each eruption goes. What is the independent variable?
Quick Recall · 2 of 2
One more brain check. Not graded.
A student writes in her lab notebook: "The Mentos eruption was probably bigger in diet soda because diet soda holds more dissolved gas." Is that an observation or an inference?
📚 Instructional Design
Why this section exists
- Force memory retrieval before the synthesis and quiz, while stakes are zero.
- Surface a wrong variable or observation/inference choice early enough to fix.
Cognitive science
- Retrieval practice strengthens memory more than rereading
- Items apply ideas to a fresh Mentos scenario, not the exact examples taught
- Try-again loop turns an error into productive struggle
Bloom's / DOK
- Remember to Apply
- DOK 1 to 2 (kept at or below the formal quiz)
Accessibility considerations
- Ungraded and low stakes, stated up front
- Retry button with feedback
- One short question at a time
Explain · Wrap-Up
Back to the Eruption
You started with a foamy mystery. Now you have the full scientist's toolkit to solve it - or any mystery.
Start With a Question
Scientific inquiry begins with curiosity.
"Which soda makes the biggest eruption?" Then turn the question into a hypothesis: a testable, educated guess that names a cause and an effect.
Design a Fair Test
Change one thing, measure one thing, lock everything else.
Every fair experiment uses the same three roles:
Independent · change it
Dependent · measure it
Controls · keep them same
Collect and Conclude
Observe carefully, then infer.
Record quantitative observations (numbers) and qualitative observations (senses), then draw an inference: the conclusion your evidence supports.
Everything in One Place
The words to know and the goals you worked toward, all in one spot.
| Term | Student-Friendly Definition |
|---|---|
| Hypothesis | An educated guess that explains an observation based on prior knowledge. A good one is testable and explains the cause-and-effect relationship between variables. |
| Independent variable | The factor in the experiment that you manipulate or change. |
| Dependent variable | The factor in the experiment that responds to the change - the thing you measure. |
| Control variables | The other factors kept constant to ensure any change measured was due to the independent variable. |
| Quantitative observation | An observation using precise measurements and numerical quantities: length, height, temperature, time. |
| Qualitative observation | An observation using sensory information to describe qualities: colors, textures, smells, tastes. |
| Inference | A conclusion you draw about something based on your observations. |
| Learning Goals | How You Showed It |
|---|---|
| Write a testable hypothesis using the if - then - because frame (SEP 1). | You built a complete hypothesis for the Mentos experiment and checked whether it was testable and explained a cause-and-effect relationship. |
| Identify independent, dependent, and control variables (SEP 3). | You sorted every factor in the plant experiment into its correct role and explained why control variables make a test fair. |
| Classify observations and draw inferences (SEP 4). | You sorted lab-notebook entries into quantitative and qualitative, and drew reasonable inferences from the raincoat and wet-grass clues. |
Essential question: How do scientists turn a curious question into an answer they can trust? If you can answer that using the words hypothesis, independent variable, dependent variable, control variables, observation, and inference, you own this lesson.
📚 Instructional Design
Why this section exists
- Tie question, hypothesis, fair test, observation, and inference into one chain.
- Map every learning goal to the evidence the student produced.
Cognitive science
- Schema building connects the separate pieces into one method
- Coherent narrative returns to the opening eruption to close the loop
- Summary tables consolidate terms and goals in one place
Bloom's / DOK
- Understand to Analyze
- DOK 2 to 3 (relating the full inquiry sequence as a connected process)
Accessibility considerations
- Three labeled beats break the method into steps
- Scrollable tables for term and goal review
- Essential question restated for self-check
Evaluate · Quiz
Check Your Understanding
Ten questions covering everything you discovered, including a brand-new experiment for you to analyze. Answer every question, then submit.
Your score will not be sent
Your score will be sent to your teacher
0 / 10 selected
🧠 Show Your Thinking
Scientists don't just know the answer. They explain their thinking.
Write your own explanation first. Then submit your work to compare your thinking with a model answer.
You tested the Mentos mystery: the same candy, three different sodas, and you measured how high each eruption reached. Make a claim about which soda erupts biggest, back it with evidence (the observations from your fair test), and explain your reasoning - how those observations let you infer an answer you can trust. Use the words observation and inference.
One strong way to say it
Claim: diet soda causes the biggest eruption. Evidence: in a fair test you change only the type of soda and keep the candy, the amount, and the bottle the same, then record quantitative observations - the diet-soda foam reached the highest measured height. Reasoning: because every other factor was controlled, the difference in eruption height had to be caused by the soda, so you can infer the soda is the cause, not luck. That is how an observation becomes an answer you can trust: a fair test lets you infer a cause from an effect. If your answer makes a claim, backs it with observations from a controlled test, and reasons from evidence to an inference, you have it.
📚 Instructional Design
Why this section exists
- Close the lesson with the student constructing an evidence-based answer, not selecting one.
- Return to the Mentos anchor so hypothesis, fair test, observation, and inference combine into one conclusion.
Cognitive science
- Generation and self-explanation surface gaps that multiple choice hides
- Mini CER matches an evidence-and-inference lesson: claim, evidence, reasoning
- Model answer revealed only after submitting supports honest self-check
Bloom's / DOK
- Analyze to Evaluate
- DOK 3 (construct and defend a conclusion from evidence)
Accessibility considerations
- Sentence-starter scaffold names claim, evidence, and reasoning
- Required before submit, with a model answer to compare against
- The same closing routine students meet in every lesson
🔍 The Mystery You Came In With
You started this lesson with one question: "How do scientists turn a curious question into an answer they can trust?" If you can name the variables, write a hypothesis, and tell observations from inferences, you've earned the toolkit.
📚 Instructional Design
Why this section exists
- Measure all four goals, including a brand-new experiment students must analyze.
- Offer practice mode for self-check and classroom mode for teacher reporting.
Cognitive science
- Retrieval practice across the whole lesson in one sitting
- Feedback loops: every item has an answer explanation
- Transfer items apply the method to unfamiliar scenarios
Bloom's / DOK
- Understand to Apply
- DOK 1 to 2 (mixed recall and applied items)
Accessibility considerations
- Plausible, evenly placed answer options
- Answer explanations provided
- Required name, teacher, and block before classroom submission
Go Further
More Learning
Experimental design is the backbone of every science unit. Extension challenges: actually run the Mentos experiment at home (outside!) - write your hypothesis first, name all three variable types, and record two quantitative and two qualitative observations. Or design an experiment to answer one of your own "why does that happen?" questions and trade designs with a partner: can they find a variable you forgot to control?
More Coming Soon
The lesson is just the beginning. More investigations, simulations, and challenges are coming soon.
Coming Soon
📚 Instructional Design
Why this section exists
- Move the method off the screen into a real or self-designed experiment.
- Use a peer swap to hunt for an uncontrolled variable in a partner's design.
Cognitive science
- Transfer applies the full method to a student's own question
- Error analysis: critiquing a peer design targets weak fair tests
- Interest-driven extension sustains motivation past the lesson
Bloom's / DOK
- Apply to Analyze
- DOK 2 to 3 (designing an investigation and critiquing a peer's controls)
Accessibility considerations
- Optional and self-paced, no penalty for skipping
- Linked next steps sorted by type and color
- Challenge restated in plain, concrete steps
Connections
Connections
Science and engineering share the same toolkit. Here is where these skills carry over.