A correlation is an association between variables; it does not by itself show that one causes the other. A third factor, reverse causation or chance may explain the pattern. Controlled investigations, plausible mechanisms and converging evidence strengthen causal claims.
Example
Ice-cream sales and sunburn cases may rise together because hotter sunny weather influences both, not because buying ice-cream causes sunburn.
Key terms
Correlation:
A pattern in which two variables change together.
Causation:
A relationship in which a change in one factor produces a change in another.
Confounding variable:
An uncontrolled factor that may influence both variables being studied.
Questions
1. Which statement best captures correlation, causation and confounding variables?
Causal conclusions require more evidence than a graph showing two variables changing together.
Whenever two variables are correlated, one must directly cause the other.
The pattern can only be explained by guessing.
The topic has no observable evidence.
2. Which term means "A pattern in which two variables change together."?
Correlation
Causation
Confounding variable
Variable
3. Which term means "A relationship in which a change in one factor produces a change in another."?
Causation
Correlation
Confounding variable
Conclusion
4. Which term means "An uncontrolled factor that may influence both variables being studied."?
Confounding variable
Correlation
Causation
Prediction
5. Which observation task is most relevant to this topic?
Inspect several paired-variable graphs and describe the pattern without using causal language.
Copy the topic title without looking at an example.
Choose a result before making observations.
Ignore details that do not match a first guess.
6. Which model would best represent the key process or relationship?
Draw alternative causal diagrams for a correlation, including a possible third variable.
A decorative drawing with no labels or connection to evidence.
A list of unrelated facts.
A model that deliberately contradicts every observation.
7. Which investigation is focused most directly on the scientific idea?
Redesign an observational comparison to control a plausible confounding variable where ethical and practical.
Change many uncontrolled factors and record nothing.
Ask only for opinions and treat them as measurements.
Repeat a memorised answer without testing it.
8. Which evidence best supports the lesson explanation?
A repeatable controlled effect plus a plausible mechanism supports causation more strongly than correlation alone.
Whenever two variables are correlated, one must directly cause the other.
One preferred answer with no observation.
A claim that cannot be checked in any way.
9. Which task applies the science in a new context?
Evaluate a media headline that turns an association into a cause-and-effect claim.
Write the heading again without explaining it.
Ignore the system and choose randomly.
Assume the same answer fits every situation.
10. Which response best corrects the misconception in this topic?
Causal conclusions require more evidence than a graph showing two variables changing together.
Whenever two variables are correlated, one must directly cause the other.
Both statements must be equally correct.
Evidence cannot help decide between explanations.
11. What makes a scientific observation useful?
It records relevant details without changing them to fit an expectation.
It includes only details that support a preferred answer.
It replaces measurements with guesses.
It hides the conditions under which it was made.
12. Why should a scientific model include its limitations?
Models simplify reality, so users need to know what the representation leaves out.
A limitation proves the model has no value.
Models are exact copies and never omit anything.
Limitations should be hidden so a model looks certain.
13. What makes a comparative investigation fair?
Change or compare the intended factor while keeping other relevant conditions consistent.
Change every condition at the same time.
Measure only the result that looks best.
Decide the conclusion before collecting data.
14. Why repeat measurements or use several samples?
To reveal variation and reduce the influence of chance or one unusual result.
To guarantee a preferred conclusion.
To make units unnecessary.
To remove the need for a clear method.
15. What is the best response to an anomalous result?
Record it, check the method and investigate whether it is error or meaningful variation.
Delete it automatically.
Delete all other results instead.
Assume it proves the whole topic wrong.
16. Which conclusion is scientifically responsible?
One that answers the question, uses the evidence and states important limits.
One that claims more than the data show.
One that ignores conflicting evidence.
One based only on the expected answer.
17. What would make the claim about correlation, causation and confounding variables stronger?
Several relevant, repeatable evidence lines that agree with the explanation.
A larger heading and no new evidence.
Removing results that are inconvenient.
Relying on a single uncheckable opinion.
18. What should happen if reliable new evidence conflicts with a model?
The model should be reviewed and revised or replaced if needed.
The evidence should always be hidden.
The original model must never change.
Scientists should stop asking questions.
19. How should safety and ethics shape an investigation?
Risks, people, living things and environments should be considered before the method is used.
Safety matters only after data collection.
Any method is acceptable if it is fast.
Ethics has no place in science.
20. What makes science communication trustworthy?
Clear methods, accurate terms, relevant evidence and acknowledgement of uncertainty.
Certainty without evidence.
Leaving out how results were obtained.
Using dramatic language instead of data.
21. What is the strongest overall outcome from studying correlation, causation and confounding variables?
Use observations, models, investigations and evidence to explain and apply this idea.
Memorise the title without using it.
Avoid testing explanations.
Treat every first idea as permanently correct.
Answer key (parent copy)
1. Causal conclusions require more evidence than a graph showing two variables changing together.
2. Correlation
3. Causation
4. Confounding variable
5. Inspect several paired-variable graphs and describe the pattern without using causal language.
6. Draw alternative causal diagrams for a correlation, including a possible third variable.
7. Redesign an observational comparison to control a plausible confounding variable where ethical and practical.
8. A repeatable controlled effect plus a plausible mechanism supports causation more strongly than correlation alone.
9. Evaluate a media headline that turns an association into a cause-and-effect claim.
10. Causal conclusions require more evidence than a graph showing two variables changing together.
11. It records relevant details without changing them to fit an expectation.
12. Models simplify reality, so users need to know what the representation leaves out.
13. Change or compare the intended factor while keeping other relevant conditions consistent.
14. To reveal variation and reduce the influence of chance or one unusual result.
15. Record it, check the method and investigate whether it is error or meaningful variation.
16. One that answers the question, uses the evidence and states important limits.
17. Several relevant, repeatable evidence lines that agree with the explanation.
18. The model should be reviewed and revised or replaced if needed.
19. Risks, people, living things and environments should be considered before the method is used.
20. Clear methods, accurate terms, relevant evidence and acknowledgement of uncertainty.
21. Use observations, models, investigations and evidence to explain and apply this idea.