The correct answer is that a statistically significant correlation may still be due to chance or confounding variables. This is because correlation quantifies the strength and direction of a linear relationship between two variables, but it does not establish causation—a cause-effect link requires controlled experiments with randomization to rule out hidden factors. On the CompTIA Data+ DA0-001 exam, this concept tests your ability to distinguish between association and causality, often appearing in scenario-based questions where a strong correlation is presented as proof of cause. A common trap is assuming that a high correlation coefficient (e.g., 0.95) automatically means one variable drives the other, when in reality a lurking variable could be responsible. Remember the mantra: “Correlation does not imply causation”—think of ice cream sales and shark attacks, which both rise in summer due to a third factor (warm weather), not because ice cream causes sharks.
DA0-001 Analyzing and Modeling Data Practice Question
This DA0-001 practice question tests your understanding of analyzing and modeling data. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Which TWO of the following are true about correlation and causation? (Select TWO).
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Correlation does not imply causation
Option C is correct because correlation measures the strength and direction of a linear relationship between two variables, but it does not imply that one variable causes the other. Causation requires controlled experiments with randomization to rule out confounding variables and establish a cause-effect relationship.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Correlation measures both linear and nonlinear relationships
Why it's wrong here
Pearson correlation measures linear relationship only.
✗
Causation can always be inferred from a controlled experiment without randomization
Why it's wrong here
Randomization is needed to infer causation.
✓
Correlation does not imply causation
Why this is correct
This is a fundamental concept.
Related concept
Read the scenario before looking for a memorised answer.
✗
If two variables are highly correlated, one must cause the other
Why it's wrong here
High correlation does not prove causation.
✓
A statistically significant correlation may still be due to chance or confounding variables
Why this is correct
Significance does not guarantee causality.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
CompTIA often tests the classic 'correlation does not imply causation' fallacy, where candidates mistakenly think that a statistically significant correlation automatically proves a causal relationship, ignoring the role of chance and confounding variables.
Detailed technical explanation
How to think about this question
In statistics, correlation coefficients (e.g., Pearson's r) range from -1 to 1 and quantify linear dependence, but they are sensitive to outliers and assume homoscedasticity. A real-world scenario: a study might find a strong correlation between years of education and income, but this does not prove education causes higher income—confounders like socioeconomic background or innate ability could drive both. Establishing causation typically requires randomized controlled trials (RCTs) or advanced causal inference methods like instrumental variables or difference-in-differences.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the DA0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Analyzing and Modeling Data — This question tests Analyzing and Modeling Data — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Correlation does not imply causation — Option C is correct because correlation measures the strength and direction of a linear relationship between two variables, but it does not imply that one variable causes the other. Causation requires controlled experiments with randomization to rule out confounding variables and establish a cause-effect relationship.
What should I do if I get this DA0-001 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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