DA0-002 Data Governance Practice Question
During a presentation, a stakeholder questions the validity of a correlation found. What is the best response?
⚠ Common exam trap
It's easy for candidates to confuse statistical significance (p-value) or sample size with validity of a correlation, overlooking the core principle that correlation does not imply causation, which is a classic pitfall in data interpretation questions.
Answer choices
Why each option matters
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, but we can perform further analysis.
It directly addresses the stakeholder's concern about validity by acknowledging the fundamental statistical principle that correlation does not imply causation. It then proposes a constructive next step—further analysis—which aligns with best practices in data communication, where validating insights requires additional testing (e.g., controlled experiments or causal inference methods). This response demonstrates both technical honesty and a commitment to rigorous data-driven decision-making.
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 does not imply causation, but we can perform further analysis.
Why this is correct
Acknowledging that correlation does not imply causation addresses the validity challenge honestly, while offering further analysis keeps the investigation open rather than defensive. This satisfies the stakeholder's concern about the correlation's meaning, distinguishing statistical association from causal mechanism without dismissing the finding outright.
- ✗
We can accept the correlation as true.
Why it's wrong here
Accepting a correlation as true bypasses the checks the stakeholder is requesting, such as confounding, outliers and spurious association. It is tempting when the analysis already passed significance testing, yet statistical significance alone never confirms that the relationship is real or meaningful in the population studied.
- ✗
We used a large sample so it's valid.
Why it's wrong here
Sample size does not establish validity; confounding, selection bias and measurement error can distort a correlation regardless of n. It is tempting because larger samples do tighten confidence intervals, but that addresses precision, not whether the observed association reflects a genuine causal or statistical relationship.
- ✗
The p-value is low, so it's significant.
Why it's wrong here
A low p-value only indicates the result is unlikely under the null hypothesis; it does not rule out confounding, bias or practical insignificance. It is tempting because significance testing is standard practice, but the stakeholder is questioning validity, which requires examining study design and alternative explanations instead.
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