DA0-002 Data Governance Practice Question
An analyst is presenting findings to stakeholders. Which TWO techniques effectively communicate uncertainty in data? (Choose TWO.)
⚠ Common exam trap
CompTIA often tests the distinction between measures of central tendency (point estimates) and measures of variability (confidence intervals, error bars), trapping candidates who think stating exact numbers or removing outliers is a valid way to handle uncertainty.
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
✓
Include confidence intervals.
Confidence intervals (option A) are the standard statistical technique for expressing uncertainty around an estimate, giving stakeholders a range of plausible values at a stated confidence level (e.g., 95%), which directly communicates the precision of the finding. Error bars on charts (option C) visually convey uncertainty—such as standard deviation, standard error, or confidence intervals—around plotted data points, making variability and reliability immediately apparent to a non-technical audience. Together these techniques quantify and visualize uncertainty rather than hiding it. In contrast, using only point estimates (B) and stating exact numbers without ranges (E) falsely imply perfect precision and conceal variability, while removing all outliers (D) is a data-cleaning decision that can bias results and does not communicate uncertainty at all.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Include confidence intervals.
Why this is correct
Confidence intervals quantify the range within which a population parameter likely falls, directly satisfying the need to communicate statistical uncertainty to stakeholders. Unlike point estimates, they expose sampling variability and precision, letting decision-makers judge reliability rather than assuming false certainty from a single figure.
- ✗
Use only point estimates.
Why it's wrong here
Point estimates alone conceal variance, so stakeholders cannot gauge confidence intervals or sampling error. Reporting a mean without spread misrepresents precision. Point estimates belong in summaries once uncertainty is quantified separately, such as alongside confidence intervals or error bars, not as the sole communication technique.
- ✓
Use error bars on charts.
Why this is correct
Error bars display the range of variability around a plotted estimate, directly satisfying the requirement to communicate uncertainty visually. They quantify confidence intervals or standard deviation, letting stakeholders judge the reliability of each data point rather than reading a single value as exact. This makes statistical dispersion explicit within the chart itself.
- ✗
Remove all outliers from the data.
Why it's wrong here
Deleting outliers conceals genuine variance and biases the results, which misrepresents rather than communicates uncertainty. It tempts as a data-cleaning step for noisy datasets, but cleaning is not a presentation technique and removes the very spread you should disclose.
- ✗
State exact numbers without ranges.
Why it's wrong here
Exact figures imply false precision; uncertainty requires ranges, confidence intervals or probability statements. Stating a single number without bounds hides sampling variability and model error from stakeholders. Exact values suit deterministic reporting, such as audited financial totals, where no statistical uncertainty exists to convey.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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