DA0-002 Data Acquisition and Preparation Practice Question
A data analyst is investigating a correlation between two continuous variables. Which THREE of the following are appropriate steps in this exploratory data analysis? (Select THREE.)
⚠ Common exam trap
DA0-002 often tests the distinction between correlation analysis (continuous variables, Pearson/scatter/outliers) and group comparison or categorical analysis (t-test, contingency table) — candidates who pick the t-test confuse 'comparing' with 'correlating'.
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
✓
Calculate the Pearson correlation coefficient
Option A is correct because the Pearson correlation coefficient (r) is the standard statistic for quantifying the strength and direction of a linear relationship between two continuous variables, which is exactly the analyst's goal. Option B is correct because a scatter plot visually reveals the form, direction, and strength of the relationship between the two continuous variables and can expose non-linearity that a single correlation value would hide. Option D is correct because box plots (or their underlying IQR-based rules) identify outliers that can disproportionately distort the Pearson correlation coefficient, so checking for them is a necessary data-quality step before trusting r. Option C is not appropriate here because a t-test compares means between groups (or against a hypothesized mean), not the association between two continuous variables. Option E is not appropriate because a contingency table summarizes counts of categorical variables, whereas both variables in this scenario are continuous.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Calculate the Pearson correlation coefficient
Why this is correct
Calculating the Pearson correlation coefficient quantifies the strength and direction of a linear relationship between two continuous variables, directly satisfying the stem's correlation investigation. It assumes interval or ratio data, linearity, and approximately normal distributions, making it the standard parametric measure for this exploratory step.
- ✓
Create a scatter plot
Why this is correct
A scatter plot plots one continuous variable against the other, revealing the shape, direction and possible non-linearity of the relationship. This visual inspection is a core exploratory step for assessing correlation between two continuous variables.
- ✗
Perform a t-test
Why it's wrong here
A t-test compares means between groups on a continuous outcome, so it tests group differences rather than the strength or direction of association between two continuous variables. It is tempting because t-tests are common in exploratory analysis, and would be correct when comparing mean values across two categories, such as treatment versus control.
- ✓
Check for outliers using box plots
Why this is correct
Box plots expose points beyond the 1.5×IQR fences, revealing extreme values that can distort a Pearson correlation coefficient. Since the stem concerns two continuous variables, identifying these outliers is a legitimate exploratory step, allowing the analyst to assess whether the relationship is driven by anomalous observations before interpreting the correlation.
- ✗
Create a contingency table
Why it's wrong here
A contingency table tabulates frequencies of categorical variables, whereas correlation requires continuous paired measurements, so it cannot quantify a relationship between them. It is tempting because contingency tables are core exploratory tools, and would be correct when examining association between two categorical variables using a chi-square test.
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Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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