DA0-002 Data Acquisition and Preparation Practice Question
An analyst is performing EDA and wants to measure the strength and direction of linear relationship between two continuous variables. Which statistical measure should they compute?
⚠ Common exam trap
Watch out — candidates often confuse univariate descriptive statistics (mean, standard deviation, mode) with bivariate measures of association; candidates who skim may pick standard deviation because it sounds 'statistical' without noticing the question asks about the relationship between two variables.
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
Correlation (typically Pearson's r for linear relationships) is the standardized measure of both the strength and direction of a linear relationship between two continuous variables, ranging from -1 to +1. A positive value indicates a positive linear association, a negative value a negative one, and the magnitude indicates strength. Standard deviation, mean, and mode are univariate descriptive statistics and say nothing about the relationship between two variables.
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
Why this is correct
Correlation quantifies both the strength and direction of a linear relationship between two continuous variables, returning a value from -1 to +1. This matches the analyst's EDA goal precisely, unlike covariance, which indicates direction but is scale-dependent.
- ✗
Standard deviation
Why it's wrong here
Standard deviation measures dispersion around the mean for a single variable, not the association between two. It is tempting because it describes continuous data spread, but Pearson's correlation coefficient is the measure that captures both strength and direction of a linear relationship.
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Mean
Why it's wrong here
The mean gives the arithmetic average of one variable, describing central tendency rather than any association between two variables. It is tempting because it summarises continuous data, but Pearson's correlation coefficient is required to express the strength and direction of their linear relationship.
- ✗
Mode
Why it's wrong here
Mode reports the most frequent value in a dataset, which suits categorical frequency analysis, not continuous variables. It is tempting as a quick descriptive summary, but Pearson's correlation coefficient is the measure that quantifies both strength and direction of a linear relationship.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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