DA0-002 Data Analysis Practice Question
A data analyst is examining a dataset of employee salaries and notices that the distribution is heavily right-skewed due to a few executives with very high salaries. The analyst wants to apply a transformation to make the distribution more symmetric for further analysis. Which transformation is most appropriate?
⚠ Common exam trap
Candidates often confuse transformations that increase skewness (square, exponential) with those that reduce it (log, square root).
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
✓
Logarithmic transformation
For right-skewed data with positive values, the logarithmic transformation is the most appropriate to reduce skewness and make the distribution more symmetric. It compresses the upper tail, mitigating the influence of extreme high values. Square root is milder, while square and exponential transformations would increase skewness. Therefore, the log transformation is the best choice.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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Square root transformation
Why it's wrong here
The square root transformation is a power transformation that can reduce right skewness, but it is milder than the logarithmic transformation. For heavily right-skewed data with extreme values, such as executive salaries, the square root may not sufficiently compress the large values. It is more suitable for moderate skewness or count data. Thus, it is less effective than the log transformation here.
- ✓
Logarithmic transformation
Why this is correct
The logarithmic transformation is highly effective for right-skewed data with positive values, as it compresses the upper tail and reduces the influence of extreme values. For salary data with a few very high earners, taking the log of salaries will make the distribution more symmetric and closer to normal. This is a common practice before applying statistical techniques that assume normality.
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Square transformation
Why it's wrong here
Squaring values exaggerates differences and increases right skewness, making the distribution even more asymmetric. It is used for left-skewed data to spread out the lower tail, not for right-skewed data. Applying a square transformation to already right-skewed salaries would worsen the skew and is therefore incorrect.
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Exponential transformation
Why it's wrong here
The exponential transformation (e.g., e^x) drastically increases the impact of large values, further skewing the distribution to the right. It is not used to correct right skewness. In fact, it would make the executive salaries even more extreme relative to the rest, which is the opposite of what the analyst wants. Hence, it is not appropriate.
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Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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