MLS-C01 Exploratory Data Analysis Practice Question
During EDA, a data scientist finds that a feature has a skewness value of 2.5. What does this indicate about the data distribution?
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
✓
The distribution is right-skewed
A skewness value of 2.5 is positive and greater than 1, indicating a highly right-skewed (positively skewed) distribution, where the tail extends to the right. Option A correctly identifies this. Option B is wrong because symmetric distributions have skewness near 0. Option C is wrong because left-skewed distributions have negative skewness. Option D is wrong because skewness measures asymmetry, not necessarily the presence of outliers.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
The distribution is right-skewed
Why this is correct
Positive skewness indicates a long right tail.
- ✗
The distribution is symmetric
Why it's wrong here
Symmetric distributions have skewness near 0.
- ✗
The distribution is left-skewed
Why it's wrong here
Left-skewed has negative skewness.
- ✗
The distribution has no outliers
Why it's wrong here
Skewness does not directly measure outliers.
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