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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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