MLS-C01 Exploratory Data Analysis Practice Question
A machine learning engineer is analyzing a dataset with a mix of categorical and numerical features. The engineer wants to understand the correlation between categorical features and the target variable. Which statistical test is most appropriate for measuring association between a categorical feature and a binary target?
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
✓
Chi-squared test of independence
The Chi-squared test of independence is used to determine if there is a significant association between two categorical variables, which is applicable here. Option A is wrong because Pearson correlation is for continuous variables. Option B is wrong because ANOVA is for comparing means across groups, but assumes continuous target. Option D is wrong because Mutual Information can be used but is not a statistical test with a p-value.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Pearson correlation coefficient
Why it's wrong here
Pearson correlation is for continuous variables, not categorical.
- ✗
ANOVA (Analysis of Variance)
Why it's wrong here
ANOVA compares means of a continuous variable across categories, not for two categorical variables.
- ✓
Chi-squared test of independence
Why this is correct
Chi-squared test tests association between two categorical variables.
- ✗
Mutual information
Why it's wrong here
Mutual information measures dependence but does not provide a hypothesis test.
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