MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is analyzing a dataset with many categorical features. The target variable is binary. Which statistical test should be used to assess the association between each categorical feature and the 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 the appropriate test to assess association between two categorical variables. Here, both the features (categorical) and the target (binary, which is categorical) are categorical, making the Chi-squared test the correct choice. Option A (Pearson correlation) is for continuous variables, not categorical. Option C (ANOVA) compares means across groups for a continuous target, but our target is binary (categorical). Option D (Kolmogorov-Smirnov test) compares distributions of continuous variables, not categorical. Therefore, B is correct.
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 is for continuous variables.
- ✓
Chi-squared test of independence
Why this is correct
Chi-squared tests association between categorical variables.
- ✗
ANOVA
Why it's wrong here
ANOVA compares means across groups for continuous target.
- ✗
Kolmogorov-Smirnov test
Why it's wrong here
KS test compares distributions of continuous variables.
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