MLS-C01 Bimodal distribution Practice Question
During EDA, a data scientist plots the distribution of a feature and sees a bimodal pattern. What does this likely indicate?
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 data may contain two distinct groups.
A bimodal distribution has two distinct peaks, which typically indicates that the data contains two different subpopulations or clusters. This is a common finding in exploratory data analysis (EDA) when the feature is influenced by a categorical variable with two categories. For example, in a dataset of customer purchases, transaction amounts may be bimodal if there are two types of customers (e.g., individuals and businesses). Therefore, option A is correct. Option B is incorrect because missing values usually appear as a separate bar or a spike at a specific value, not as a second peak. Option C is incorrect because outliers typically appear as extreme values far from the main distribution, not as a second mode. Option D is incorrect because standardization (scaling to zero mean and unit variance) does not change the shape of the distribution; it only changes the scale.
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 data may contain two distinct groups.
Why this is correct
Bimodal suggests mixture of two populations.
- ✗
The feature has missing values.
Why it's wrong here
Missing values do not produce bimodal shape.
- ✗
The feature contains outliers.
Why it's wrong here
Outliers cause long tails.
- ✗
The feature needs to be standardized.
Why it's wrong here
Standardization does not affect modality.
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