What is the primary consequence of applying standard scaling to a feature with a bimodal distribution?

Question

Data Science — Hard

What is the primary consequence of applying standard scaling to a feature with a bimodal distribution?

Accepted Answer

Standard scaling (z-score normalization) is a linear transformation that subtracts the mean and divides by the standard deviation. It does not change the shape of the underlying distribution, meaning a bimodal distribution remains bimodal. It merely re-centers the data at zero and scales the spread to unit variance. Distractors incorrectly suggest that scaling alters the fundamental shape or statistical properties of the distribution, which is a common misconception regarding linear feature scaling.