How does noise affect outlier detection in data analysis?

How does noise affect outlier detection in data analysis?

Noise can obscure true outliers, making them harder to detect. It can also introduce false positives.

What types of noise are most problematic for outlier detection?

Common types include Gaussian noise, salt-and-pepper noise, and systematic errors, each affecting detection differently.

Are there techniques to mitigate the impact of noise?

Yes, techniques like robust statistical methods, filtering, and data preprocessing can help reduce noise effects.

Can you give an example of a robust method for outlier detection?

One example is the use of the Median Absolute Deviation (MAD), which is less sensitive to noise than the mean.