What approach would you take to identify and clean outliers in a dataset?

Example Answer

I would begin by asking whether the outliers are actually errors, rare but valid observations, or signals of a separate population. That distinction matters because removing all extreme values blindly can make the dataset look cleaner while actually throwing away important business cases.

In practice, I would use a mix of visualization and statistical methods such as box plots, z-scores, robust quantiles, and domain thresholds to identify unusual values. Then I would inspect them with context. If the outliers are caused by measurement error or bad joins, I would correct or exclude them. If they are valid but extreme, I might transform the feature, cap values, model them separately, or keep them untouched depending on the use case. The key is that outlier handling should be explicit, justified, and reproducible rather than a silent cleanup step.

Common Poor Answer

A weak answer says they would remove all outliers because they distort the model, without checking whether those records are errors or important rare events the model actually needs to learn from.