How would you handle skewed data in a dataset?

Official Answer

First ask whether skewness is expected and whether the chosen analysis actually requires a different distribution. Spending and waiting times are often naturally skewed. Do not remove valid observations merely to make a histogram look normal.

Check data-entry errors, units, missingness and extreme observations. Summarize the distribution with plots and relevant quantiles. The mean and median answer different questions: median spending describes a typical person, while mean spending matters for expected revenue.

A log transformation can be useful for positive data with multiplicative variation; zeros and negative values need an explicit treatment. A transformation does not guarantee normality, and back-transforming a prediction is not automatically an unbiased estimate of the mean on the original scale. Fit any learned transformation using training data only.

Alternatively, choose a model or inferential method appropriate to the outcome and sampling process. For regression, distinguish assumptions about errors from the marginal distribution of every input. Robust methods can help with particular outlier or distributional issues, but changing to a median or trimmed mean changes the target quantity.

Avoid arbitrary binning: it discards information and introduces threshold choices. Report the original-scale interpretation, assumptions and sensitivity checks.