Outlier mitigation involves identifying and addressing data points that deviate significantly from the rest of the dataset to prevent them from skewing the results of analysis. Effective outlier mitigation can improve the accuracy and reliability of statistical models and machine learning algorithms by ensuring that the data used is representative of the underlying phenomena.