Outlier Rejection

Outlier rejection is a process used in data analysis to identify and remove data points that significantly differ from the rest. These unusual points may result from errors, measurement issues, or rare events, and can distort results. By rejecting outliers, analysts ensure that the overall analysis reflects the true pattern or trend in the data. This process helps improve accuracy and reliability, especially in scientific or statistical work, by focusing on data that genuinely represents the underlying phenomena rather than anomalies or errors.