Use Box Plots, Scatter Plots, and Histograms- Quickly spot outliers and assess their distribution. Example: In a financial services project, a scatter plot can highlight transactions that are unusually high or low compared to the average, helping you identify potential fraud or exceptional cases. Apply Z-Scores, IQR Method, or Grubbs' Test: Quantitatively identify outliers based on statistical thresholds. Example: In a quality control analysis for a manufacturing client, use Z-scores to flag products with defect measurements that significantly deviate from the mean, ensuring only high-quality products are delivered. Determine if outliers result from mistakes or represent meaningful anomalies.

When faced with statistical outliers in client projects, it’s important to approach them carefully without jumping to conclusions. My first step is to investigate the cause—whether it's a data entry error, a measurement anomaly, or a valid deviation. I use visualization tools like box plots or scatter plots to identify and assess these outliers visually. Depending on the context, I either adjust my model to account for them or treat them as exceptions that don’t reflect the overall trend. In some cases, I might apply data transformation techniques to reduce their impact.