What technique helps identify data outliers during validation?

Statistical analysis is crucial for identifying data outliers during validation because it involves applying various mathematical methods and statistical tests to a dataset. Outliers are typically values that significantly differ from the majority of the data points, and statistical analysis provides concrete metrics, such as z-scores, interquartile ranges, or box plots, which help in quantifying how far a data point deviates from the norm. By utilizing these statistical techniques, an analyst can systematically identify which data points fall outside the expected range, thereby ensuring the integrity of the dataset for further analysis.

Data segmentation, data cleansing, and data visualization can play supportive roles in the data validation process. However, they do not specifically focus on the identification of outliers in the same quantitative manner as statistical analysis. Data segmentation may categorize data into different segments, which might help isolate outliers, but it does not provide the mathematical context needed to recognize outliers effectively. Data cleansing focuses more on correcting or removing erroneous data, and while it might involve the removal of outliers, it does not explicitly identify them. Data visualization can illustrate outliers through graphs but does not inherently provide the statistical foundations necessary for their identification. Thus, statistical analysis stands out as the most effective technique for pinpointing outliers in