What action should be taken if outliers are identified during statistical analysis in the Validation Report?

In statistical analysis, outliers can significantly affect the results and interpretations of data. Identifying outliers is a critical part of ensuring the integrity of data validation reports. When outliers are detected, they should not simply be excluded without valid reasons because doing so can lead to biased results and misinterpretations.

The correct action involves excluding the outliers, but this should be accompanied by a clear justification. Justifying the exclusion is essential to maintain transparency and replicability in research. Documenting the statistical tool used in the analysis provides a framework for understanding how the outliers were assessed and why they do not reflect the true characteristics of the data set. This process helps uphold scientific rigor and allows others to follow the methodology, fostering trust in the results presented in the validation report.

In contrast, disregarding outliers without documentation or justification could result in an incomplete understanding of the data's variations and underlying trends. Including all outliers in the analysis can skew results and lead to potentially misleading conclusions. Excluding them without justification undermines the statistical integrity of the analysis and could raise questions regarding the credibility of the reported findings.