Once outliers are detected, several approaches can be taken:
1. Verification: Double-check the experimental conditions and data entry to rule out human error. 2. Analysis: Investigate whether the outlier represents a significant scientific finding or an anomaly. This might involve repeating the experiment. 3. Exclusion/Inclusion: Depending on the context, decide whether to exclude the outlier from the dataset or include it with appropriate notes. In some cases, outliers can provide valuable insights into rare catalytic behaviors.
