What type of data should not be rejected even though it appears unusual?

Valid but unusual data should not be rejected because it can provide valuable insights and reveal trends or patterns that are not immediately apparent. Such data points may seem out of the ordinary, but they could indicate significant variations or highlight outliers that are essential for a comprehensive analysis.

In the context of data analytics, valid but unusual data can stem from rare events or unique circumstances within the dataset. Dismissing this type of data could lead to an incomplete understanding of the subject matter, potentially disregarding important information that could influence future decisions or strategies.

On the other hand, invalid data needs to be identified and removed as it does not meet the necessary criteria for accuracy or relevance. Boundary condition data represents the extremes of conditions and can be useful, but that doesn't mean it should be treated the same way as valid but unusual data. Document testing is a process of examining the documentation associated with data and does not pertain to the actual data quality itself.