RelativityOne Analytics Specialist Practice Exam

When documents are skipped or coded as neutral during a validation process, it lowers the randomness of the sample and can introduce bias into the validation statistics. This is due to the fact that omitting certain documents or categorizing them as neutral means that the sample is no longer a true reflection of the entire dataset.

In a well-structured validation process, a random sample is critical for obtaining reliable statistics that can be generalized to the overall population. By skipping documents, the sample may lack diversity and not include documents that could significantly contribute to the understanding of the dataset as a whole. This can lead to skewed results, where the validation statistics do not accurately represent the quality or characteristics of the entire collection of documents.

Therefore, while it may seem that skipping or neutral coding would not have an impact, it actually compromises the integrity of the sample and the validity of any conclusions drawn from the validation process. In this context, introducing bias means that the results might favor certain patterns or trends that are not indicative of the actual document population. This underlying principle emphasizes the importance of a comprehensive and unbiased approach to sampling during validation efforts.