What is the sample size used in validation?

The correct approach to validating data in an analytics context commonly involves using documents that have been set aside during the review process, typically referred to as the discard pile. These documents are often not representative of the final dataset intended for analysis; however, they can still provide valuable insights into the coding accuracy and consistency when sampled appropriately.

When using a designated number of documents from the discard pile for validation, it allows analysts to assess the performance of the coding without bias towards those that have been prioritized or deemed relevant. This method can uncover potential issues in the coding process, ensuring the validation is reflective of the entire dataset's quality.

The other options do not accurately represent best practices in data validation. For example, using the total number of documents in the queue would not provide a focused analysis on the previously coded samples. Relying solely on the number of already-coded documents overlooks crucial aspects from cases that may have been discarded and provides limited insights into the validation process. Furthermore, including all documents that have been skipped or coded neutral does not reflect a representative sample from the dataset since it may include materials that were not fully reviewed or assessed.