What is a characteristic of document testing?

Document testing is a critical aspect of data analytics as it ensures the accuracy and reliability of data through various methodologies. One of its key characteristics is the involvement of manual verification methods. This process typically includes reviewing data entries, checking for completeness, and validating content against source documents or through user expertise. These manual methods allow for a more nuanced and contextualized examination of the data, which may not be fully captured by automated systems alone.

In the context of data analytics, relying solely on automated calculations may miss subtle issues that human reviewers, with their understanding of context and potential errors, can identify. Peer feedback is often considered an essential part of various testing processes, allowing for collaborative insight and improvement, so excluding it would go against best practices. Additionally, while quantitative metrics can be valuable in assessing data quality, document testing encompasses both qualitative and quantitative approaches to ensure comprehensive validation. Therefore, the characteristic of involving manual verification methods is fundamental to robust document testing practices.