What does the Margin of Error (Elusion) affect?

The Margin of Error (Elusion) plays a critical role in determining the sample size based on Elusion accuracy. Elusion refers to the potential risk of missing relevant documents or key information during the review process. The Margin of Error indicates the extent to which the sample may differ from the actual population in terms of Elusion, which directly influences how many documents need to be reviewed to achieve a certain level of confidence in the results.

When assessing the accuracy of Elusion, a larger sample size may be required to ensure that the results accurately reflect the totality of the documents in a dataset. This is because a smaller sample might not provide enough data to confidently gauge the level of undetected relevant information. Consequently, understanding and calculating the Margin of Error allows project managers and analysts to make informed decisions about sample sizes necessary for achieving reliable Elusion accuracy in their reviews.

When focusing on the other choices, they either pertain to different aspects of sampling and review processes (like recall accuracy and review speed) or do not specifically address how Elusion impacts sample size in a quantifiable manner. Therefore, the connection between Margin of Error and Elusion highlights its importance in effectively managing and evaluating data review projects.