A plain-language glossary for TAR, sampling, and defensibility.

The share of documents in a population that are actually responsive.

Why it matters: Low richness changes sample sizes, expectations, and review cost.

The share of truly responsive documents that were found by the workflow.

Why it matters: Often the headline metric in defensibility discussions.

The share of documents marked responsive that were actually responsive.

Why it matters: It affects cost and reviewer burden more than completeness.

A document review process that uses machine learning to predict which documents are relevant, rather than relying solely on linear human review.

Why it matters: It is the umbrella term for the workflows the rest of this glossary describes.

An earlier name for TAR, referring to using a trained model to code documents as responsive or not.

Why it matters: Courts and protocols often still use this term interchangeably with TAR.

A TAR approach where the model is continuously retrained on reviewers' coding decisions, constantly reprioritizing the highest-value documents to review next.

Why it matters: It is the dominant modern workflow and changes how you decide when review is complete.

A one-shot TAR workflow: train a model on a fixed sample, apply it to the full set, then validate the result.

Why it matters: It produces a clean statistical story but assumes the collection is stable.

A continuous TAR workflow (usually CAL) where training and review happen together until a stopping point is reached.

Why it matters: It finds responsive documents faster but shifts the defensibility question to the stopping decision.