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

A sample-based estimate of what responsive material may remain in the unreviewed set.

Why it matters: It informs stopping decisions but never proves a perfect review.

A fixed reference sample used to compare workflow behavior over time.

Why it matters: Helpful in some workflows, but weak design can mislead.

The initial set of coded documents used to begin training a TAR model.

Why it matters: Its composition shapes early model behavior and is sometimes negotiated between parties.

The documents whose human coding decisions are used to teach the model what is responsive.

Why it matters: How it is built and documented is a frequent point of scrutiny.

The point in a continuous learning workflow where more training stops meaningfully changing the model's rankings.

Why it matters: It is a common, though not the only, signal that review can wind down.

The pre-defined rules that determine when a TAR review is considered complete.

Why it matters: Defensibility often turns on whether these were set in advance and met.

A random sample reviewed to estimate the quality of a completed review, such as its recall.

Why it matters: It is the evidence behind a claim that the review was reasonable.

The proportion of coding decisions changed when documents are re-reviewed during quality control.

Why it matters: A high overturn rate signals inconsistent coding that can undermine results.