Tiny Development Sets Miss Very Small Accuracy Gains

Typical Development Set Sizes for Tiny Accuracy Gains

High-Impact Business Systems May Justify a Larger Development Set

Formal significance tests for validation-set changes

What dev set size is most suitable for spotting a 0.1 percentage-point gain in accuracy?

A development set should always be expanded to the maximum possible size, even after it is already large enough to reveal meaningful performance changes.

Validation set size for noticing a tiny accuracy change

Match each evaluation target with the dev set size it suggests.

Order the steps for deciding whether a dev set is large enough to detect a useful accuracy gain.

Match dev-set size to the smallest gain you care about.

Choose a dev set size that can detect a tiny but important gain.

Why is a 150-example dev set not enough to tell 83.0% from 83.4% accuracy?

When is a validation set much larger than 10,000 examples most justified?

If a validation set is already large enough to tell whether one model is meaningfully better than another, it does not need to be made much larger.