Make the Dev Set Match the Main Improvement Goal

A Shared-Source Validation/Test Gap Usually Means Validation Overfitting

Why Different Dev and Test Distributions Complicate Diagnosis

Mismatched Validation and Test Splits Can Make Chance Matter More

If a model is tuned on a development set and then performs worse on a separate test set, even though both sets come from the same source distribution, what is the most likely explanation?

True or False: If validation and test data are drawn from different populations, a score gap between them always has one clear cause.

If a model has started fitting the validation set too closely and the training and validation data come from the same distribution, the usual remedy is to get more _____ data.

Why should the development set match the main goal of the project?

When validation and test data come from the same distribution, a strong validation score followed by a much weaker test score suggests overfitting to the validation set.

If the development set is being overused and the training and development data come from the same distribution, the practical fix is to collect more _____ data.

Match each development-and-test-set situation with its consequence for debugging.

Order the troubleshooting steps when validation performance is strong but holdout performance is weak.

Why a Test Set Can Score Lower Than a Dev Set

A test score from a different data distribution tells you exactly why the model failed.

After the development and test sets are set, the team will spend its effort improving _____ set performance.

Match each concept about dev and test set distributions to the correct description.

How to Choose Dev and Test Sets for Reliable Iteration

Explain what poor final performance means when the tuning set and final set do or do not match.

Why a model can look strong in development but weak in final evaluation

What is the diagnosis and remedy when test results lag far behind dev results under the same data distribution?