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MATH-C10-C-001

Subtract train and validation counts from the total.

Add all four numbers, then divide by 4.

Width means upper end minus lower end.

Each fold gets one turn as validation data.

Use the Bayesian update shape before normalization: likelihood times prior.

Think about what happens if every model choice is influenced by the test score.

High training error means the model is struggling even before we ask whether it generalizes.

Likelihood compares parameter choices or models against observed data. It does not certify truth.

Bootstrap sampling is with replacement.

Statistical evidence and practical importance are related, but they are not the same question.

Ask how the examples were collected, not only how many there are.

One sample can be unusual even when the rule is reasonable.

Maximum likelihood compares the candidates included in the model family.

The interval surrounds an estimate, not an individual future example.

If the scores helped choose the model, they are part of development evidence.