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A development set with 100 examples cannot reliably distinguish 88.0% accuracy from 88.1% accuracy. In many machine learning settings, a 100-example dev set is too small to measure a difference that small.

Tiny Development Sets Miss Very Small Accuracy Gains

What can a 100-example validation set tell you about 84.0% versus 84.1% accuracy?

Can a dev set with 100 examples reliably detect a 0.1 percentage-point accuracy gap?

If a validation set has 1,000 examples, one additional correct prediction changes accuracy by _____ percentage point.

Small Validation Sets Miss Tiny Accuracy Changes

Match each element in the comparison with its meaning.

Order the steps for judging whether a validation set can detect a small accuracy gap.

Question:
A development set contains 64 labeled examples. Model A is correct on 51 examples, and Model B is correct on 52 examples. In a short analytical response, explain what this says about using the set to choose between the models.

Sample answer:
The one-example difference is too small to treat as strong evidence that one model is better. On 64 examples, moving from 51 correct to 52 correct changes accuracy by about 1.56 percentage points, which can easily come from sampling noise. A larger development set would give a more stable comparison. The broader lesson is that 64 examples is still a fairly small sample for model selection in practice.

Key points:
- The development set has 64 examples.
- Model A is correct on 51 examples.
- Model B is correct on 52 examples.
- The difference is one example, or about 1.56 percentage points.
- That gap is not a reliable basis for choosing between the models.
- 64 examples is a small sample for this kind of evaluation.

Rubric:
A strong response identifies the 64-example development set, the 51-versus-52 comparison, the one-example gap, and the conclusion that the set is too small to support a confident model choice. It should also note that 64 examples is a small sample for model comparison.

Limits of a 64-Example Development Set

Scenario:
A lab compares Model A and Model B on a validation set containing 100 records. Model A scores 91.0% accuracy and Model B scores 91.1% accuracy.

Question:
Should the lab treat the measured gap as something this validation set can reliably distinguish?

Sample answer:
No. With only 100 validation examples, that data sample is too small to confidently tell apart models whose accuracies differ by just 0.1 percentage point. The safest conclusion is that the observed gap is within the noise level of such a small evaluation set.

Key points:
- The reported difference is 0.1 percentage point.
- The validation set includes 100 examples.
- That sample size is too small to separate such a tiny gap.
- A small evaluation set can make near-tied models look different by chance.

Rubric:
The response should say the set is not large enough to reliably detect the 0.1-point difference, mention the 100-example size, and explain that the observed gap may be due to sampling noise.

Can a tiny validation set resolve a 0.1-point accuracy gap?

Question:
Answer in one to three sentences using the accuracy figures and the stated limitation.

Sample answer:
A 100-example validation set is too coarse to tell whether a model at 84.0% is really better than one at 84.6%, because one example changes the score by 1 percentage point. With so few cases, the observed gap could just be noise rather than a real improvement.

Key points:
- 100-example validation set
- 84.0% versus 84.6% accuracy
- The difference is too small to measure reliably

Rubric:
The answer should refer to the 100-example size, the two accuracy values or a sub-1-point difference, and the fact that this gap cannot be distinguished reliably.

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