Replace identical data test with precise delta validation test

[asamal4]

Original PR: #160
Additionally fixed lint issue, restored relative change assert & added statistical non-significance assert

Replaced test_compare_score_distributions_identical_data with test_compare_score_distributions_precise_delta to provide more meaningful validation.
The new test validates behavior with a precise 0.001 mean difference instead of identical values, using reasonable floating-point tolerance (1e-6) and verifying mean calculations, relative change percentage, and that statistical tests correctly report non-significance for small differences with small sample sizes.

This change improves test coverage by validating real-world scenarios where differences are measurable but not statistically significant, rather than testing the trivial case of identical data where difference is exactly zero.

Description

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• Documentation Update
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• Unit tests improvement

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Summary by CodeRabbit

• Tests
  • Improved test coverage for evaluation comparisons by switching to a precise small-delta scenario. Tests now validate detection and reporting of a 0.001 mean shift while confirming statistical non-significance, increasing confidence in calculation accuracy for subtle differences.

[coderabbitai]

No actionable comments were generated in the recent review. 🎉

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Reviewing files that changed from the base of the PR and between 6675a21 and d7f22fe.

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• tests/script/test_compare_evaluations.py

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Walkthrough

A unit test in evaluation comparison tests was renamed and refactored: from validating identical score distributions to validating a precise delta (scores2 = scores1 + 0.001). Assertions now check computed means, mean_difference, and relative_change against expected values with approximate tolerances and non-significance of statistical tests.

Changes

Cohort / File(s)

Summary

Test Method Refactor tests/script/test_compare_evaluations.py

Renamed test_compare_score_distributions_identical_data to test_compare_score_distributions_precise_delta (applied to two occurrences) and replaced the identical-data scenario with a delta-based scenario. Updated test data to use a 0.001 offset, recomputed expected means/difference, and adjusted assertions to verify mean values, mean_difference, relative_change, and non-significance of statistical tests with approximate matching.

Estimated code review effort

🎯 2 (Simple) | ⏱️ ~10 minutes

🚥 Pre-merge checks | ✅ 3

✅ Passed checks (3 passed)

Check name	Status	Explanation
Description Check	✅ Passed	Check skipped - CodeRabbit’s high-level summary is enabled.
Title check	✅ Passed	The title directly and accurately describes the main change: replacing a test method that validates identical data with one that validates precise delta (0.001 difference).
Docstring Coverage	✅ Passed	Docstring coverage is 100.00% which is sufficient. The required threshold is 80.00%.

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[coderabbitai]

Actionable comments posted: 1

🤖 Prompt for all review comments with AI agents

Verify each finding against the current code and only fix it if needed.

Inline comments:
In `@tests/script/test_compare_evaluations.py`:
- Around line 210-222: Add assertions that validate the relative change and
non-significance: compute expected_relative = expected_diff / expected_mean1 and
assert result["relative_change"] == pytest.approx(expected_relative), and assert
the test result reports non-significance by checking the boolean flag (e.g.,
result["significant"] is False) or the explicit key your code uses (e.g.,
result["statistically_significant"] is False); place these after the existing
mean and mean_difference assertions to complete the scenario for result (keys:
"relative_change" and the significance flag).

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ℹ️ Review info

Configuration used: Path: .coderabbit.yaml

Review profile: CHILL

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📥 Commits

Reviewing files that changed from the base of the PR and between 6441d75 and 6675a21.

📒 Files selected for processing (1)

• tests/script/test_compare_evaluations.py

[VladimirKadlec]

LGTM