Explain the gap between theory and practice for significance tests on dev set changes.
Question: In a concise analytical response, explain why the source distinguishes the theoretical availability of statistical significance testing from its practical use by teams.
Sample answer: A team can theoretically test whether an algorithm change produces a statistically significant difference on the dev set. In practice, however, most teams do not perform this test. Publishing an academic research paper is the exception identified by the source. For ordinary interim progress, the author usually does not find statistical significance tests useful, so theoretical availability does not make them a routine development practice.
Key points:
- A significance test on a dev set change is theoretically possible.
- Most teams do not perform such tests in practice.
- Publishing academic research papers is the stated exception.
- The author usually does not find the tests useful for measuring interim progress.
Rubric: A strong response clearly contrasts theoretical possibility with typical practice, identifies academic research publication as the stated exception, and accurately explains the author's view of interim-progress measurement without adding unsupported claims.
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When are teams most likely to test whether a dev set change is statistically significant?
Most teams routinely test every dev set improvement for statistical significance.
Most teams skip significance tests unless publishing _____.
Match each context with the source's position on significance testing.
Order the reasoning for deciding whether to test a dev set change.
Explain the gap between theory and practice for significance tests on dev set changes.
Should a team test every interim dev set change for statistical significance?
Why are significance tests uncommon for interim dev set progress?
Which statement best captures the source's position on testing dev set changes?
The source rejects the possibility of testing dev set changes because teams rarely do it.