The regression your eval set is too small to catch
Article summary
Quick briefing — cleaned from the original RSS feed
TL;DR. To catch a drop from a 0.90 pass rate to 0.85 at 80% power (one-sided, alpha 0.05), you need about 253 examples. A 50-example set has roughly 35% power, so it misses that regression about two times in three. The move that matters is not "collect more data" as a slogan. Size the set to the effect you actually care about, report a confidence interval on each run instead of a bare point delta, and prefer per-criterion binary labels over vague graded scores. The two-point win that wasn't A…
1Key Takeaways
- To catch a drop from a 0.90 pass rate to 0.85 at 80% power (one-sided, alpha 0.05), you need about 253 examples.
- A 50-example set has roughly 35% power, so it misses that regression about two times in three.
- The move that matters is not "collect more data" as a slogan.
- Size the set to the effect you actually care about, report a confidence interval on each run instead of a bare point delta, and prefer per-criterion binary labels over vague graded scores.
2AIWedia Score
8.4/10
High relevance — worth your attention today
Based on source trust, recency, category impact, and story depth.
3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that to catch a drop from a 0.90 pass rate to 0.85 at 80% power (one-sided, alpha 0.05), you need about 253 examples.
Explore related
Browse toolsCoding AI news
Explore curated coding ai tools on AIWedia — compare, rank, and launch from our directory.
Full story on DEV — ML
Read full articleHeadlines aggregated via RSS for discovery on AIWedia. Original content © DEV — ML. We link to the source and do not republish full articles.