Section 017 · Chapter 3, Sampling and Uncertainty
Basic Stats Every AI Builder Should Know
A few practical statistics can help developers explain non-deterministic quality without pretending the data is more precise than it is.
basic stats builder know
What to do
- Use training metrics as background signals.
- Define runnable checks that exercise basic stats builder know.
- Set acceptable outcomes and blocker failures for basic stats builder know before running the evaluation.
Evidence to preserve
- Preserve the inputs, versions, configurations, raw outcomes, and results for basic stats builder know needed to reproduce work on Basic Stats Every AI Builder Should Know.
- Report results for basic stats builder know by relevant slice, separate blocker failures from averages, state uncertainty and blind spots, and connect the result to a release decision.
Expert note
Expert reports avoid letting one metric dominate. For skewed distributions, the median and percentiles may explain user experience better than the mean. For safety-sensitive systems, the tail and failure rate often matter more than average quality.
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Cite this page
Jason Arbon. "Basic Stats Every AI Builder Should Know." Testing AI Knowledge Edition, section 17.
https://jarbon.ai/testing-ai/knowledge/ch017-basic-stats-builder-know.html