How to present evaluation results clearly to technical and non-technical stakeholders.
Key vocabulary
Practical significance — whether a measured improvement is meaningful in real-world deployment, not just statistically detectable.
Confidence interval — a range expressing uncertainty around a measured metric; e.g. “accuracy is 87.3% ±1.2%.”
Benchmark score framing — how results are contextualized: relative to baselines, prior versions, or state of the art.
Meaningful for production — a phrase used to link evaluation results to user-facing or business outcomes.
Evaluation result narrative — the story you tell around numbers: what improved, why it matters, and what the limitations are.
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1 / 5
A researcher says: “The improvement from 84.1% to 85.3% on MMLU is statistically significant but may not be practically significant.” What does “practically significant” mean here?
Practical significance asks: does this improvement actually change anything for users? A 1.2 percentage point gain might be statistically detectable with enough test cases, but if users cannot notice the difference, it may not justify the cost of deploying a new model. Good stakeholder communication always addresses both statistical and practical significance.
2 / 5
A report states: “Our model achieves 91.4% accuracy on the held-out test set (95% CI: 89.8%–93.0%).” What does the confidence interval communicate?
Confidence intervals express measurement uncertainty. A single point estimate (91.4%) implies false precision — it depends on the specific test examples used. Reporting a CI acknowledges that with a different sample, results would vary. This is especially important when comparing models: if two CIs overlap substantially, the difference may not be reliable.
3 / 5
You are presenting model results to a non-technical business stakeholder. Which framing is most effective?
When communicating to non-technical stakeholders, translate metrics into business outcomes: time saved, cost reduced, user impact. Technical metrics like ROUGE-L, perplexity, and F1 are meaningful to ML engineers but opaque to business audiences. The best eval narratives bridge both worlds: start with business impact, then support with technical evidence.
4 / 5
A colleague writes: “This improvement is meaningful for production use because latency decreased by 40ms at p95, which brings us within our SLO.” What makes this a strong evaluation statement?
Strong evaluation statements link measurements to production criteria. “This improvement is meaningful for production use because...” is a key signal phrase that contextualises numbers: it answers “so what?” by tying the result to an SLO, user experience threshold, or business requirement. Numbers without context are hard to act on.
5 / 5
When presenting a new model version, a team says: “Our model achieves X% on benchmark Y, compared to Z% for the previous version and W% for the current state of the art.” This multi-point framing is effective because:
Benchmark score framing works best with at least two reference points: a baseline (what we had before) and a comparison (what others have achieved). Without these anchors, a score of “87%” is uninterpretable. Good evaluation communication always contextualises numbers: “up from X, approaching state-of-the-art Y, achieved with Z.”