---
name: michael-denyer-principle-explain-the-number
description: "Apply before you measure, and before you trust, report, or act on a number you measured: a speedup, a regression, a throughput, a latency, or an eval result. Find what limits it, and rule out that it measured something other than the work you think."
license: "MIT"
metadata:
  decimalai-skill-id: "11ec08d4-89e9-4f15-9587-171977bcb245"
  decimalai-version-id: "452d1407-3dca-427d-9a71-c634004fb34c"
  decimalai-version: "1"
  decimalai-body-sha256: "0ad2b5bee073d483ce57528282bbcc42a62926ea99ea63ef43463347a7017284"
  decimalai-name: "michael-denyer/principle-explain-the-number"
  decimalai-format: "1"
  decimalai-invocation: "model"
  decimalai-category: "testing"
  decimalai-source: "https://app.decimal.ai/s/michael-denyer-principle-explain-the-number@1/SKILL.md"
---
# Explain the Number

- **Write the decision before you run.** Name the result that would change a decision, and the noise floor. Stop when the run-to-run spread is larger than the effect.
- **Ask "why not double?"** Name the resource or code path that bounds the result, such as a core, a lock, the disk, the network, or the load generator itself. Get it from a profile or from system counters taken during a run, then map it to source. A guess from reading the code is not a limiter. If you cannot say why the number is not twice as good, you do not know what you measured.
- **List what else the number could be measuring, and rule out each one with evidence.** The usual suspects are errors, skipped or cached work, code that never ran, an untuned side (one left on default settings), noise, and a piece too small to matter end to end.
- **Keep the evidence with the number.** Put the run count, the spread, and the limiter in the notes or a linked artifact.

For a performance number, run the full procedure with the [benchmark-checklist](../benchmark-checklist/SKILL.md) skill. For an eval result, ask the same of the trials: did every run do the task, does the gap hold across trials and models, and does the scenario matter.

You skipped this when a run started without the result that would change a decision, when the evidence behind a number has no run count, no spread, or no named limiter, or when the time saved is larger than the time the changed piece took.

Distinct from [Prove It Works](../principle-prove-it-works/SKILL.md), which checks that an output is real.