/principle-explain-the-number

name: principle-explain-the-number
description: "Apply 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."
disable-model-invocation: true

Explain the Number

A measured number is a claim about a system. Before you trust it, report it, or act on it, find what limits it and rule out that it measured something else.

**Why:** A run that went wrong still prints a plausible number. Requests that failed, a cache that skipped the work, code that never ran, a side left on default settings, and run-to-run noise all produce results that look fine. If you cannot say why the number is not twice as good, you do not know what you measured.

**Pattern:**

• **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.
• **List what else the number could be measuring, and rule out each one with evidence.** The usual suspects are errors, skipped or cached work, an untuned side, 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, so a reader can check the claim.

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 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. This checks that a measured number means what you say it means.