principle-explain-the-number

Explain the Number

Before you trust a number, find what limits it and rule out that it measured something other than the work you think.

• 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.
• Beat the noise, not just the mean. Repeat the measurement and compare the delta against run-to-run variance. A 3% gain inside ±5% variance is not a gain; it is a different sample. A baseline taken on a cold cache against a result taken on a warm one measures the cache, not your change.
• Keep the evidence with the number. Put the run count, the spread, and the limiter in the notes or a linked artifact.

When the number decides whether a change stays:

Result vs. baseline	Action
Past the threshold, tests green	Keep. Commit with the before/after numbers in the message.
Within noise (no measurable change)	Revert.
Worse	Revert.
Improved, but a test went red	Revert. A regression wearing a win's clothing.

For a performance number, run the full procedure with the benchmark-checklist 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, which checks that an output is real.