A score difference is not yet an explanation
Consider a candidate that outperforms its baseline in an evaluation. During the same period, the team also refreshes the dataset and changes the scoring instructions. This is an explanatory example, not a reported XIVTech project.
The result describes two tested configurations. It does not isolate the candidate change as the cause of the difference. That distinction matters when the question is whether this change justifies choosing the candidate, rather than simply which run scored higher.
Write the comparison claim first
State the claim the experiment is intended to support. For example: changing one component improves a specified behavior under otherwise comparable conditions.
Then identify observations that would leave that claim unresolved. These might include missing baseline records, materially different examples or scoring changes that alter what success means.
Doing this before interpreting results reduces the temptation to explain away inconvenient differences after seeing a preferred score. Inconclusive should have a concrete meaning: the available evidence cannot distinguish the proposed explanation from another plausible cause.
Keep a condition ledger
Create one entry for each condition that could affect interpretation. Record both baseline and candidate values, then explain the consequence:
- Examples and sampling: do the recorded versions describe the same population or a changed comparison?
- Scoring criteria: do the two rubrics give success the same meaning?
- Execution settings: are the known settings operationally comparable?
- Supporting components: is the experiment testing one change or a bundle?
Mark unknowns explicitly. Do not fill missing baseline settings from current defaults. The ledger is not a demand to hold everything constant forever; it makes the experiment's boundary visible.