evidence-not-assertion

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Use whenever a number, a comparison or a claim is about to enter a stage summary or the report — at analysis and writing, and any time you are tempted to state a value you have not computed in this run. Covers where a number must come from, what to do when the experiment did not run, and why an honest gap outscores a plausible sentence.

AI & Automation 804 stars 25 forks Updated today NOASSERTION

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# Every number comes from a file this run wrote A reader who cannot trace a number to an artifact has to decide whether to trust it, and a strict one decides no. That is not a style preference: a value you recall from the literature, infer from a trend, or round from memory is indistinguishable in the prose from one you measured, and the moment a single number turns out to be invented the whole report is worth nothing. So, for every quantity that reaches the report: - It was produced by code in `code/` and written to a file under `outputs/` or `results/` during this run. Name that file next to the number. - If it comes from the literature, say so in the same sentence, with the source. "The published value is X (Smith 2023); we measure Y" is a result. "The value is X" where X came from a paper is a fabrication with a citation missing. - If you have not run the experiment, do not describe what it would have shown. Write what you did run and what remains unmeasured. ## When the experiment did not run State it plainly, in one sentence, in the section where the result would have gone: what was not run, why, and what would settle it. Do not bury it in a limitations paragraph at the end, and do not substitute a proxy analysis without saying that is what you are doing. An honest "we did not measure this" costs you that one result. A plausible sentence with no measurement behind it, once found, costs you the reader's belief in the results you *did* measure — including th...

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Author
tangxiangru
Repository
tangxiangru/AutoR
Created
5 months ago
Last Updated
today
Language
Python
License
NOASSERTION

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