run-the-requested-analysis

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Use when the supplied data looks synthetic, degraded, incomplete or wrong, and whenever you are tempted to reframe the study around what you found about the inputs, the harness or the evaluation. Covers what to do with a real data problem without losing the study.

AI & Automation 804 stars 25 forks Updated today NOASSERTION

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# A problem with the inputs is a finding to add, not a study to replace You will often be right that the supplied data is a thin surrogate, that a file is mislabelled, that the corpus cannot support the claim it was cut from. Noticing that is good research. What follows it decides whether the run is worth anything. The failure mode is consistent: the run investigates the inputs, finds something real, and reorganises the whole study around that finding. The requested analysis is then never run at all — not because it was impossible, but because the run stopped being about it. From the inside this feels like following the evidence. From the outside every requested result is missing. ## Do both, in this order 1. **Run the requested analysis on the supplied data anyway**, exactly as asked, and report it as the primary result. If the data is a surrogate, the numbers are what they are; caveat them in a sentence. 2. **Then add what you found**, as a section of its own: what is wrong with the inputs, how you established it, what it means for the numbers above. 3. If you fetched better data, report that as an *additional* arm beside the requested one, never instead of it. The caveated result plus the audit is strictly more than the audit. It is also more useful: a reader can see both what the specified protocol yields and why they should discount it. ## What never to substitute Do not turn the study into an analysis of the harness, the evaluation, the scoring, or 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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