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To prevent misinterpretation of empirical results, you can perform a defamiliarization audit that systematically lists every visible feature in your output, proposes alternative explanations, and flags unexplained spikes before you write your report.

Defamiliarization in an empirical audit forces you to treat every visible element in a figure or table as unfamiliar, requiring you to generate candidate generators and isolate the hardest unresolved feature before interpreting the findings.

You can check sample size consistency by running a systematic audit workflow that verifies N counts across outputs, tests pattern coherence, and enforces an ownership test to ensure you can account for every number.

You should run an empirical audit right when your output first appears and before you begin drafting the write-up, ensuring your writing does not lock in premature narratives based on misread signs.

Yes, auditing results files isolates visible features like unexplained spikes and generates candidate generators to propose alternative explanations, ensuring you explicitly flag open questions through an ownership test.

If you cannot account for every number during an interpretation audit, the ownership test requires you to explicitly flag those open questions rather than proceeding to report the findings with unresolved inconsistencies.