Glossary
Signal / noise separation
Definition
The discipline of telling a real signal apart from background variability — In AI outputs, in personal intuition, or in noisy datasets — Without collapsing the distinction prematurely into a story.
Why it matters
Most premature conclusions in AI evaluation, and most poor intuitive calls in everyday decisions, are signal/noise errors: treating one suggestive sample as evidence, or dismissing a real but quiet pattern as variance. The same craft applies in both domains and is teachable.
Example
A single odd answer from a frontier model is not a finding. The same odd shape across five reformulations, two seeds, and a smaller sibling model is.