Distinguish a repeatable signal from a one-off result
Compare observations across experiments with sample metadata and conditions kept in view
Put the prediction to a new test
Use prior observations to predict outcomes, then evaluate them on held-out data and new experiments
Make sense of an unexpected reading
Examine instrument records, sample histories and conditions alongside unusual measurements
Give collaborators a conclusion they can question
Keep analysis and source measurements connected so others can inspect assumptions and record corrections
Define what a better outcome looks like
Does this prediction hold up on experiments it has not seen?
- Start with a fair comparison
- Define comparable conditions, a held-out set and a simple baseline before evaluating the model
- Keep the decision in view
- Inspect unexpected results with the researcher’s judgment and record changes to assumptions, protocols or analysis
- Measure the result
- Track prediction error, uncertainty calibration and consistency across repeat experiments. Preserve conditions and analysis versions so results can be reproduced