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Coating Trial Data Corrections: Preserve the Original Entry and the Reason
A transcription error can be corrected without erasing the original evidence. Preserve the source entry, corrected value and reason so a reviewer can distinguish a genuine correction from a result changed to improve the conclusion.
Updated · NANOLAB Knowledge
The key distinction
The process can be simple, but it should be consistent. Keep raw instrument files or original forms separate from the cleaned analysis dataset. Record who made a material correction and how it was verified, particularly when the change affects a pass decision or product ranking.
Practical checks
- Compare the suspected error with the original source.
- Document the corrected value and supporting reason.
- Recalculate affected summaries and retain the prior version where needed for traceability.
Example
Illustrative example: a decimal point is entered incorrectly when a measurement is copied into a spreadsheet. The instrument record supports the correction. A brief change record preserves that evidence, while silently overwriting the cell would leave a later reviewer unable to explain the altered result.
Scope and limits
Do not use a correction process to replace valid but inconvenient observations. This guide does not require a particular database or software system. It requires a traceable connection between the original evidence, the corrected dataset and any conclusion that depends on the change.
Related guidance
- Coating Trial Exclusions: Publish the Rule Before Removing Data
- Coating Trial Data: Keep Raw Observations Separate from Scores
- Coating Trial Data: Separate Zero, Missing and Not Applicable
- Coating Trial Aggregation: A Better Overall Rate Can Hide a Worse Result in Each Group
Ask NANOLAB about the exact product and intended use. Use the current product documentation for formulation-specific instructions.
Continue reading
Coating Trial Exclusions: Publish the Rule Before Removing Data
Data exclusions can be legitimate, but selective removal after seeing unfavourable results undermines the comparison.
Coating Trial Data: Keep Raw Observations Separate from Scores
A score compresses observations into a category. Retaining the underlying observations allows later review when the scoring rule is questioned.
Coating Trial Data: Separate Zero, Missing and Not Applicable
A recorded zero, a missing observation and a not-applicable field have different meanings. Keep them distinct so that averages, rates and completion checks do not silently treat absent information as a measured outcome.