Nullable Fields and Edge Cases
Reference for how to read incomplete structure, missing fields, and ambiguous edge cases conservatively in the current release.
Some workflows remain technically possible even when the input structure is incomplete, ambiguous, or unusually sparse.
This page explains why those cases should still be read conservatively.
Missing or nullable structure
If key design information is missing or nullable, the product may still process the data, but that does not mean every interpretation is equally reliable.
Examples include:
- missing subject identifiers
- absent grouping context
- incomplete covariate fields
- nullable metadata that weakens design interpretation
Ambiguity is not the same as confirmation
When the structure is ambiguous, the system may choose one of several practical behaviors:
- continue with a warning
- ask for confirmation
- narrow the allowed export behavior
The important point is that continuation does not equal full certainty.
Edge cases and support claims
A method or figure can be supported in the general case while still having edge cases that require extra caution.
For example:
- an analysis may be supported for clean inputs but less interpretable when key structure is incomplete
- a figure may render successfully while its implied comparison remains difficult to justify
- a workflow may proceed while preserving caveats in audit metadata
Practical rule
If a key field is missing, nullable, or only weakly inferable, treat the result as needing stronger researcher review even when the product does not fully block the workflow.