Analysis cannot rescue an unknown dataset
Statistical software can calculate results even when a dataset contains structural or semantic problems.
This makes quality review a prerequisite for interpretation rather than an optional cleanup step.
Basic checks reveal important problems
Researchers should inspect missingness, impossible values, duplicated records, unexpected categories, inconsistent units and variable types.
The purpose is not to force every dataset into perfection but to make limitations visible before analysis.
Validation rules need context
A value that is invalid in one study can be legitimate in another.
Quality systems should therefore combine general structural checks with study-specific variable definitions and constraints.
Preserve the findings
Quality findings should remain associated with the dataset version that produced them.
This creates an auditable link between source data, corrections, exclusions and subsequent statistical outputs.