Which data-quality validation method involves comparing incoming data to historical patterns or expected ranges so that values outside those norms can be flagged before further processing?
Data audits
Data profiling
Cross-validation
Reasonable expectations
Reasonable expectations validation establishes what a "normal" value should look like based on past data or business rules. Incoming records are checked against those expectations, and any value falling outside the expected range is flagged for review. Cross-validation compares two different models or datasets, data profiling explores structure and statistics without necessarily setting thresholds, and data audits are broader periodic reviews rather than record-level checks of plausibility.
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