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False negative

A real case that a check or matching step failed to flag: two records for the same customer left unmatched, or a wrong value that passed validation.

False negatives are the quiet kind of error, because nothing draws attention to them. Finding them means examining a sample of what was not flagged.

Where it comes up

Near-duplicate recordsTwo rows describe the same real thing but differ enough that an exact comparison misses them.
The same entity in two systemsOne person or organization exists in two systems under different identifiers, with nothing linking the records.

See also

False positiveA case that a check or matching step flagged wrongly: two different people treated as one, or a correct value reported as an error.
Match thresholdThe similarity score at or above which two records are treated as the same thing. Pairs scoring below it are left as separate records.
SamplingExamining a subset of the rows in place of all of them, either to get a quick picture of a dataset or to check the result of a change by hand.

Karg checks for many of these when it profiles a file, applies the fixes it is confident about, and sets the rest aside for your review. See how it works, or have our team clean your data for you.