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A high match rate can still mislead

A high match rate does not guarantee an unbiased dataset. If errors cluster across variables or groups, the direction and size of bias cannot be inferred from the rate alone.

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False matches join records from different people. They can add noise and dilute associations between variables, often pushing effect estimates toward zero, though not always.

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Missed matches leave records from the same person unlinked. That shrinks the sample and statistical power, and informative misses can undercount exposures or outcomes.

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Linkage success may not be random. If some groups are harder to link, the linked data can omit important subgroups and distort comparisons.

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Practical response: validate against a gold standard, compare linked with unlinked records, then test whether conclusions change under different linkage rules.

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