Removing 40% of a dataset’s rows does not prove that those rows were wrong. In this personal account, the author says they can verify that 27% of the deleted rows were errors; the denominator, dataset, evidence, and year are not supplied, so neither percentage is independently verified. The central lesson is that a suspicious-looking record is not automatically a bad one: cleaning decisions need evidence, a stated purpose, and a record of what was changed.
What does “27% were wrong” establish?
As presented, it establishes only the author’s account: they removed 40% of their own rows and can prove that 27% of the removed rows were wrong. It does not establish how many rows the dataset contained, how “wrong” was defined, how the author verified the errors, or whether the remaining deleted rows were valid. The figures should not be treated as a measured error rate for data cleaning in general.
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There are at least three different outcomes among deleted records: a confirmed error, a valid observation removed by mistake, and a case that cannot now be resolved. “I cannot prove this row was wrong” is not the same as “this row was correct”; equally, “I cannot prove this row was correct” is not evidence that it was wrong. Keep those categories separate when describing the results.
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Start by defining what counts as a valid record for the analysis at hand. A value can be unusual and still be accurate. A record might be invalid under a schema rule, inconsistent with a source document, or impossible under a well-supported domain constraint. By contrast, a value that merely falls outside an expected pattern is a reason to investigate, not proof that the entire row should be deleted.
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The U.S. Government Accountability Office’s current guide to assessing data reliability frames reliability in relation to a specific purpose, including accuracy, completeness, and applicability. That means the same data may be adequate for one use and unsuitable for another. A decision rule should explain which purpose it serves and what evidence would make a record fail it.
Separate evidence states
- Confirmed error: Evidence shows the record violates a relevant rule or conflicts with a more authoritative source.
- Suspected anomaly: A check raises a concern, but the record has not been verified as wrong.
- Unresolved case: The available evidence is insufficient to decide, perhaps because the original source or context is missing.
- Valid but unusual: Review supports keeping the record, even though it is rare or unexpected.
This is a practical way to report decisions, not a formal classification imposed by the GAO. It prevents a flag from silently becoming a deletion verdict.
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Should I delete outliers from my dataset?
Not solely because they are outliers. First ask whether the value is a data-entry or measurement error, a valid but rare case, or an observation that falls outside the analysis’s intended population. Then consider what each choice does to the result: deleting a valid extreme value can distort the analysis, while retaining an erroneous value can distort it in a different direction.
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The trade-off resembles the distinction between false matches and missed matches in data linkage, but linkage terminology does not automatically apply to every cleaning task. The Office for National Statistics (ONS) discusses precision and recall for linkage and notes that its earlier accuracy formula was removed because it did not represent linkage quality well and was difficult to interpret. See the ONS guidance on record linkage for that specific context.
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For a linkage task, precision and recall help describe different error directions: precision concerns how many declared matches are genuine, while recall concerns how many genuine matches were found. For other cleaning decisions, choose measures that fit the error being assessed. No universal threshold or deletion rate follows from the linkage guidance.
How to clean data without losing the evidence
- State the purpose and validity criteria. Write down what the dataset will support and what makes a record usable for that purpose. Examples of checks include allowed ranges, duplicate rules, required fields, schema constraints, or linkage criteria; adapt them to the data rather than treating them as universal rules.
- Preserve the original. Keep a read-only source file or a recoverable version before transforming the data. The superseded GAO-03-273G guidance explains that deleting original files can leave reliability undetermined; it also states, “Reliability does not mean that computer-processed data are error-free.”
- Define checks before applying them. Record the rule, its scope, and the reason it is relevant. A rule such as “remove every value beyond this cutoff” needs a defensible basis; an unexplained cutoff is not evidence that each affected row is invalid.
- Flag first, then decide. Route suspicious records into the evidence categories above. Correct a value when a trustworthy source supports the correction; retain a valid unusual record; delete only when the evidence and purpose justify exclusion. Preserve unresolved cases as unresolved rather than forcing a binary answer.
- Review a meaningful sample—and ambiguous cases. Automated checks can handle routine detection, while targeted human review can examine cases that are uncertain or consequential. Statistics Netherlands describes automatic detection and correction alongside selective manual editing. The ONS likewise says clerical review is laborious as a primary linkage method but can help with ambiguous cases and checks on true positives. These are examples from their respective contexts, not a universal review threshold.
- Record the transformation. For each rule or decision, capture the source, rule and version, action taken, rationale, and review evidence. Preserve enough information for another analyst to reproduce the result and identify which records changed.
- Report both removals and uncertainty. State how many rows were removed under each rule, how many were checked, what was verified, and what cannot be determined. Do not label every unverified deletion an error—or treat the absence of proof as proof of correctness.
What if I deleted valid data?
If the original data and a traceable transformation history still exist, restore the affected records, correct the rule, rerun the cleaning steps, and compare the resulting analysis. Document the correction so the new output can be distinguished from earlier versions. If the original records are gone, the right conclusion may be that some decisions cannot be assessed; do not reconstruct certainty from memory or from the fact that a row was deleted.
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The older GAO-03-273G guide is superseded, so it should not be presented as current policy. Its point about preserving originals is still useful alongside the current GAO-20-283G reliability framework: without the source files or adequate documentation, a later reviewer may be unable to determine whether the transformed data are reliable.
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Check whether the rule disproportionately removes a particular range, group, source, time period, or type of case, and whether those records differ in ways relevant to the analysis. Compare the dataset before and after cleaning where possible, and examine how conclusions change under defensible alternatives—for example, retaining unresolved records, correcting only source-confirmed errors, or reporting results with and without a contested subset.
This does not prove that a cleaning choice is biased or unbiased on its own. It makes the effect visible and lets readers judge whether the exclusions fit the stated purpose. In clinical research, a review of data-processing studies found wide variation among methods and emphasized reporting the measured error rate, uncertainty, and measurement method. Those studies concern particular clinical research processes, not general-purpose row filtering, so they cannot supply a benchmark for the author’s percentages.
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