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If two analyses produce different numbers, first check whether they use the same data, population, definitions, preparation steps, method, and software environment. Then trace both results from their source inputs through the full workflow. A mismatch may be a fixable error, a defensible difference in assumptions, or limited numerical variation—but a rerun that reproduces an answer does not prove that answer is correct.
Why don’t my data analysis results match?
Two outputs can disagree even when they appear to use the same data. A file may have been updated, a filter may differ, or one workflow may treat missing values, weights, or outliers differently. Results can also diverge because of model assumptions, code changes, software dependencies, or random and order-sensitive computation.
Start by asking whether the two numbers are actually the same target: the same estimate or statistic, unit, rounding, population, and time period. A difference in any of these means the outputs are not directly comparable.
- Data or population: different data releases, extracts, date boundaries, filters, joins, duplicate handling, or inclusion rules.
- Preparation: different recodes, units, transformations, missing-value treatment, outlier rules, weights, or manual spreadsheet edits.
- Statistical choices: different estimands, model specifications, assumptions, sample-design treatment, or uncertainty calculations.
- Implementation: a coding error, wrong variable, stale script, changed file path, dependency, software release, or run order.
- Run-to-run instability: an unseeded random process, unstable sorting, non-unique sort keys, or order-dependent operations.
- Numerical approximation: some algorithms can yield small differences; whether those are acceptable depends on the method and the problem.
The U.S. Census Bureau’s Statistical Quality Standard E1 emphasizes appropriate data and assumptions and verification of computational accuracy. It is an institutional standard, but its checks are useful beyond Census Bureau work.
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How do I check which analysis is correct?
Use a controlled comparison rather than changing settings until the outputs agree. Record both results and trace each from its documented inputs to its final reported value. This sequence helps separate a workflow mistake from a legitimate methodological difference.
- Freeze the comparison. Save the exact outputs and note each analysis’ date or version. Identify the statistic, unit, rounding, target population, and time period being compared.
- Verify the inputs and population. Check the original file or extract, its release or version, filters, joins, duplicate handling, inclusion and exclusion rules, and date boundaries. Note access restrictions. The World Bank Reproducible Research Repository advises documenting source datasets and the workflow built from them.
- Compare preparation. Inspect cleaning, recoding, units, missing values, outlier treatment, transformations, weighting, and any manual edits to spreadsheets, tables, or figures. An undocumented edit can sever the link between the code and the reported result.
- Compare the method. Review equations, variables, model specification, assumptions, estimand, sample design, weights, clustering, and how uncertainty is calculated. Ask whether both analyses answer the same question using appropriate assumptions and design.
- Compare code and environment. Check scripts, variable references, file paths, dependencies, language and software versions, and execution order. For random or order-sensitive routines, check seed settings, stable sorting, and unique identifiers.
- Rerun the complete workflow. Start from the recorded source inputs and run the scripted steps through to the final outputs. Compare intermediate results as well as generated tables and figures against the manuscript or report; confirm they belong to the same analysis version. Inspect relevant diagnostics or residual plots.
- Test defensible alternatives. Use robustness checks and sensitivity analysis to see whether the finding depends on a particular choice, such as a missing-data rule or model assumption. Record the alternatives and their rationale rather than selecting a method simply because it matches a previous number.
- Assess what remains. If the inputs and steps match but a small discrepancy persists, determine whether the algorithm is approximate or stochastic and whether the difference falls within a justified tolerance for this analysis.
The Census Bureau’s Standard E1 calls for checking assumptions, sample design where relevant, and computational accuracy. Its guidance also supports robustness and sensitivity analyses. Those are useful checks, not a universal rule that every analysis must use identical specifications.
Why do I get different results from the same data?
“Same data” may mean the same named file, not the same observations or values. A refreshed extract, changed join, different date cutoff, or revised exclusion can change the analysis population without changing the filename. Preparation can also alter what a variable means: for example, one workflow may convert units or recode a category while another does not.
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Even with identical records, the analyses may answer different questions. A weighted estimate, a model with different covariates, or a different treatment of missing observations can produce different results without either computation being mistaken. Compare the estimand and assumptions—not just the output label—to establish whether the results are intended to match.
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesFinally, investigate implementation details. A variable reference that points to the wrong column, stale code, changed dependency, or undocumented edit may create a genuine error. The World Bank repository’s reproducibility guidance identifies version mismatches, incomplete environment information, manual changes, coding errors, and unstable code as practical obstacles to reproducing reported results.
Why do my numbers change when I rerun the analysis?
A changing result may indicate that the workflow is not deterministic. Randomized procedures can vary between runs when their random-number state is not controlled. Operations that rely on row order, unsorted merges, or non-unique sort keys can also behave inconsistently when the input order changes.
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Check whether the method includes random sampling, resampling, simulation, or another stochastic step; then verify the seed and whether it is set at the appropriate point in the workflow. Also inspect sorting and joins, and use unique identifiers where a stable order is required. Record software and dependency versions because changes in the environment can alter behavior.
A fixed seed can help make a stochastic computation repeatable, but it does not establish that the method is suitable or the result is accurate. Review the design and implementation as well as the run-to-run consistency.
When is a difference acceptable rather than an error?
Not every numeric discrepancy signals a bug. Approximate or high-performance numerical methods may produce slightly different results, and stochastic algorithms may vary by design. The acceptable range is specific to the method, data, and decision the analysis supports; there is no general tolerance to apply to every problem.
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Before treating a difference as harmless, understand the numerical method and the uncertainty relevant to the analysis. Establish a tolerance that is justified for the application, then check whether the observed variation fits it. If the discrepancy changes the interpretation or decision, it deserves further investigation even if it looks small.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should I choose between competing results?
Do not choose whichever number matches an earlier report. Compare the analyses on the features that determine whether they answer the same question and can be trusted:
- Do they use the same target population and data vintage?
- Do their estimands and assumptions address the same question?
- Do they handle sample design, missingness, and uncertainty appropriately?
- Is the full workflow documented and independently rerunnable, subject to any data-access restrictions?
- Does the result persist under reasonable robustness and sensitivity checks?
The National Academies distinguishes computational reproducibility—consistent computation using the same inputs, methods, steps, code, and analysis conditions—from replicability, which uses newly collected data to address the same scientific question. The distinction matters: rerunning one dataset checks a workflow, while a study using new data asks a different question about whether results hold across studies. See the National Academies’ Reproducibility and Replicability in Science.
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What reproducibility can—and cannot—show
A documented workflow makes it possible to check how a reported result was produced. But reproducibility is not proof of correctness: the same faulty code can consistently repeat a wrong answer. Data quality, study design, assumptions, and method appropriateness still require scrutiny.
Public rerunning is not always possible. Confidential or proprietary data may restrict access; reviewers can still examine documented methods, and expert review and robustness checks can provide evidence. The Census Bureau describes these limits in its Transparency and Reproducibility guidance. Where data cannot be shared, clear documentation should make the inputs, restrictions, methods, and checks understandable without implying that outsiders can recreate the analysis.
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