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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesIf two analyses disagree, first check whether they used the same data, population, definitions, preparation steps, method, and software conditions. A mismatch can be a workflow error, a legitimate difference in assumptions, or small numerical variation; repeating the same run does not prove the result is correct. The way to find out is to compare the complete workflows, from source data to reported output.
Why don’t my data analysis results match?
Results can differ even when they appear to come from “the same data.” One analysis may use an updated file, a different filtered population, or a changed variable definition. Another may treat missing values or outliers differently, apply weights, or use a different model assumption. Code, software versions, dependencies, and run order can also change the result.
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Start by checking whether the two outputs answer the same question. Confirm the statistic or estimate, units, rounding, population, and time period. A number that looks inconsistent may instead be a different measure, a different subset, or a more precisely rounded version of the same estimate.
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- Different data or population: source-file versions, extracts, filters, joins, duplicate handling, inclusion rules, or date boundaries differ.
- Different preparation: recoding, unit conversion, missing-value rules, outlier treatment, transformations, weights, or manual spreadsheet edits differ.
- Different statistical choices: model specification, assumptions, sample design, clustering, or uncertainty calculations differ.
- Different implementation: a bug, stale script, wrong variable, file-path problem, dependency change, software release, or altered run order affects the computation.
- Run-to-run instability: a random routine lacks a controlled seed, or order-sensitive processing uses unstable sorting or non-unique identifiers.
- Numerical variation: an approximate or high-performance algorithm may produce slightly different values. Whether that variation is acceptable depends on the method, uncertainty, and a justified tolerance for the particular analysis.
How do I check which analysis is correct?
Use a traceable comparison rather than choosing the output that matches an earlier report. Record what is being compared, then work backward from the reported result through the method, preparation, and source data. The U.S. Census Bureau’s statistical quality guidance calls for appropriate data and assumptions and verification of computation accuracy; its standards are a useful checklist, though they are institutional requirements for Census Bureau work rather than rules binding every analyst.
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- Freeze the comparison. Save both outputs and record the analysis version and run date. Write down the exact estimate or statistic, unit, rounding, population, and time period so you are not comparing unlike quantities.
- Verify the source data and population. Compare the original file or extract, data release, filters, joins, duplicate handling, inclusion and exclusion rules, and date boundaries. Document any access restrictions. The World Bank’s Reproducible Research Repository FAQs recommend documenting datasets and beginning with the source data.
- Compare data preparation. Check cleaning, recoding, units, missing values, outlier rules, transformations, weights, and any hand-edited spreadsheet, table, or figure. Manual changes that are not recorded can break the link between code and the number that appears in a report.
- Compare the analysis method. Review the equations, variable definitions, estimand, model specification, assumptions, sample design, weights, clustering, and treatment of uncertainty. Confirm that both methods address the same question and target population.
- Compare the code and environment. Check script versions, variable references, file paths, dependencies, language and software versions, and execution order. For random or order-sensitive steps, inspect seed handling, sort stability, and whether sort keys uniquely identify records.
- Rerun the full workflow. Starting from the recorded source inputs, run the complete scripted process and compare intermediate tables and figures as well as final results. Confirm that generated outputs match the version used in the report or manuscript. Inspect relevant diagnostics or residual plots, and use robustness checks or sensitivity analysis to see whether defensible alternative choices materially change the result.
- Evaluate any remaining difference. If the inputs and steps match but outputs vary slightly, identify whether the algorithm is approximate or stochastic. Judge the difference against the method’s numerical behavior and the analysis’s uncertainty; do not call it harmless without a reasoned tolerance.
Why do I get different results from the same data?
“Same data” does not guarantee the same analysis. The file may be identical while the selected rows, definitions, preparation, or statistical choices differ. A reproducibility check therefore needs more than the input file: it needs the methods, computational steps, code, and conditions of analysis.
The National Academies distinguishes computational reproducibility—consistent computational results using the same input data, methods, steps, code, and analysis conditions—from replicability, which asks whether studies using newly collected data reach consistent results while addressing the same scientific question. The distinction matters: rerunning one workflow tests whether that computation can be repeated, not whether a new sample or study will reach the same conclusion. See the National Academies’ report on reproducibility and replicability.
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Why do my numbers change when I rerun the analysis?
First check whether the workflow includes random or order-sensitive operations. A random routine may produce different results between runs if its seed or random-state handling is not controlled. Results can also depend on record order when sorting is unstable, keys are not unique, or operations process data in sequence. Check execution order as well as the code itself.
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How should I choose between two plausible results?
Do not select a method simply because it reproduces a previously published number. Compare whether each analysis uses the same data vintage and target population, estimates the same quantity, and handles assumptions, sample design, missing data, and uncertainty appropriately. Then assess whether the result is documented, independently rerunnable where access permits, and stable under defensible robustness and sensitivity checks.
Reproducibility is evidence about the consistency of a computational process, not proof that the process is sound. The same coding error can be repeated perfectly. Review the design and method as well as the run: the Census Bureau’s Statistical Quality Standard E1: Analyzing Data emphasizes checking assumptions, data, and computation accuracy.
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What if the data cannot be shared?
Confidential or proprietary data can prevent other people from publicly rerunning an analysis. That does not make review impossible: document the data provenance, permitted access, transformations, methods, and software conditions, and arrange appropriate expert review or robustness checks where feasible. The Census Bureau’s transparency and reproducibility guidance recognizes that confidential data can limit public replication while still supporting transparency about methods.
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