Trace the real calculation
We identify the production formula, preprocessing rules, options, and output path used by the app.
Calculation confidence
Your results should be trustworthy—not a black box.
DataStatPro calculations are tested with fixed datasets and compared with independently executed results from established statistical software. We document what matched, what differs by convention, and what still needs more coverage.
Production calculationsThe same calculation code used in the app
Repeatable datasetsFixed normal, boundary, and missing-data cases
Full-precision checksCompared before display rounding
Documented limitationsCoverage gaps stay visible
How it works
Four checks between your data and the result you see
Verification follows the calculation from raw rows to the values displayed by DataStatPro. This helps catch problems in formulas, missing-data handling, statistical defaults, and result formatting.
Use known test data
Hand-checkable and challenging datasets test ordinary cases, ties, missing values, sparse cells, and boundaries.
Compare other software
The same data and aligned statistical method are run independently in Python, R, and SPSS where supported.
Fix and re-test
Material differences are investigated. Confirmed defects are corrected and protected with repeatable tests.
Current coverage
Calculations checked across DataStatPro
Verification is broader than publication tables. The current evidence also covers core descriptive, inferential, association, regression, and experimental-design calculations.
Cross-software verified
Descriptive statistics
VerifiedMeans, medians, variance, standard deviation, ranges, percentiles, quartiles, shape measures, valid counts, and missing-value handling.
- Python
- R
- SPSS
- 157 matched comparisons
Cross-tabs & categorical association
VerifiedCounts, expected counts, chi-square, likelihood ratio, Fisher exact, residuals, Cramer's V, contingency coefficient, Tschuprow T, and related measures.
- Python
- R
- SPSS
- 597 matched comparisons
t-tests & rank tests
VerifiedOne-sample, independent, Welch, and paired t-tests, plus Mann–Whitney and Wilcoxon calculations, alternatives, confidence intervals, and effect sizes.
- Python
- R
- SPSS
- 182 matched comparisons
ANOVA, repeated measures & ANCOVA
VerifiedOne-way and unbalanced two-way ANOVA, Type III tests, ANCOVA, repeated and mixed designs, sphericity, and Greenhouse–Geisser correction.
- Python
- R
- SPSS
- 155 matched comparisons
Correlation & association
VerifiedPearson, Spearman, Kendall tau-b, point-biserial, Phi, pairwise sample sizes, ties, exact small-sample behavior, and missingness.
- Python
- R
- SPSS
- 44 matched comparisons
Linear & logistic regression
VerifiedCoefficients, standard errors, inference, prediction intervals, logistic fit, classification, AUC, probabilities, coding, and missing-data paths.
- Python
- R
- SPSS
- 178 matched comparisons
Publication-ready tables
VerifiedTable 1 summaries, Table 2 group comparisons, Table 2a selection, Table 3 correlations, and comparative regression across app, dashboard, saved-analysis, and export paths.
- Python
- R
- SPSS
- Shared production runners
Design of experiments
VerifiedFactorial and fractional-factorial design structure, response-surface curvature, categorical contrasts, fitted-model diagnostics, safe variable names, and missing responses.
- Python
- R
- SPSS
- 50 matched comparisons
Meta-analysis & effect-size conversion
VerifiedFixed- and random-effects pooling, five heterogeneity estimators, Hartung–Knapp inference, prediction intervals, subgroup analysis, meta-regression, influence diagnostics, Mantel–Haenszel sensitivity, robust inference, and twelve effect-size conversion families.
- Python
- R
- SPSS
- 437 matched comparisons
- 17 production tests
Reading the evidence
What our verification labels mean
Cross-software verified
DataStatPro's production calculation agreed within the declared numerical tolerance for the specific methods, options, and fixed datasets tested.
Aligned statistical methods
Software packages can use different valid defaults—for example, quartile estimators or exact versus asymptotic p-values. Comparisons align the statistical method before results are evaluated.
Software-specific evidence
Each reference package contributes the equivalent outputs it supports, creating complementary evidence across Python, R, and SPSS.
Ongoing quality assurance
Repeatable fixtures and regression tests help keep covered calculations consistent as DataStatPro continues to evolve.
Built for transparency
Verification is an ongoing quality process
Audits have identified real issues in preprocessing, formula selection, missing-value handling, statistical conventions, and exports. The important result is not that differences never occur—it is that they are traced, corrected, re-run, and documented.
- Calculations are tested through the production path used by DataStatPro.
- Results are compared at full precision before display rounding.
- Reference software versions and unavailable procedures are recorded.
- Corrected defects receive focused regression tests.
Academic oversight
Statistical reviewers
DataStatPro is informed by academic expertise spanning biostatistics, epidemiology, mathematical statistics, probability modelling, survey sampling, data science, and applied research.
Our reviewers contribute specialist knowledge across statistical methodology, research practice, and applied analysis.
Founder & lead statistical reviewer
Prof. Dr. Nadeem Shafique Butt
Professor of Biostatistics · King Abdulaziz University
Founder of DataStatPro and an applied statistician with expertise in epidemiology, survival analysis, health data science, distribution theory, and medical statistics. He leads the platform's vision for guided, transparent, and publication-ready statistical analysis.
View LinkedIn profile ↗FounderDataStatPro
Applied statisticsResearch and teaching
Health data scienceBiostatistical methods
Academic leadershipGlobal collaboration
Prof. Dr. Asif Hanif
Professor of Biostatistics · Sakarya University and the University of Lahore
Specialist in medical statistics, applied epidemiology, data mining, machine learning, public-health research, and research-capacity development.
View LinkedIn profile ↗Prof. Dr. Muhammad Qaiser Shahbaz
Professor of Statistics · King Abdulaziz University
Expert in mathematical statistics, distribution theory, probability modelling, order statistics, survey sampling, and improved estimation methods.
View LinkedIn profile ↗Prof. Dr. Rehan Ahmad Khan Sherwani
Professor of Statistics · University of the Punjab
Specialist in biostatistics, data modelling, statistical process control, curriculum development, data science, and advanced computational methods.
Prof. Dr. Ahmad Azam Malik
Associate Professor of Epidemiology and Public Health · King Abdulaziz University
Medical researcher working across epidemiology, disease-burden modelling, population health, scientometrics, behavioural medicine, and community-health interventions.
View LinkedIn profile ↗Dr. Haitham M. Yousof
Assistant Professor of Statistics, Mathematics, and Insurance · Benha University
Mathematical statistician specializing in probability distributions, parametric and nonparametric regression, heavy-tailed models, and actuarial risk analysis.
View LinkedIn profile ↗Explore the analyses covered by DataStatPro
Choose a method, review its guidance, and run your analysis in the app.
Last evidence review: September 8, 2026. DataStatPro and DSRConsult LLC are independent of Python, R, IBM, SPSS, and their maintainers. Product names identify comparison environments and do not imply endorsement or independent certification.