Qlik Delivers Research-Grade Statistical Accuracy for T-Tests
When analytical decisions hinge on rigorous hypothesis testing, the tools you trust must meet the highest standards. This research paper demonstrates that Qlik analytics produces two-sample t-test results that align with R, the gold standard of statistical computing, across diverse scenarios, sample sizes, and variance assumptions. For organizations already using Qlik, this is evidence that your platform is ready for serious statistical work.
Published
Jun 11, 2026
14 • Pages
IPC Global
Technology
AUTHOR
Igor Alcantara, Alane Miguelis, Priscila Rubim, Mark Meersman
AUTHORED YEAR
2024
AUDIENCE
Data Scientists & Analytics Leaders
INDUSTRY
Technology (Data Analytics)
TOPIC
Qlik Statistical Accuracy, t-Test vs R
White Paper Snapshot
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Key Topics
Qlik Matches R Across 10 Real-World Statistical Tests
Validates platform trust at scale: Across five distinct two-sample t-tests, each run under both equal and unequal variance assumptions, Qlik's results aligned with R on every key statistic, including means, standard deviations, degrees of freedom, t-values, and confidence intervals.
Performs reliably at both ends of the sample-size spectrum: Tests ranged from large populations (11,000+ observations from the Framingham Heart Study) down to small filtered subgroups of 30 records, and Qlik held up in both conditions without degradation in accuracy.
Handles non-significant results with equal precision: Not just significant findings, Qlik also correctly computed large p-values and correctly supported failing to reject the null hypothesis, a critical capability for sound scientific practice.
T-value consistency provides a reliable fallback: When p-values at extreme precision diverge slightly between platforms, the t-statistic remained fully consistent in every test, giving analysts a dependable reference point against standard t-distribution tables.
Highlights
Understanding Qlik's P-Value Precision — and Why It Doesn't Matter
Precision differences are negligible for decision-making: Qlik and R diverge only at extreme decimal precision for very small p-values, far below any conventional significance threshold (0.1, 0.05, or 0.01), meaning the analytical conclusion is never affected.
Qlik offers greater display flexibility: Unlike R, which caps p-value display at 2.2e-16, Qlik allows researchers to configure decimal precision, giving them more visibility into highly significant results rather than a threshold flag.
Matches R up to 13 decimal places in standard scenarios: When the p-value falls within R's displayable range, Qlik aligns with R to an impressive degree of precision, confirmed in Test 5 where both platforms produced values matching to 13 significant figures.
Transparent methodology, reproducible results: All R and Qlik code is publicly available via GitHub, along with anonymized Framingham data, enabling any researcher to validate these findings independently.
Results & Impact
Validated Across 10 Hypothesis Tests
All seven statistical measures, including mean, standard deviation, t-value, degrees of freedom, and confidence intervals, matched between Qlik and R across every test scenario, confirming Qlik as a reliable platform for two-sample hypothesis testing.
Ready for Small and Large Datasets
Qlik demonstrated consistent accuracy whether analyzing over 11,000 observations or a narrow subgroup of 30 participants, making it a dependable choice regardless of dataset size or complexity.