Analyze grouped data with robust randomization logic. Review observed statistics, null summaries, and exact settings. Download clean outputs for audits, study notes, and sharing.
| Sample A | Sample B | Statistic | Alternative | Iterations |
|---|---|---|---|---|
| 12, 15, 14, 16, 13 | 9, 11, 10, 12, 8 | Difference in means | Two-sided | 5000 |
Observed statistic: T = statistic(Sample A) − statistic(Sample B)
Statistic choices: mean difference, median difference, or sum difference.
Two-sided p-value: p = count(|T*| ≥ |Tobs|) / total regroupings in exact mode.
Monte Carlo p-value: p = (count(extreme T*) + 1) / (B + 1)
One-sided logic: use T* ≥ Tobs for greater, or T* ≤ Tobs for less.
Cohen's d: d = (meanA − meanB) / pooled standard deviation.
A permutation test p-value calculator helps you compare two groups without assuming a normal distribution. It builds a null distribution by repeatedly shuffling labels across pooled observations. This randomization process estimates how unusual your observed difference looks under the null hypothesis. The tool is useful for small studies, skewed data, and robust nonparametric checking. It supports mean, median, and sum differences. It also reports group summaries, effect size, confidence limits, and a decision at your chosen significance level.
Permutation methods are flexible. They focus on the data you collected. They do not rely heavily on textbook distribution assumptions. That makes them valuable in experiments, A/B testing, education studies, clinical pilots, and quality control reviews. You can test whether group A tends to be larger than group B, smaller than group B, or simply different. It also supports automatic exact testing when reallocations stay manageable.
First, the calculator computes the observed statistic from both samples. Next, it pools every value into one combined list. Then it reassigns observations into new groups many times. Each reassignment keeps group sizes unchanged. For every reshuffle, the same statistic is recalculated. Those simulated values form the null distribution. The p-value is the share of null statistics that are at least as extreme as the observed result. Small p-values suggest stronger evidence against the null hypothesis.
The report shows observed difference, estimated p-value, null mean, null standard deviation, and central null interval. It also summarizes both groups with count, mean, median, sum, minimum, maximum, and standard deviation. These details help you interpret magnitude and uncertainty together. Export buttons let you save a spreadsheet style summary or a printable PDF report for review and practice. This makes the calculator practical for coursework, audits, and reproducible statistical reporting.
Exact mode reviews every valid regrouping and returns a precise result. Monte Carlo mode samples regroupings and runs faster on larger datasets. Increasing iterations lowers simulation noise and improves p-value stability across repeated checks for careful decisions.
It measures how often shuffled group assignments produce a statistic as extreme as your observed result under the null hypothesis.
Use it when data are small, skewed, unusual, or when you want a distribution-light comparison between two groups.
Exact mode checks every valid regrouping. Monte Carlo mode samples many regroupings and is faster when datasets become larger.
It stabilizes the estimated p-value and avoids returning zero when no sampled regrouping is more extreme than the observed statistic.
Yes. This calculator lets you compare differences in means, medians, and sums using the same randomization framework.
No. A p-value reflects evidence against the null. Effect size and group summaries help you judge practical importance.
A seed makes Monte Carlo results reproducible, which is helpful for audits, coursework, collaboration, and repeated reporting.
Yes. The page includes CSV and PDF download options for result sharing, printing, recordkeeping, and further review.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.