Compared with GraphPad
We built the alternative, so treat this as an interested party's account rather than a neutral one. What follows is our honest read on where each tool fits — including the cases where GraphPad is the better answer.
GraphPad is two different products
Most confusion here starts with the name covering two very different things. QuickCalcs is a set of free web calculators — no account, no install, one answer at a time. Prism is commercial desktop software for analysing and plotting real datasets, widely used in the life sciences and priced accordingly.
This site is an alternative to the first, not the second. If your work involves a dataset rather than a statistic, a package like Prism does something no web calculator can: it keeps a record of what you ran, so the analysis can be checked and repeated.
Where the two differ
| QuickCalcs | This calculator | |
|---|---|---|
| Price | Free | Free |
| Account needed | No | No |
| Statistic → p-value | Yes | Yes |
| Raw data in | Some tools | t-test, A/B, χ², ANOVA, correlation |
| Effect size shown | Sometimes | Always, next to the p-value |
| Confidence interval | Sometimes | Always, where one is defined |
| Plain-English interpretation | No | With every result |
| Copy-ready APA string | No | Yes |
| Shaded distribution chart | No | Yes |
| Published accuracy validation | No | Against SciPy |
| Analysis you can save and repeat | No | No — use Prism or R |
The three things we deliberately do differently
Every result carries an interpretation. The evidence on p-value misuse is that computing one is easy and reading one is not. So each result here comes with a correct plain-English reading and the specific error that result invites — which differs between a significant finding and a null one. Most calculators, GraphPad's included, return the number and stop.
Effect size and interval get equal billing. Not tucked below the p-value, not optional. A p-value tells you an effect is detectable; only the interval tells you whether it is worth acting on, and putting them side by side is the clearest way to keep that distinction visible.
The tail arithmetic is done properly and published. Computing an upper tail as 1 − CDF(x) loses all precision far out: at z = 8 it returns exactly zero in double precision, and calculators that do it print p = 0.000, which is never a true p-value. Every distribution here exposes a separate survival function, so z = 8 correctly gives 6.22 × 10⁻¹⁶. The methodology page publishes the comparison against SciPy so the claim is checkable rather than asserted.
When GraphPad is the better choice
- You need a reproducible analysis. A saved Prism file records what you ran on what data. A web calculator records nothing, and for anything heading towards publication that difference matters more than any feature on this page.
- You need publication-quality figures. Prism's plotting is its real strength and nothing here competes with it.
- You need tests we do not have. Non-parametric tests, survival analysis, non-linear regression, repeated-measures designs — all outside this calculator's scope.
- Your field already runs on it. If your lab standardised on Prism, using the same tool as everyone reviewing your work has a value that is not statistical.
When this calculator is the better choice
- You have a statistic and want the p-value now. No install, no account, and the shaded curve shows you what the number represents.
- You are not certain how to read the result. The interpretation panel is the whole point of the site.
- You are writing it up. The APA string is generated to style and copies in one click.
- You are working in the far tail. Very small p-values stay exact here rather than collapsing to zero.
- You want to check another tool's answer. Two independent implementations agreeing is worth something, and the accuracy table makes it easy to see which one to trust when they do not.
Checking us against them
Run the same numbers through both. For ordinary values they will agree, and where a difference appears the methodology page shows exactly which algorithms are used here and how they compare to SciPy's reference implementations, value by value. We would rather you verified that than took our word for it — and if you find a disagreement we cannot explain, that is a bug we want to know about.
GraphPad is a good tool built by people who take statistics seriously, and the free QuickCalcs have helped an enormous number of researchers. This site exists because we think a calculator should also help you read the number it hands you — not because the alternatives are bad at arithmetic.
Frequently asked questions
Is GraphPad’s p-value calculator free?
The QuickCalcs tools on GraphPad’s site are free and need no account. Prism, their desktop statistics and graphing package, is commercial software with a free trial. The two are often confused because they share a brand — QuickCalcs is a set of small web calculators, Prism is a full analysis environment.
Should I use QuickCalcs or Prism?
If you need a single number from a statistic you already have, QuickCalcs or a calculator like this one is the right tool. If you are analysing a real dataset — multiple tests, figures for publication, a record of what you ran — that is what Prism is for, and neither web calculator substitutes for it. The work Prism does that a web page cannot is keeping your analysis reproducible.
Do the two calculators give the same answers?
For ordinary values, yes — both compute the same standard distributions and agree to the precision either displays. The differences show up in the far tail, where implementations that compute an upper tail as 1 − CDF lose all precision and start printing zero. This calculator evaluates tail areas directly for exactly that reason, and publishes its accuracy against SciPy on the methodology page.
Why does this calculator show an interpretation?
Because the research on p-value misuse is consistent that the number is easier to compute than to read. Every result here ships with a plain-English reading and the specific misinterpretation that result invites — a significant p-value and a null result invite opposite errors. That is an editorial choice, not a technical one, and it is the main thing that differs between the two tools.