A Free GraphPad Prism Alternative That Runs in Your Browser
Looking for a free GraphPad Prism alternative? Inference Stats runs statistical analysis in your browser with no install, keeps your data local, and is validated against R and SciPy.
Short answer: Inference Stats runs in a browser tab, needs no installation or licence, can keep your data on your own machine, and every statistical method in it is checked against an independent implementation — R, SciPy, statsmodels, lifelines or pingouin — to numerical precision. If you need common statistical tests, regression, survival analysis and publication-ready plots, it will do the job. If you need Prism's specific dose–response curve library or its exact figure styling, it will not.
That last sentence is the honest part, and the rest of this page is written the same way.
Why people go looking
The search usually starts in one of three places. A student loses access to a departmental licence. A clinician wants to check an audit result without asking IT to install anything. A researcher on a shared or locked-down machine cannot install desktop software at all.
All three have the same shape: the statistics are not exotic, but the software is in the way.
What running in a browser actually changes
No installation, no licence server, no admin rights. It opens like a web page, because it is one. That matters most on managed NHS, university and hospital machines, where installing anything is a ticket and a wait.
Your data can stay on your machine. Switch on Local-only mode and the analysis runs entirely in the browser — nothing is uploaded, and there is no server-side copy to worry about. This is usually the point at which an information-governance conversation becomes short, because there is no data transfer to assess.
It works the same on any operating system, since there is no build to match to your machine.
The trade-off is real: a browser tab has less memory than a desktop application. Very large datasets are better handled by R or Python. This tool is built for the sizes that clinical audit, laboratory work and student projects actually produce.
The part most alternatives skip: is it correct?
Free statistics tools are easy to find. Free statistics tools that show their working are not.
Every method here is checked against an independent implementation on the same input, and the results must agree to numerical precision. Not "we used a well-known library" — each method is named on the validation page beside the specific implementation it is checked against, and the scripts that generated those reference values are in the repository. The full automated suite runs on every change, and a result cannot ship if it disagrees with its reference.
Two examples of what that means in practice:
- Survival analysis is checked against R's
survivalpackage and Python'slifelines, including the log-rank test and Cox proportional hazards. - Post-hoc tests are checked against SciPy, pingouin and scikit-posthocs — Tukey HSD, Games–Howell and Dunn's test each against the implementation that specialises in it.
What validation does not prove: that the method suits your data. Agreeing with R means the arithmetic is right. Choosing an inappropriate test and computing it perfectly still produces a wrong answer, which is why the next section exists.
The thing a spreadsheet-shaped tool cannot do
Most statistics software assumes you already know which test you want. That assumption is wrong often enough to be the main source of error in published analyses.
This tool asks about your design — what you measured, how many groups, whether the same subjects appear more than once — and routes you to a method, showing its reasoning. It tells you why not the alternatives, what the result will mean, and what it cannot conclude. It is a deterministic decision tree, not a chatbot: the same answers always produce the same recommendation, and it will say "we have not built that" rather than inventing something.
It also declines. Ask for a clustered binary outcome and it will tell you that needs a generalised linear mixed model, that this app does not have one, and what to do instead — rather than quietly fitting the wrong model.
What it covers
Comparisons (t-tests, ANOVA family, non-parametric equivalents, chi-square, exact tests, equivalence testing), regression (linear, logistic, ordinal, Poisson, negative binomial, penalised, mixed models, multiple imputation), survival analysis (Kaplan–Meier, log-rank, Cox, competing risks, restricted mean survival time), diagnostic accuracy (ROC, AUC with DeLong comparison), agreement and reliability (Bland–Altman, ICC, kappa, Cronbach's alpha), meta-analysis, clustering and dimension reduction, sample-size calculation, and a monitoring toolkit — control charts, CUSUM, risk-adjusted CUSUM, VLAD and funnel plots — that most general-purpose packages do not include at all.
What it does not cover
Being specific here is more useful than a feature grid:
- No dose–response curve fitting. Prism's non-linear regression library is its strongest feature and this does not attempt to replace it.
- No matching of Prism's figure styling. Plots here are publication-quality and export cleanly, but if a journal or a supervisor expects Prism-styled figures, they will look different.
- No scripting language. For programmable, reproducible pipelines over large data, use R or Python. Every analysis here does record what it ran, but that is a reproducibility log rather than a script you can edit.
- No generalised linear mixed models, and the app says so where it matters instead of substituting something close.
If any of those is central to your work, the honest answer is that this is not the tool.
Is it really free?
The free tier covers data handling, exploration, the guided analysis, group comparisons, graphing and sample-size calculation — enough for a large share of student and audit work. The advanced modules are paid. There is no trial that expires mid-project and no licence to renew.
Where to start
Open the guided analysis with your data and let it work out which test fits, or go straight to a method if you already know. If you want to check the claims on this page first, the validation page lists every method against the implementation it is tested against.
FAQ
Is Inference Stats a full replacement for GraphPad Prism? For common statistical tests, regression, survival analysis and standard plots, yes. For dose–response curve fitting and Prism-specific figure styling, no — those are Prism's strengths and this does not attempt to copy them.
Does my data get uploaded anywhere? Only if you choose. Local-only mode runs the entire analysis inside your browser with no upload and no server-side copy, which is usually what an information-governance review needs to hear.
How do I know the results are correct? Every method is checked against an independent implementation — R, SciPy, statsmodels, lifelines or pingouin — and must agree to numerical precision. The validation page names each method beside its reference, and the scripts that produced the reference values are in the repository.
Can I use it on a locked-down hospital or university computer? Yes. It runs in a browser tab with no installation and no admin rights, which is the usual blocker on managed machines.
What if I do not know which test to use? That is what the guided analysis is for. It asks about your study design and recommends a method, explains why not the alternatives, and states what the result cannot conclude.
Is it free for students? The free tier covers data handling, exploration, guided analysis, group comparisons, graphing and sample-size calculation. The advanced modules are paid, and there is no trial that expires part-way through a project.
Written by Dr Hoong Sern Lim MB ChB MD FRCP, Consultant Cardiologist, Queen Elizabeth Hospital Birmingham; Honorary Senior Lecturer, University of Birmingham. ORCID 0000-0002-6569-1805