GraphPad Prism alternatives: what to use alongside it, and how to check the statistics

Disclosure. Licklider makes SciGround, one of the options on this page. Options are grouped by the job they do rather than ranked, and each statement about a product names its source and the date we checked it. Statements about each application come from its own pages, and the worked check verifies the calculations in R and Python, not Prism’s output. Before you choose, try the applications yourself to see their screens and confirm what they offer.

Quick answer. If Prism already does your analyses and graphs, keep it: the Prism 11 guide documents Welch’s t-test as the default for unpaired data (older versions and files may store another choice, so check the options saved with your analysis), an assumptions checklist for each analysis, nonparametric, survival and mixed-effects analyses, and publication graphing. Add R or Python when you need free, scripted, rerunnable code; JASP when you want free menus with Bayesian versions; jamovi when you want free menus that show the R code — after checking that the analyses you need are in their menus. Whichever you use, report which test and options produced the number: in the constructed example below, the same data give p = 0.027 or p = 0.013 depending on one setting. If an AI assistant wrote your code, the three checks on the Welch guide tell you which test it ran.

Terms used below. Welch’s t-test: compares two group means without assuming equal spreads; Prism 11, R’s t.test() and this site use it by default; SciPy’s ttest_ind does not unless you pass equal_var=False. Unpaired t test (equal SDs): the classic Student’s t-test, which treats both groups as having the same spread. Lognormal Welch’s t test: Welch’s test on log-transformed values; it compares geometric means and reports a ratio. Geometric mean: the mean of the logged values converted back to the original scale (exp of the mean of the logs), suited to right-skewed concentrations. MCP (Model Context Protocol): the standard that lets an AI assistant connect to an outside tool; you do not need to understand it to use one. Independent groups: different animals in each group; paired: the same animal measured twice. Spread: how far values sit from their group mean, measured by the standard deviation (SD) or its square, the variance. Degrees of freedom (df): a number printed with a t-test; for two groups of 6, the equal-SDs test always prints 10 (n₁ + n₂ − 2), and Welch’s prints a smaller decimal estimated from the spreads. 95% CI: the range of differences (or ratios) compatible with the data. Two-sided: the test looks for a difference in either direction. ANOVA: the comparison of three or more group means. Nonparametric: tests based on ranks rather than the values themselves. Mixed-effects model: a regression that accounts for repeated measurements of the same animal. Bayesian: a second way of stating the evidence, as probabilities for the hypotheses rather than a p-value. Nonlinear curve fitting: fitting an S-shaped or other curve, for example to estimate an EC50.

What Prism does well

These come from GraphPad’s own pages, checked on 10 November 2026.

• A broad library of tests from menus. The features page lists t-tests, one-, two-, three-way and multifactor ANOVA, linear and nonlinear regression including dose-response curves, binary logistic regression, survival analysis and principal component analysis, plus “Kruskal-Wallis or Friedman nonparametric one-way ANOVA with Dunn’s post test” and “Fisher’s exact test or the chi-square test” (features).
• Nonlinear curve fitting, a core Prism job (EC50/IC50, growth curves). “Fit one of our 105 built-in equations, or enter your own.” “Global nonlinear regression – share parameters between data sets. Robust nonlinear regression.” It can “Compare models using extra sum-of-squares F test or AICc” (features).
• Guidance on choosing a test. “Each analysis includes a checklist to help you understand the required statistical assumptions and confirm you’ve selected an appropriate test” (features).
• Sensible defaults for unequal spreads. For unpaired data, Prism 11’s guide calls Welch’s t test (or lognormal Welch’s t test) “the default option”, and its one-way ANOVA can run “without assuming populations with equal standard deviations using Brown-Forsythe and Welch ANOVA, followed by appropriate comparisons tests (Games-Howell, Tamhane T2, Dunnett T3)” (unpaired t test guide; features).
• Designs that need dedicated handling. Repeated measures “even with missing data”, where “Prism will automatically fit a mixed effects model”, and nested tables “analyzed using nested t tests or nested one-way ANOVA” (features).
• Graphs that update with the analysis, and a file format in which “data are stored in CSV format and all analysis parameters and results are stored in standard JSON schemas”, so “every selected option for every analysis is readily available alongside every parameter and calculated analysis result” (features; Prism 11 User Guide, file format).

Why people look elsewhere: it is commercial (annual or monthly subscriptions or a perpetual licence, with academic and student pricing and a 30-day free trial, per the pricing and trial pages); some analyses need code or methods outside its menus; and some labs want every step as a script they can rerun and share.

Which tool for which job

You need to…	A good fit	Why (basis, checked 10 Nov 2026)
Analyse and graph by pointing and clicking, with guidance on the test	GraphPad Prism	Assumptions checklist per analysis; graphs update with the data (features page)
Many of the same analyses by menus, with no licence cost	JASP or jamovi, after checking that your analyses (for example dose-response curve fitting) are in their menus	Both free on the desktop (jamovi’s cloud Guest plan is free too); JASP adds Bayesian versions, jamovi shows the R code (their pages)
A script you can rerun, version and cite	R or Python	Free; every option is written in the code
Bayesian versions of standard tests from menus	JASP	“standard analysis procedures in both their classical and Bayesian form” (jasp-stats.org)
Check that a reported result matches its data	Rerun it in R or Python; or SciGround (our product) for its supported analyses — not Welch’s ANOVA, two-way ANOVA, mixed-effects models, survival or curve fitting	See the worked check and the SciGround section

The options, grouped by job

Each row states its source and the date we checked it. Nothing here comes from hands-on testing.

Option	Best for	Less suited for	Basis (checked 10 Nov 2026)
GraphPad Prism	Point-and-click analysis and publication graphs; assumption checklists; Welch’s t-test as the unpaired default in Prism 11; Welch and Brown–Forsythe ANOVA; mixed-effects models for repeated measures with missing values; survival and curve fitting	No-budget settings; analyses you want as a shareable code script	features; unpaired t test guide; pricing
JASP	Free point-and-click analysis in classical and Bayesian form; “Support for APA format (copy graphs and tables directly into Word)”	Methods outside its menus: check that the analyses you need, for example dose-response curve fitting, are in its menus or modules before switching	jasp-stats.org: “JASP is an open-source project supported by the University of Amsterdam”
jamovi	Point-and-click analysis that shows the R code, free on the desktop and on the cloud Guest plan; “70+ Library Modules”; runs in the browser or on the desktop	Methods outside its modules: check that the analyses you need, for example dose-response curve fitting, are in its modules before switching	jamovi.org: “a free and open statistical spreadsheet”; “See the exact R syntax behind every analysis as you click”; “Desktop is always free. Cloud’s Guest plan is free too”
R	Any analysis, scripted and rerunnable, including dose-response fitting with the drc package (“Dose-Response Analysis Using R”) or base R’s nls(); the reference implementation for most methods on this site	Readers who will not write code	r-project.org: “a free software environment for statistical computing and graphics”
Python (SciPy, statsmodels)	Analyses inside a wider data pipeline	Readers who will not write code; check defaults (SciPy’s ttest_ind assumes equal variances unless told otherwise)	Our reproducible check below, SciPy 1.18.1
SciGround (our product)	Checking that a result, from any software, matches its data and supports the claim, for its supported analyses, inside ChatGPT or Claude; a match does not prove the original software computed it the same way	Analyses outside its supported workflows (including Welch’s ANOVA, two-way ANOVA, mixed-effects models, survival and curve fitting); graphing; replacing a statistics package	Our capability pages: check an existing analysis, supported analyses and their limits

Worked check: one setting, two p-values

We ran the same constructed data (IL-6 concentrations, 6 independent mice per group, treated values far more spread out) through the three unpaired t test options that Prism’s guide describes. These are the calculations, run in SciPy and R rather than in Prism itself. If the same animal was measured twice, or if your values are technical replicates from fewer animals, none of these rows applies: use a paired test, or one value per animal.

Option (Prism’s name)	What it assumes	Result: estimate (95% CI), t(df), p
Welch’s t test (Prism 11’s default)	Spreads may differ	Difference 21.2 pg/mL (95% CI 3.6 to 38.8), t(5.32) = 3.04, p = 0.027
Unpaired t test (equal SDs)	Equal spreads (SD 3.0 vs 16.8 here)	Same difference (95% CI 5.7 to 36.7), t(10) = 3.04, p = 0.013
Lognormal Welch’s t test (only if planned for a ratio question)	Spreads may differ on the log scale	Ratio of geometric means 2.38 (95% CI 1.44 to 3.94), t(7.07) = 4.07, p = 0.0046 — a different question (“how many times higher?”), not a better p for the same one

With spreads this unequal, the equal-SD version gives a smaller p-value because it uses 10 degrees of freedom instead of 5.3, as if the noisy group were as reliable as the quiet one (with 6 per group the t value itself is the same, 3.04). Choose the row before you see the results: Prism’s guide suggests normal when the difference between groups is of interest and lognormal when the ratio is, so plan the lognormal version for a right-skewed concentration whose question is “how many times higher?”; otherwise use Welch’s t-test. Do not pick whichever row gives the smallest p after seeing the results; report the row you planned (Welch’s t-test vs Student’s t-test).

# Install once: pip install numpy scipy   (replace the two arrays with your own values, one per animal)
import numpy as np
from scipy import stats

control = np.array([12.1, 15.4, 9.8, 18.2, 11.6, 14.0])
treated = np.array([21.5, 48.3, 17.9, 33.0, 26.4, 61.2])
w = stats.ttest_ind(treated, control, equal_var=False)         # Welch's t test
print(w, treated.mean() - control.mean(), w.confidence_interval())
s = stats.ttest_ind(treated, control)                          # SciPy default: equal SDs
print(s, s.confidence_interval())
lw = stats.ttest_ind(np.log(treated), np.log(control), equal_var=False)   # lognormal Welch
ci = lw.confidence_interval()
print(lw, "ratio", np.exp(np.log(treated).mean() - np.log(control).mean()), "CI", np.exp([ci.low, ci.high]))

# Base R only; replace the two vectors with your own values, one per animal
control <- c(12.1, 15.4, 9.8, 18.2, 11.6, 14.0)
treated <- c(21.5, 48.3, 17.9, 33.0, 26.4, 61.2)
t.test(treated, control)                     # R default: Welch
t.test(treated, control, var.equal = TRUE)   # equal SDs
lw <- t.test(log(treated), log(control))     # lognormal Welch
ratio <- exp(mean(log(treated)) - mean(log(control)))   # ratio of geometric means
ci <- exp(lw$conf.int)                                  # its 95% CI
c(ratio = ratio, lower = ci[1], upper = ci[2])

Both print the t, df, p and 95% CI in the table (the difference, 21.2, is the treated mean minus the control mean, which both print): Welch t = 3.04 on 5.32 df, p = 0.027, difference 21.2 (3.6 to 38.8); equal SDs t = 3.04 on 10 df, p = 0.013 (5.7 to 36.7); lognormal Welch t = 4.07 on 7.07 df, p = 0.0046, ratio 2.38 (1.44 to 3.94).

What to do: whichever package you use, write the exact test name and options in Methods, so a reader or a reviewer can reproduce the number — for example: “IL-6 was compared between groups (n = 6 mice each) with Welch’s t test, two-sided (GraphPad Prism [version]); we report the difference in means with its 95% CI.” In Prism 11, the choices are made on the Experimental Design tab of the t test dialog (design, distribution and which test), and the Prism 10 and later file format stores “every selected option for every analysis” with the results, so you can read back which test produced the number. If you only have a p-value from an assistant, ask for t, df and n: with 6 per group, df = 10 means the equal-SDs test and a decimal df means Welch’s.

Where SciGround fits, and where it stops

SciGround is a checking tool rather than a statistics or graphing package; use it alongside Prism, R or any other tool when you want a result checked. It connects to ChatGPT (web) and Claude (web and desktop) through MCP, with access details issued to you (use with your AI).

• Check an existing analysis. Bring the data, the analysis settings and the reported results; SciGround runs the supported analysis on the data and classifies each reported value as a match, a mismatch or not comparable, using rounding and tolerance rules fixed in advance (check an existing analysis).
• Its limits for this job. Agreement with today’s recomputation does not prove that the original software produced the reported values in the same way; data are not extracted from arbitrary PDFs or images; and past analyses are recomputed only within the supported workflows (existing analyses: limits).
• Which analyses. Two independent groups (Student’s t-test, or Welch’s t-test with its approximation shown separately), before and after with a paired t-test, three or more groups by a one-way ANOVA assuming equal variances, a two-group Mann–Whitney comparison, several planned outcomes, bulk RNA-seq and a few other designs (supported analyses). Not covered: repeated measures beyond two time points, multi-factor designs and interactions, covariate adjustment, general mixed models, single-cell data and a general catalogue of nonparametric tests (limits). That leaves out Prism’s Welch and Brown–Forsythe ANOVA, two-way ANOVA, mixed-effects models, Kruskal–Wallis, survival and curve fitting; for those, rerun the analysis in R or Python to check it.

When to choose another tool instead

• Keep Prism when it already covers your analyses and you value guided menus and publication graphs; most readers of this page are in this position.
• Choose JASP or jamovi when you want menus without a licence cost; JASP for Bayesian versions and APA tables, jamovi to see and keep the R code. Check first that the analyses you need, for example dose-response curve fitting, are in its menus or modules.
• Choose R or Python when you need a rerunnable script, a method outside the menus, or analyses inside a larger pipeline.
• Choose SciGround when you work in ChatGPT or Claude and want a supported result checked against its data and the claim it supports; keep Prism or R for the analyses and graphs SciGround does not cover.

How we compared

• Sources: each product’s own public pages and guides, checked on 10 November 2026 (GraphPad features, pricing and trial pages; Prism 11 Statistics Guide and User Guide; jasp-stats.org; jamovi.org; r-project.org).
• Our reproducible check: the calculations Prism’s guide describes for its unpaired-t options, run in SciPy 1.18.1 and R 4.3.3 with stored code. It tests the methods themselves rather than Prism.
• No hands-on testing of Prism, JASP, jamovi or any other product was done.
• Conflict of interest: Licklider makes SciGround.

Check any statement against its linked source and date.

Questions

Is there a free alternative to GraphPad Prism? JASP and jamovi (on the desktop, and jamovi’s cloud Guest plan) are free point-and-click packages, and R and Python are free if you will write code. Each differs from Prism’s graphing and menus: check first that the analyses you need, for example dose-response curve fitting, are in its menus, then try it on a dataset you have already analysed and compare the numbers and options.

Does Prism use Welch’s t-test by default? Prism 11’s Statistics Guide lists Welch’s t test (or lognormal Welch’s t test) as the default option for unpaired data. Older files or versions may store a different choice, so read the options saved with the analysis (the Experimental Design tab of the t test dialog).

Can SciGround check an analysis I ran in Prism? For its supported analyses, yes: bring the data, the settings and the reported results, and it recomputes the analysis and classifies each reported value as a match, a mismatch or not comparable. A match does not prove that Prism computed the value the same way, and analyses outside its workflows, such as two-way ANOVA or survival, are not covered.

References

• GraphPad Prism features. graphpad.com/features (checked 10 November 2026)
• GraphPad Prism 11 Statistics Guide. How to: Unpaired t test and Lognormal t test from raw data. graphpad.com (checked 10 November 2026)
• GraphPad Prism 11 Statistics Guide. Interpreting results: Welch and Brown-Forsythe tests. graphpad.com (checked 10 November 2026)
• GraphPad Prism 11 User Guide. The PRISM file format. academy.graphpad.com (checked 10 November 2026)
• GraphPad pricing and free trial. graphpad.com/how-to-buy; graphpad.com/free-trial (checked 10 November 2026)
• JASP Team. JASP. jasp-stats.org (checked 10 November 2026)
• The jamovi project. jamovi. jamovi.org (checked 10 November 2026)
• Ritz C et al. drc: Dose-Response Analysis Using R, version 4.0-0. CRAN (checked 10 November 2026)
• R Core Team. The R Project for Statistical Computing. r-project.org (checked 10 November 2026)

Changes

• 11 November 2026: first published.

GraphPad Prism, JASP, jamovi, ChatGPT, Claude and Python are names of their owners’ products. Licklider has no affiliation with GraphPad, the JASP team, the jamovi project, OpenAI, Anthropic or the Python Software Foundation.