Hypothesize
Hypothesize: A focused Python toolkit for group comparisons, associations, and bootstrap inference. Ranked #54 of 106 in Statistical Analysis Software by our editors (4.0/10); pricing: Free plan; best for python users needing group-comparison tests.
At a glance
- Editor score4.0 / 10
- PricingFree plan
- Best forPython users needing group-comparison tests
- Free planYes
- Paid fromFree
- DeploymentDesktop
- Founded2020
- Facts checked23 Sep 2026
Where it wins
- Single- and two-factor group comparison procedures
- Bootstrap confidence intervals, tests, and family-wise error control
- Works with Pandas Series and DataFrames
Where it doesn't
- Focused scope rather than broad statistical coverage
- No documented regression-model support
- Not a standalone graphical application
Our verdict on Hypothesize
Hypothesize is a BSD-licensed Python library for statistical analysis, aimed at people who work in Python rather than those seeking a standalone graphical application. It supports comparisons across single-factor and two-factor designs, association measures, bootstrap procedures, and several estimator choices. Independent, dependent, and mixed groups are covered, with automatic linear contrasts and methods for controlling family-wise error. The project is vendor-maintained by Allan Campopiano and was founded in 2020.
The package is open source and has a free plan, with installation through pip. Its data workflow centers on Pandas Series and DataFrames; CSV files can be handled through Pandas. Published platform coverage includes Linux, Windows, and macOS, while deployment is self-hosted. For analysts already working in Python, the combination of scripting support, example-data generation, and procedures for trimmed means and statistical associations keeps analysis inside an existing code-based workflow. There are no listed AI features, and support is provided through the community.
Hypothesize is strongest when the question centers on group comparisons, associations, contrasts, or bootstrap inference. Its documented procedure set is more focused than a broad statistical suite, and the category specifications do not list regression models. That makes it a practical choice for Python users who need these specific methods and can manage a package-based workflow. Teams looking for a graphical application, wider model coverage, or vendor-provided support should consider a different statistical product. Solo users through enterprise teams can deploy it themselves, but the appropriate fit depends on comfort with Python and Pandas.
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Hypothesize pricing
Hypothesize fact sheet
| Free plan | Yes |
|---|---|
| Paid from | Free |
| Deployment | Desktop |
| Statistical tests | Yes |
| Regression models | No |
| Scripting support | Yes |
| Bayesian analysis | Not verified |
| Data import formats | Pandas DataFrames or Series; CSV via Pandas |
| Deployment | Self-hosted |
| Platforms | Linux, Windows, macOS |
| Support | Community |
| Built for | Solo, Small business, Mid-market, Enterprise (editorial estimate) |
| Pricing | Free plan |
| Website | alcampopiano.github.io |
| Facts checked | 23 Sep 2026 |
Alternatives to Hypothesize
- RA free, programmable environment for statistical analysis, modeling, visualization, and data import.9.4
- StataA broad, scriptable desktop environment for professional and reproducible applied analysis.9.3
- IBM SPSS StatisticsA broad desktop statistics suite with testing, modeling, forecasting, reporting, and R/Python integration.9.2
See all Hypothesize alternatives →
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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
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