A powerhouse for scientific computing, SciPy extends its capabilities to statistical analysis with modules dedicated to hypothesis testing, descriptive statistics, and inferential statistics. Whether you're conducting t-tests, ANOVA, or correlation analysis, SciPy provides a comprehensive toolkit for statistical exploration.

Enhance post-hoc analysis with scikit-posthocs, a Python library expanding statistical capabilities through diverse post-hoc tests. Simplifying pairwise comparisons post-ANOVA or Kruskal-Wallis tests, it provides a user-friendly interface and varied statistical methods. Focused on revealing nuanced group differences, scikit-posthocs is a valuable asset for researchers extracting deeper insights

Specializing in estimating and testing statistical models, Statsmodels is a versatile library for regression analysis, time-series analysis, and hypothesis testing. With a focus on providing detailed statistical summaries, Statsmodels enables practitioners to delve deep into the nuances of their data.

Elevate your statistical analyses with Pingouin, a library designed for effortless and expressive statistical computations. From basic t-tests to advanced mixed-design ANOVA, Pingouin simplifies statistical testing while providing informative visualizations, making it an ideal choice for both beginners and experienced statisticians.

Expand your statistical toolkit with StatsPy, a library that complements traditional statistical analysis with a focus on user-friendly syntax and extensive functionality. StatsPy encompasses a range of statistical tests, from basic t-tests to advanced analyses like multivariate analysis of variance (MANOVA). With an emphasis on accessibility and clarity, StatsPy serves as a valuable addition to your statistical analysis endeavors, offering an array of tools to unravel the intricacies of your data.

Broaden your analytical horizons with Researchpy, a Python library crafted for descriptive and inferential statistics. With an emphasis on simplifying statistical analysis and reporting, Researchpy offers an intuitive interface for tasks such as summary statistics, correlation analysis, and effect size calculations. Whether you're exploring data patterns or conducting hypothesis tests, Researchpy is a versatile addition to your statistical toolkit, providing efficiency and clarity in your analytical endeavors.