data-stats-analysis

Run hypothesis testing locally by supplying your dataset to execute standard statistical tests using SciPy and StatsModels, which outputs test statistics, p-values, and confidence intervals for your analyses.

Perform multiple testing corrections by applying established library functions to your p-values, generating FDR-adjusted q-values to control false discovery rates across varying dataset sizes.

Yes, you can use this approach for non-parametric tests including Shapiro, Mann-Whitney, and Kruskal-Wallis, providing practical interpretation guidance alongside the standard test outputs.

Yes, this approach supports two-sample t-tests and ANOVA for group comparisons, executing analyses locally to deliver test statistics, confidence intervals, and adjusted q-values.

You need a Python environment with SciPy and StatsModels installed; no other external dependencies are required to execute the statistical tests and corrections locally.