statistical-analysis

To generate APA-style statistical reports, input your dataset and specify analysis goals to produce a reproducible package containing descriptive statistics, effect sizes, diagnostic plots, and formatted results tables ready for publication.

Automated test selection evaluates your data types and experimental design to recommend appropriate statistical tests, including t-tests, ANOVA, regression, correlation, and Bayesian options, ensuring the chosen method aligns with your data structure.

Yes, you can run automated assumption checks for normality, homogeneity, and linearity before test selection. The process generates diagnostic visuals to guide your method choice and verify data suitability for parametric testing.

The best way to calculate effect sizes and perform power analysis is using a guided statistical framework that estimates effect sizes alongside APA-style reporting, providing robust planning metrics for publication-ready research outcomes.

Yes, you need pandas, numpy, scipy, matplotlib, and seaborn as dependencies to perform statistical analysis, as these libraries handle data manipulation, scientific computations, and generation of diagnostic visualizations within the workflow.

You should not use automated statistical analysis when your dataset requires custom non-standard modeling outside common designs, or when your research demands manual intervention for complex covariate interactions that predefined test selection cannot adequately capture.