statistical-analysis

To generate APA-formatted statistical analysis reports, provide your dataset and specify the required tests to receive structured outputs including test selection, assumption checks, and publication-ready results templates.

Checking statistical assumptions involves verifying normality, homogeneity, and linearity across your data before executing tests. This guided workflow automatically evaluates these prerequisites to ensure valid frequentist and Bayesian analysis.

Selecting the right statistical test for t-tests, ANOVA, or chi-square data requires guided test selection logic that matches your variables and data structure, ensuring appropriate frequentist or Bayesian methods are applied.

Yes, you can run Bayesian methods and power analysis leveraging Python dependencies like scipy and pandas. The analysis workflow integrates these libraries to execute comprehensive statistical tests and interpret effect sizes.

Statistical analysis with Python supports effect size calculation and interpretation for manuscripts. It calculates effect sizes and generates APA-style reporting templates to streamline your manuscript preparation and reporting workflow.