statistical-analysis

Choosing the right statistical test requires evaluating your data type, distribution, and study design. This skill guides you through structured test selection, checking assumptions like normality and variance, and executing analyses to produce accurate, reproducible conclusions.

Yes, you can generate APA-formatted statistical reports directly from your pandas dataset. By providing your research question and data, the tool executes statistical testing and outputs APA-style results, complete with effect sizes and clear interpretation for academic research.

Checking statistical assumptions involves verifying normality, variance, independence, and linearity. This skill automates assumption checks with diagnostic visuals generated via matplotlib and seaborn, ensuring your data meets requirements before executing tests like ANOVA or regression.

Yes, the tool supports a priori power analysis for study planning. It calculates required sample sizes and helps you plan studies effectively, ensuring your research design has sufficient power before data collection begins.

The best way to run regression and correlation analyses with reproducible results is through a structured workflow. This skill uses numpy and scipy to execute tests, checks assumptions, and generates consistent, APA-ready outputs for academic publishing.

Bayesian alternatives should be used when traditional hypothesis testing assumptions are not met or when you need to quantify evidence more robustly. This skill performs both standard tests and Bayesian alternatives, reporting effect sizes and APA-style interpretations for either approach.