statistical-analysis

Choosing the right statistical test for group comparisons involves evaluating your study design and data characteristics. This Skill provides guidance on selecting appropriate tests like t-tests, ANOVA, and regression based on your specific research framework.

To report statistical analysis results in APA format, you need structured templates matching journal standards. This Skill provides APA-style reporting templates alongside your analysis plan to ensure publication-ready results across social, medical, engineering, and humanities studies.

You can run hypothesis testing and effect size estimation using Python with this Skill. It offers ready-to-use example code utilizing libraries like scipy, statsmodels, and pingouin to execute t-tests, ANOVA, and regression analyses.

Yes, the statistical analysis workflow includes normality and homogeneity checks before running tests. It assists researchers in planning and executing complete analyses, ensuring assumptions are verified prior to applying hypothesis testing or regression models.

The best way to integrate statistical analysis planning into the manuscript writing process is using a structured workflow. This Skill provides quick-start workflows that generate analysis plans and reporting templates directly aligned with academic paper drafting.

Using pingouin and scipy for academic statistical analysis provides specialized functions for effect size estimation and APA-compliant outputs. This Skill leverages these libraries to deliver ready-to-use Python code tailored for rigorous, reproducible research reporting.