statistical-analysis

To generate APA-style statistical reporting, you input your dependent variable, grouping variable(s), and research question. The Skill selects appropriate statistical tests, verifies assumptions, and outputs publication-ready summaries with test statistics, exact p-values, effect sizes, and confidence intervals.

Choosing the right statistical test involves matching your research question and data type to methods like t-tests, ANOVA, or chi-square. The process includes checking key assumptions such as normality and variance homogeneity to decide between parametric and non-parametric approaches.

Checking statistical assumptions requires performing normality testing, variance homogeneity checks, outlier detection, and regression diagnostics. These assumption diagnostics ensure your data meets the requirements for parametric testing before you run regression or correlation modeling.

Yes, you can calculate effect sizes and perform a priori power analysis for academic manuscripts. The Skill integrates these calculations into the study planning phase to ensure your hypothesis testing is properly powered and your results include structured statistical output.

Yes, Bayesian analysis alternatives are supported for hypothesis testing. Alongside traditional statistical testing, you can generate results that include credible intervals, providing a Bayesian perspective for your research datasets when preparing academic papers.