Statistical analysis and hypothesis testing on medical insurance data using t-tests, ANOVA, Mann-Whitney U, Chi-square, confidence intervals, effect sizes, correlation analysis, and multiple comparison corrections.

This project analyzes A/B testing data to evaluate the effectiveness of three marketing campaigns. By conducting pairwise comparisons using statistical tests with a 99% confidence level, it identifies the best-performing campaign, providing actionable insights to optimize marketing strategies and boost overall sales performance.

Automated one-sample t-test and Wilcoxon testing tool with assumption checks, transformation logic, APA reporting, and ggplot diagnostics. Built in R for data science, athlete monitoring, and research use.