This project demonstrates how small sample sizes in A/B testing can lead to inconclusive results and how adjusting sample sizes through power analysis reveals statistically significant effects. Simulated data is used to compare p-values, conversion rates, and statistical power before and after sample size adjustment. Visualizations include ggstatsplot, ggpubr, ggsignif, and ggpmisc.