Medical students must understand statistical reasoning and sample size selection to design and interpret clinical trials. Beyond achieving sufficient statistical power, ensuring meaningful precision in treatment effect estimates is equally important. We developed free, interactive Shiny/R tools that let learners explore how varying sample sizes influence both power and precision in common study designs. As an example, we discuss a Shiny/R app that explores a two-group design with a binary outcome, allowing sample size computation, effect estimation, and simulation of experiments in different scenarios. By engaging with these simulations, students gain a practical grasp of clinical trial design, ultimately enhancing their research skills and improving patient care.