In this episode of The Effective Statistician, I sit down with Kaspar Rufibach to tackle a topic that affects statisticians every day—how to interpret p-values, confidence intervals, and statistical hypotheses.

We explore the differences between Fisher’s and Neyman-Pearson’s approaches, clear up common misconceptions, and discuss how misinterpreting statistical significance can lead to flawed conclusions.

Using real-world examples from clinical trials and drug development, we highlight best practices for communicating statistical results effectively.

Whether you're working with clinicians or business stakeholders, this episode will help you gain clarity on these fundamental statistical concepts and use them correctly in your daily work.