Understanding Statistical Power and Significance Testing: an interactive visualization

Abstract

Much has been said about significance testing – most of it negative. Methodologists constantly point out that researchers misinterpret p-values. Some say that it is at best a meaningless exercise and at worst an impediment to scientific discoveries. Consequently, I believe it is extremely important that students and researchers correctly interpret statistical tests. This visualization is meant as an aid for students when they are learning about statistical hypothesis testing. The visualization is based on a one-sample Z-test. You can vary the sample size, power, significance level and the effect size using the sliders to see how the sampling distributions change.