Are common language effect sizes easier to understand than traditional effect sizes?

Abstract

Communicating the results of research to nonscientists presents many challenges. Among these challenges is communicating the effectiveness of an intervention in a way that people untrained in statistics can understand. Use of traditional effect size metrics (e.g., r, r(2)) has been criticized as being confusing to general audiences. In response, researchers have developed nontraditional effect size indicators (e.g., binomial effect size display, common language effect size indicator) with the goal of presenting information in a more understandable manner. The studies described here present the first empirical test of these claims of understandability. Results show that nontraditional effect size indicators are perceived as more understandable and useful than traditional indicators for communicating the effectiveness of an intervention. People also rated training programs as more effective and were willing to pay more for programs whose effectiveness was described using the nontraditional effect size metrics.