In their essay, "Why Assessment Will Never Work...," Bacon and Stewart (2016) recommend that instead of carrying out the expensive process of experimenting themselves, many business schools would get a bigger bang for their buck if they used "published pedagogical studies that use direct measures of learning with sufficient statistical power" (p. 181) to improve instructional programs and student learning. This is because, according to Bacon and Stewart (2016), such studies are doomed to fail the inferential test of significance due to lack of statistical power--a consequence of small enrollments in programs and the unreliability of faculty-made measures of student achievement. In this rejoinder, Richard Shavelson takes issue with two points made by Bacon and Stewart in their essay: (1) that reliance on statistical significance is a necessary condition to learn and justify something about program impact on student learning; and (2) that the biggest problem programs face is "measuring post change outcomes to determine if the implemented changes produced the desired effect." Here Shavelson discusses these issues in greater depth.