Meta-analysis (MA) is the quantitative integration of empirical studies that address the same or similar issues. It provides overall estimates of effect size, and can thus guide practical application of research findings. It can also identify moderating variables, and thus contribute to theory-building and research planning. It overcomes many of the disadvantages of null hypothesis significance testing. MA is a highly valuable way to review and summarise a research literature, and is now widely used in medicine and the social sciences. It is scarcely mentioned, however, in introductory statistics textbooks. I argue that MA should appear in the introductory statistics course, and I explain how software that provides diagrams based on confidence intervals can make many of the key concepts of MA readily accessible to beginning students.
The CAUSE Research Group is supported in part by a member initiative grant from the American Statistical Association’s Section on Statistics and Data Science Education