Statistical significance testing and cumulative knowledge in psychology: Implications for the training of researchers

Data analysis methods in psychology still emphasize statistical significance testing, despite numerous articles demonstrating its severe deficiencies. It is now possible to use meta-analysis to show that reliance on significance testing retards the development of cumulative knowledge. But reform of teaching and practice will also require that researchers learn that the benefits that they believe flow from use of significance testing are illusory. Teachers must revamp their courses to bring students to understand that (a) reliance on significance testing retards the growth of cumulative research knowledge; (b) benefits widely believed to flow from significance testing do not in fact exist; and (c) significance testing methods must be replaced with point estimates and confidence intervals in individual studies and with meta-analyses in the integration of multiple studies. This reform is essential to the future progress of cumulative knowledge in psychological research. In 1990, Aiken, West, Sechrest, and Reno pub-lished an important article surveying the teaching of quantitative methods in graduate psychology programs. They were concerned about what was not being taught or was being inadequately taught to future researchers and the harm this might cause to research progress in psychology. For example, they found that new and important quantitative methods such as causal modeling, confirmatory factor analysis, and meta-analysis were not being taught in the majority of graduate programs. This is indeed a legitimate cause for concern. But in this article, I am concerned about the opposite: An earlier version of this article was presented as the presidential address to the Division of Evaluation