Powering Reproducible Research

The widely used null hypothesis significance testing (NHST) framework grew out of the distinct statistical theories of Fisher, and Neyman and Pearson. From Fisher, this chapter considers the concept of null hypothesis testing, and from Neyman-Pearson the concepts of Type I (α) and Type II error (β). Power is a concept arising from Neyman-Pearson theory, and reflects the likelihood of correctly rejecting the null hypothesis (i.e., 1 - β). However, the hybrid statistical theory typically used leans most heavily on Fisher's concept of null hypothesis testing. Within the hybrid NHST framework that continues to dominate within psychological science, the power of a statistical test is the probability that the test will correctly reject the null hypothesis when the null hypothesis is genuinely false. Low power may therefore contribute to the poor reproducibility of scientific findings, which continues to be a cause of concern.