The Problem of Statistical Power in MIS Research

Statistical power is a topic of importance to any researcher using statistical inference testing. Studies with low levels of statistical power usually result in inconclusive findings, even though the researcher may have expended much time and effort gathering the data for analysis. A survey of the statistical power of articles employing statistical inference testing published in leading MIS journals shows that their statistical power is, on average, substantially below accepted norms. The consequence of this low power is that MIS researchers typically have a 40 percent chance of not detecting the phenomenon under study, even though it, in fact, may exist. Fortunately, there are several techniques, beyond expanding the sample size (which often may be impossible), that researchers can use to improve the power of their studies. Some are as easy as using a different but more powerful statistical test, while others require developing more elaborate sampling plans or a more careful construction of the research design. Attention to the statistical power of a study is one key ingredient in assuring the success of the study. This article should serve as a useful guide for MIS researchers in the planning, execution, and interpretation of inferential statistical analyses.