Understanding Multiplicity Issues in Statistical Analysis

It is not uncommon for researchers to try to use the same dataset to conduct more than one inferential statistical analysis. For example, sometimes researchers want to conduct hypothesis testing with outcomes that are different from the originally planned main effect, or with a similar outcome but across different subgroups, using the same significance level for all tests. A significant finding could just be a product of an increased rate of spurious statistical significance or false-positive rate. This article introduces the topic of multiplicity, including definitions, examples, and implications for research studies, and offers potential solutions. It is intended primarily for nursing researchers and other health care professionals, students, and people conducting research based on statistical hypothesis analysis. It can also be used as an introductory guide for nurses who would like a basic understanding of multiplicity issues in studies based on inferential statistical analysis.