Common pitfalls in statistical analysis: The perils of multiple testing

Abstract

Multiple testing refers to situations where a dataset is subjected to statistical testing multiple times - either at multiple time-points or through multiple subgroups or for multiple end-points. This amplifies the probability of a false-positive finding. In this article, we look at the consequences of multiple testing and explore various methods to deal with this issue.

Keywords

• Biostatistics
• data interpretation
• multiplicity
• statistical significance