Open AccessCommon pitfalls in statistical analysis: The perils of multiple testing
Author(s) -
Priya Ranganathan,
C S Pramesh,
Marc Buyse
Publication year - 2016
Publication title -
perspectives in clinical research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.649
H-Index - 8
eISSN - 2229-5488
pISSN - 2229-3485
DOI - 10.4103/2229-3485.179436
Subject(s) - multiple comparisons problem , statistical hypothesis testing , computer science , statistical analysis , statistics , significance testing , econometrics , data mining , medicine , mathematics
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.