A general introduction to adjustment for multiple comparisons.
By Shiyi Chen, Zhe Feng, Xiaolian Yi. Journal of Thoracic Disease, 2017
673 citations recorded by the source corpus.
In experimental research a scientific conclusion is always drawn from the statistical testing of hypothesis, in which an acceptable cutoff of probability, such as 0.05 or 0.01, is used for decision-making. However, the probability of committing false statistical inferences would considerably increase when more than one hypothesis is simultaneously tested (namely the multiple comparisons), which therefore requires proper adjustment. Although the adjustment for multiple comparisons is proposed to be mandatory in some journals, it still remains difficult to select a proper method suitable for the various experimental properties and study purposes, especially for researchers without good background in statistics. In the present paper, we provide a brief review on mathematical framework, general concepts and common methods of adjustment for multiple comparisons, which is expected to facilitate the understanding and selection of adjustment methods.
Subfields: Analytical Chemistry, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/06522e9bef14658b915c4254c2a91758d0178acd
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In Analytical Chemistry
In Statistics and Probability
Citations in the corpus
- What is the proper way to apply the multiple comparison test? (2018, methodology): “Another alternative to the Bonferroni correction to yield overly conservative results is to use the stepwise (sequential) method, for which the Bonferroni-Holm and Hochberg methods are suitable, which are less conservative than the Bonferroni test [5].”