• Corpus ID: 222141721

Dealing with multiple testing: To adjust or not to adjust

@article{Pawitan2020DealingWM,
  title={Dealing with multiple testing: To adjust or not to adjust},
  author={Yudi Pawitan and Arvid Sjolander},
  journal={arXiv: Other Statistics},
  year={2020}
}
Multiple testing problems arise naturally in scientific studies because of the need to capture or convey more information with more variables. The literature is enormous, but the emphasis is primarily methodological, providing numerous methods with their mathematical justification and practical implementation. Our aim is to highlight the logical issues involved in the application of multiple testing adjustment. 
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SHOWING 1-7 OF 7 REFERENCES
No Adjustments Are Needed for Multiple Comparisons
  • K. Rothman
  • Computer Science, Medicine
    Epidemiology
  • 1990
TLDR
A policy of not making adjustments for multiple comparisons is preferable because it will lead to fewer errors of interpretation when the data under evaluation are not random numbers but actual observations on nature.
Estimating the number of true discoveries in genome-wide association studies.
TLDR
This paper presents a practical model-free procedure to estimate the number of true discoveries as a function of thenumber of top-ranking SNPs together with the confidence bounds, which allows a practical methodology of general utility and produces relevant statistical quantities with simple interpretation.
Most Reported Genetic Associations With General Intelligence Are Probably False Positives
TLDR
The molecular genetics of psychology and social science requires approaches that go beyond the examination of candidate genes, and SNP-based calculations of genetic relatedness are used to replicate previous estimates that about half of the variance in g is accounted for by common genetic variation among individuals.
A comprehensive review of genetic association studies
TLDR
It is found that over 600 positive associations between common gene variants and disease have been reported; these associations, if correct, would have tremendous importance for the prevention, prediction, and treatment of most common diseases.
In All Likelihood
  • R. Sauter
  • Computer Science, Mathematics
    Technometrics
  • 2002
TLDR
This book discusses using the likelihood function for both modeling and inference, providing a nice introduction to a variety of topics and can serve as a good initial exposure to possibly new concepts without overwhelming them with details.
Multiple testing for exploratory research, with discussion
  • Statistical Science, 26 (4), 584-597.
  • 2011
The Relevance of Stopping Rules in Statistical Inference