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p-Value Histograms: Inference and Diagnostics
1
Department of Biostatistics, University of Iowa, Iowa City, IA 52242, USA
2
Department of Statistics, University of Kentucky, Lexington, KY 40508, USA
*
Author to whom correspondence should be addressed.
High-Throughput 2018, 7(3), 23; https://doi.org/10.3390/ht7030023
Received: 30 July 2018
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Revised: 26 August 2018
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Accepted: 30 August 2018
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Published: 31 August 2018
It is increasingly common for experiments in biology and medicine to involve large numbers of hypothesis tests. A natural graphical method for visualizing these tests is to construct a histogram from the p-values of these tests. In this article, we examine the shapes, both regular and irregular, that these histograms can take on, as well as present simple inferential procedures that help to interpret the shapes in terms of diagnosing potential problems with the experiment. We examine potential causes of these problems in detail, and discuss potential remedies. Throughout, examples of irregular-looking p-value histograms are provided and based on case studies involving real biological experiments.
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Keywords:
p-value; histograms; inference; diagnostics
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Breheny, P.; Stromberg, A.; Lambert, J. p-Value Histograms: Inference and Diagnostics. High-Throughput 2018, 7, 23. https://doi.org/10.3390/ht7030023
AMA Style
Breheny P, Stromberg A, Lambert J. p-Value Histograms: Inference and Diagnostics. High-Throughput. 2018; 7(3):23. https://doi.org/10.3390/ht7030023
Chicago/Turabian StyleBreheny, Patrick, Arnold Stromberg, and Joshua Lambert. 2018. "p-Value Histograms: Inference and Diagnostics" High-Throughput 7, no. 3: 23. https://doi.org/10.3390/ht7030023
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