Bayes factors for genome-wide association studies: comparison with P-values¶
Wakefield, J. (2009). Bayes factors for genome-wide association studies: comparison with P-values: comparison with P-values. Genetic Epidemiology.
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Domain-specific¶
- Jeffreys-Lindley Paradox
- . Genomics and high-throughput science — the paradox's sample-size dependence resurfaces in genome-wide association studies, where a Bayes-factor analysis shows that a consistent p-value procedure needs a significance threshold that falls as sample size grows, contrary to the common practice of a fixed threshold
This sourceArgues that p-values are hard to calibrate in genome-wide association studies because their interpretation depends on sample size, so that a consistent p-value procedure needs a significance threshold that falls with sample size, contrary to common practice; the paper does not name the Jeffreys-Lindley paradox.
- . Genomics and high-throughput science — the paradox's sample-size dependence resurfaces in genome-wide association studies, where a Bayes-factor analysis shows that a consistent p-value procedure needs a significance threshold that falls as sample size grows, contrary to the common practice of a fixed threshold
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