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Explores the challenges of multiple testing in genomic data analysis, covering error rate control, adjusted p-values, permutation tests, and pitfalls in hypothesis testing.
Covers ANOVA method, focusing on partitioning total sum of squares into treatment and error components, mean square calculations, Fisher statistic, and F-distribution.
Delves into hypothesis testing, covering test statistics, critical regions, power functions, p-values, multiple testing, and non-parametric statistics.
Explores optimal testing methods in statistics, focusing on the Neyman-Pearson framework and the construction of test functions for different types of hypotheses.
