Explores the challenges of multiple testing in genomic data analysis, covering error rate control, adjusted p-values, permutation tests, and pitfalls in hypothesis testing.
Explores statistical hypothesis testing, including constructing confidence intervals, interpreting p-values, and making decisions based on significance levels.
Covers confidence intervals, hypothesis tests, standard errors, statistical models, likelihood, Bayesian inference, ROC curve, Pearson statistic, goodness of fit tests, and power of tests.
