Day 1 Big data, reproducibility, multiple hypothesis testing, relation to Bayesian inference.
Day 2 False Discovery Rate (FDR) and the Benjamini-Hochberg procedure.
Day 3 Empirical Bayes and the local FDR
Day 4 Testing hypotheses on a tree: error rates and controlling strategies.
Day 5 High-probability FDP bounds and multiple confidence intervals.
Day 6 Selective inference.
Day 7 The knockoff filter, linear regression with fixed-X design. The model-X framework.
Day 8 Constructing knockoffs, group knockoffs and multi-resolution analysis.
Day 9 The problem of confounding: conditional testing and causal inference.
Day 10 Conformal inference: trustworthy machine predictions.