Statistics on big biomedical data - Methods and pitfalls when analyzing high-throughput screens

This document discusses statistical methods for analyzing high-throughput biomedical screens and common pitfalls. It introduces several statistical tests that can be used such as t-tests, ANOVA, Fisher's exact test, Mann-Whitney U test, Kolmogorov-Smirnov test, multiple testing corrections like Bonferroni and Benjamini-Hochberg, and resampling methods. It also discusses biases that can occur in big data analyses like studiedness bias and abundance bias, and how to determine if findings are statistically significant as well as biologically relevant.