How does multiple testing correction work?
When prioritizing hits from a high-throughput experiment, it is important to correct for random events that falsely appear significant. How is this done and what methods should be used? Imagine that you have just invested a substantial amount of time and money in a shotgun proteomics experiment designed to identify proteins involved in a particular biological process. The experiment successfully identifies most of the proteins that you already know to be involved in the process and implicates a few more.