This workshop will cover techniques for conducting large-scale inference, using Python and its libraries.  We will cover the principals of how large scale inference is different from classical inference, and why multiple comparisons should usually be accounted for in an analysis.  We will then discuss the Bonferroni method, Benjamini and Hochberg’s False Discovery Rate (FDR), Efron’s local FDR, the Scheffe approach, and the knockoff filter.  The motivation for each approach will be covered, and the process for carrying it out in a data analysis will be discussed in detail.  Several case studies will be used to illustrate these analytic approaches.