Multiple hypotheses may be generated by multiple treatment arms; heterogeneous treatment effects; or measuring multiple outcome variables. In a hypothesis testing framework, using p <0.05 as a criterion for declaring significance, it can be easy to get spurious results when many hypotheses are tested. This talk will discuss 4 things you can do when faced with multiple comparisons- covering the difference between controlling the family-wise error rate and the false discovery rate; the Bonferroni-Holm adjustment; the Benjamini-Hochberg adjustment; strategies for multiple outcome variables and strategies for correlated multiple comparisons.
The talk will be about 30 minutes long and following by discussion.
Date: Thursday 23 September 2021
Speaker: Eve Slavich, Statistical Consultant, UNSW Stats Central