Randomized clinical trials are the backbone of all clinical development programs. Statistical analyses and conclusions from such trials can be unwittingly distorted by unnecessary and/or untenable assumptions, potentially elevating the sponsor and/or consumer risk. This presentation will illustrate and exemplify hidden traps in analytic assumptions associated with "standard" analyses in a variety of contemporary settings. Simple and efficient ways to strengthen the analyses by weakening or eliminating key assumptions will be demonstrated using case studies and realistic simulations. Specific topics covered will include crossover, longitudinal, dose-response, and stratified time-to-event trials.