  When teaching and discussing statistical assumptions, our focus is oftentimes
placed on how to test and address potential violations rather than the effects
of violating assumptions on the estimates produced by our statistical models.
The latter represents a potential avenue to help us better understand the
impact of researcher degrees of freedom on the statistical estimates we
produce. The Violating Assumptions Series is an endeavor I have undertaken to
demonstrate the effects of violating assumptions on the estimates produced
across various statistical models. The series will review assumptions
associated with estimating causal associations, as well as more complicated
statistical models including, but not limited to, multilevel models, path
models, structural equation models, and Bayesian models. In addition to the
primary goal, the series of posts is designed to illustrate how simulations can
be used to develop a comprehensive understanding of applied statistics.
