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When teaching and discussing statistical assumptions, our focus is oftentimesplaced on how to test and address potential violations rather than the effectsof violating assumptions on the estimates produced by our statistical models.The latter represents a potential avenue to help us better understand theimpact of researcher degrees of freedom on the statistical estimates weproduce. The Violating Assumptions Series is an endeavor I have undertaken todemonstrate the effects of violating assumptions on the estimates producedacross various statistical models. The series will review assumptionsassociated with estimating causal associations, as well as more complicatedstatistical models including, but not limited to, multilevel models, pathmodels, structural equation models, and Bayesian models. In addition to theprimary goal, the series of posts is designed to illustrate how simulations canbe used to develop a comprehensive understanding of applied statistics.

The Sources of Statistical Bias Series: Simulated Demonstrations to Illustrate the Causes and Effects of Biases in Statistical Estimates