In 1959, George Box and other scientists became the first to study the effect of model misspecification errors or "bias" in experimental design. They found that bias has a far greater effect on accuracy than variance and that efforts to mitigate the effects of bias generally help with other errors, but not the reverse. The propose of this paper is to discuss the research attempts to mitigate the effects of bias at the planning stage of experimentation and what assumption methods are relevant to a new generation of planning and analytic techniques designed to mitigate these effects. Thus, we will review the definition of bias, assumptions about it, its effect, and several criteria for determining its presence that are used to obtain optimal designs and evaluate bias sensitivity, including irregularly shaped design regions.