Decision-making and optimization approaches in chemical product/process design often incorporate probability distributions to express uncertainty about the underlying data. We will show in this paper that probabilities are subjective, that they are caused by the decision makers' lack of confidence in the underlying knowledge, and that the shape of the distribution is affected by psychological input factors. Furthermore, distributions do not necessarily express the range of possible values but rather express the values that are perceived to be possible. Decisions based on distributions can, therefore, be wrong. Screening literature, however, reveals that the origin of risk and uncertainty is not fully understood and should be discussed. To close this gap, we explain where risk, distributions, and probabilities come from. On the basis of these insights, we provide a new optimization framework that describes a process of systematically identifying the optimum alternative by stepwise resolving uncertainty. Finally, we apply the optimization methodology on a case study.