In this chapter, the authors’ develop procedures for asking questions or testing hypotheses about simple models. They consider the logic of answering questions about data for the case of the simplest models because it is easy to focus on the logic when the models are simple and because the logic generalizes easily to more complex models. The authors’ provide a rule for resolving the inherent tension in data analysis between reducing the error as much as possible and keeping the model of the data as parsimonious as possible. Confidence intervals provide an alternative way for considering statistical inference. Confidence intervals are useful for describing post hoc the actual statistical power achieved in terms of the precision of the parameter estimates. Wide confidence intervals represent low statistical power and narrow intervals represent high statistical power. One way to reduce error is to control as many of the possible random perturbations as possible.